U.S. patent application number 10/097727 was filed with the patent office on 2002-12-26 for method and system for the computerized analysis of bone mass and structure.
This patent application is currently assigned to ARCH DEVELOPMENT CORPORATION. Invention is credited to Chinander, Michael R., Giger, Maryellen L., Jiang, Chunsheng.
Application Number | 20020196966 10/097727 |
Document ID | / |
Family ID | 22496116 |
Filed Date | 2002-12-26 |
United States Patent
Application |
20020196966 |
Kind Code |
A1 |
Jiang, Chunsheng ; et
al. |
December 26, 2002 |
Method and system for the computerized analysis of bone mass and
structure
Abstract
An automated method, storage medium, and system for analyzing
bone. Digital image data corresponding to an image of the bone are
obtained. Next there is determined, based on the digital images, a
measure of bone mineral density (BMD) and at least one of a measure
of bone geometry, a Minkowski dimension, and a trabecular
orientation. The strength of the bone is estimated based upon the
measure of BMD and at least one of the measure of bone geometery,
the Minkowski dimension, and the trabecular orientation. To improve
bone texture analysis, the present invention also provides a novel
automated method, storage medium, and system in which digital image
data corresponding to an image of the bone is obtained, and a
region of interest (ROI) is selected within the bone. A fractal
characteristic of the image data within the ROI using an artificial
neural network is extracted. The strength of the bone is estimated
based at least in part on the extracted fractal characteristic. To
perform bone analysis with an improved measure of bone mineral
density, the present invention also provides a novel automated
method, storage medium, and system in which digital image data
corresponding to an image of the bone is obtained. A measure of
normalized bone mineral density (BMD) corresponding to a volumetric
bone mineral density of the bone is determined, and the strength of
the bone based is estimated based at least in part on the
normalized BMD.
Inventors: |
Jiang, Chunsheng;
(Naperville, IL) ; Chinander, Michael R.;
(Chicago, IL) ; Giger, Maryellen L.; (Elmhurst,
IL) |
Correspondence
Address: |
OBLON SPIVAK MCCLELLAND MAIER & NEUSTADT PC
FOURTH FLOOR
1755 JEFFERSON DAVIS HIGHWAY
ARLINGTON
VA
22202
US
|
Assignee: |
ARCH DEVELOPMENT
CORPORATION
Chicago
IL
|
Family ID: |
22496116 |
Appl. No.: |
10/097727 |
Filed: |
March 15, 2002 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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10097727 |
Mar 15, 2002 |
|
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09141535 |
Aug 28, 1998 |
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Current U.S.
Class: |
382/132 |
Current CPC
Class: |
G06T 7/0012
20130101 |
Class at
Publication: |
382/132 |
International
Class: |
G06K 009/00 |
Goverment Interests
[0001] The present invention was made in part with U.S. Government
support under grant numbers. This study was supported in parts by
USPHS Grants RO1 AR42739 and T32 CA09649. The U.S. Government has
certain rights in this invention.
Claims
What is claimed as new and desired to be secured by Letters Patent
of the United States is:
1. A method for the analysis of bone, comprising: obtaining digital
image data corresponding to an image of the bone; determining,
based on said digital image data, at least one of a measure of bone
geometry, a Minkowski dimension, and a trabecular orientation;
estimating the strength of the bone based upon the at least one of
the measure of bone geometry, the Minkowski dimension, and the
trabecular orientation.
2. The method of claim 1, wherein: said determining step comprises
determining bone mineral density (BMD); and said estimating step
comprises estimating the strength of the bone based at least in
part upon the determined BMD.
3. The method of claim 1, further comprising: inputting subject
data; and wherein said estimating step comprises estimating the
strength of the bone based at least in part upon on the input
subject data.
4. The method of claim 3, wherein said inputting step comprises:
inputting the age of the patient whose bone is being analyzed as
said subject data.
5. The method of claim 1, further comprising: determining both of
the Minkowski dimension for the bone and the trabecular orientation
of the bone; inputting subject data of the patient whose bone is
being analyzed; and the step of estimating comprising estimating
bone strength based on the measure of the Minkowski dimension, the
trabecular orientation, and the subject data.
6. The method of claim 5, wherein: said determining step comprises
determining bone mineral density (BMD): and said estimating step
comprises estimating the strength of the bone based at least in
part upon on the determined BMD.
7. The method of claim 1, further comprising: predicting the
likelihood of bone fracture from the estimation of bone
strength.
8. The method of claim 6, further comprising: predicting the
likelihood of bone fracture from the estimation of bone
strength.
9. The method of claim 2, wherein the step of determining the BMD
comprises: determining an area-based BMD as the measure of BMD.
10. The method of claim 2, wherein: the determining step comprises
determining a normalized BMD corresponding to a voltumetric bone
mineral density of the bone as the measure of BMD; and the
estimating step comprises estimating the strength of the bone based
at least in part on the normalized BMD.
11. The method of claim 10, wherein the step of determining the
normalized BMD comprises: determining an area-based BMD of the
bone; performing bone geometry analysis to generate a measure of
bone geometry; and determining the normalized BMD from the
area-based BMD and the measure of bone geometry.
12. The method of claim 11, wherein the step of performing bone
geometry analysis comprises: determining a neck width of the
bone.
13. The method of claim 11, wherein the step of performing bone
geometry analysis comprises: determining a shaft width of the
bone.
14. The method of claim 1, further comprising: selecting a region
of interest (ROI) within the bone; performing texture analysis of
the image data within the ROI to determine at least one measure of
bone structure; and the estimating step comprising estimating the
strength of the bone based at least in part on the at least one
measure of bone structure.
15. The method of claim 14, wherein the step of performing texture
analysis comprises: extracting fractal characteristics of the image
data within the ROI using an artificial neural network, said at
least one measure of bone structure including the fractal
characteristics.
16. The method of claim 14, wherein the step of performing texture
analysis comprises: determining a directional Minkowsi dimension
for the image data within the ROI, said at least one measure of
bone structure including the directional Minkowski dimension.
17. The method of claim 14, wherein the step of performing texture
analysis comprises: determining a trabecular orientation for the
image data within the ROI, said at least one measure of bone
structure including the trabecular orientation.
18. The method of claim 10, further comprising: predicting the
likelihood of bone fracture from the estimation of bone
strength.
19. A method for the analysis of bone, comprising: obtaining
digital image data corresponding to an image of the bone; selecting
a region of interest (ROI) within the bone; extracting a fractal
characteristic of the image data within the ROI using an artificial
neural network; and estimating the strength of the bone based at
least in part on the extracted fractal characteristic.
20. The method of claim 19, wherein said extracting step comprises:
performing fractal analysis of the image data within the ROI to
generate slope data; and inputting the slope data into the
artificial neural network to generate information representative of
bone strength.
21. A method for the analysis of bone, comprising: obtaining
digital image data corresponding to an image of the bone;
determining a measure of normalized bone mineral density (BMD)
corresponding to a volumetric bone mineral density of the bone; and
estimating the strength of the bone based based at least in part on
the normalized BMD.
22. The method of claim 21, wherein the step of determining the
normalized BMD comprises: determining an area-based BMD of the
bone; performing bone geometry analysis to generate a measure of
bone geometry; and determining the normalized BMD from the
area-based BMD and the measure of bone geometry.
23. The method of claim 22, further comprising: predicting the
likelihood of bone fracture from the estimation of bone
strength.
24. A computer readable medium storing computer instructions for
the analysis of bone, by performing the steps of: obtaining digital
image data corresponding to an image of the bone; determining,
based on said digital image data, at least one of a measure of bone
geometry, a Minkowski dimension, and a trabecular orientation;
estimating the strength of the bone based upon the at least one of
the measure of bone geometry, the Minkowski dimension, and the
trabecular orientation.
25. The computer readable medium of claim 24, wherein: said
determining step comprises determining bone mineral density (BMD);
and said estimating step comprises estimating the strength of the
bone based at least in part upon on the determined BMD.
26. The computer readable medium of claim 24, further storing
instructions for performing the steps of: inputting subject data;
and wherein said estimating step comprises estimating the strength
of the bone based at least in part upon on the input subject
data.
27. The computer readable medium of claim 26, wherein said
inputting step comprises: inputting the age of the patient whose
bone is being analyzed as said subject data.
28. The computer readable medium of claim 24, further storing
instructions for performing the steps of: determining both of the
Minkowski dimension for the bone and the trabecular orientation of
the bone; inputting subject data of the patient whose bone is being
analyzed; and the step of estimating comprising estimating bone
strength based on the measure of the Minkowski dimension, the
trabecular orientation, and the subject data.
29. The computer readable medium of claim 28, wherein said
determining step comprises determining BMD and said estimating step
comprises estimating the strength of the bone based at least in
part upon on the determined BMD.
30. The computer readable medium of claim 24, further storing
instructions for performing the steps of: predicting the likelihood
of bone fracture from the estimation of bone strength.
31. The computer readable medium of claim 29, further storing
instructions for performing the steps of: predicting the likelihood
of bone fracture from the estimation of bone strength.
32. The computer readable medium of claim 25, wherein the step of
determining the BMD comprises: determining an area-based BMD as the
measure of BMD.
33. The computer readable medium of claim 25, wherein: the
determining step comprises determining a normalized BMD
corresponding to a volumetric bone mineral density of the bone as
the measure of BMD; and the estimating step comprises estimating
the strength of the bone based at least in part on the normalized
BMD.
34. The computer readable medium of claim 33, wherein the step of
determining the normalized BMD comprises: determining an area-based
BMD of the bone; performing bone geometry analysis to generate a
measure of bone geometry; and determining the normalized BMD from
the area-based BMD and the measure of bone geometry.
35. The computer readable medium of claim 34, wherein the step of
performing bone geometry analysis comprises: determining a neck
width of the bone.
36. The computer readable medium of claim 34, wherein the step of
performing bone geometry analysis comprises: determining a shaft
width of the bone.
37. The computer readable medium of claim 24, further storing
instructions for performing the steps comprising: selecting a
region of interest (ROI) within the bone; performing texture
analysis of the image data within the ROI to determine at least one
measure of bone structure; and the step of estimating comprising
estimating the strength of the bone based at least in part on the
at least one measure of bone structure.
38. The computer readable medium of claim 37, wherein the step of
performing texture analysis comprises: extracting fractal
characteristics of the image data within the ROI using an
artificial neural network, said at least one measure of bone
structure including the fractal characteristics.
39. The computer readable medium of claim 37, wherein the step of
performing texture analysis comprises: determining a directional
Minkowsi dimension for the image data within the ROI, said at least
one measure of bone structure including the directional Minkowski
dimension.
40. The computer readable medium of claim 37, wherein the step of
performing texture analysis comprises: determining a trabecular
orientation for the image data within the ROI, said at least one
measure of bone structure including the trabecular orientation.
41. The computer readable medium of claim 33, further storing
instructions for performing the steps comprising: predicting the
likelihood of bone fracture from the estimation of bone
strength.
42. A computer readable medium storing computer instructions for
the analysis of bone, by performing the steps of: obtaining digital
image data corresponding to an image of the bone; selecting a
region of interest (ROI) within the bone; extracting a fractal
characteristic of the image data within the ROI using an artificial
neural network; and estimating the strength of the bone based at
least in part on the extracted fractal characteristic.
43. A computer readable medium according to claim 42, wherein said
extracting step comprises: performing fractal analysis of the image
data within the ROI to generate slope data; and inputting the slope
data into the artificial neural network to generate information
representative of bone strength.
44. A computer readable medium storing computer instructions for
the analysis of bone, by performing the steps of: obtaining digital
image data corresponding to an image of the bone; determining a
measure of normalized bone mineral density (BMD) corresponding to a
volumetric bone mineral density of the bone; and estimating the
strength of the bone based based at least in part on the normalized
BMD.
45. A computer readable medium according to claim 44, wherein the
step of determining the normalized BMD comprises: determining an
area-based BMD of the bone; performing bone geometry analysis to
generate a measure of bone geometry; and determining the normalized
BMD from the area-based BMD and the measure of bone geometry.
46. A computer readable medium according to claim 45, further
comprising: predicting the likelihood of bone fracture from the
estimation of bone strength.
47. A system for the analysis of bone, comprising: a mechanism
configured to obtain digital image data corresponding to an image
of the bone; a mechanism configured to determine, based on said
digital image data, at least one of a measure of bone geometry, a
Minkowski dimension, and a trabecular orientation; a mechanism
configured to estimate the strength of the bone based upon the at
least one of the measure of bone geometry, the Minkowski dimension,
and the trabecular orientation.
48. The system of claim 47, wherein: said determining mechanism
comprises a mechanism configured to determine bone mineral density
(BMD); and said estimating mechanism comprises a mechanism
configured to estimate the strength of the bone based at least in
part upon on the determined BMD.
49. The system of claim 47, further comprising: a mechanism
configured to input subject data; and wherein said estimating
mechanism comprises a mechanism configured to estimate the strength
of the bone based at least in part upon on the input subject
data.
50. The system of claim 49, wherein said inputting mechanism
comprises: a mechanism configured to input the age of the patient
whose bone is being analyzed as said subject data.
51. The system of claim 47, further comprising: a mechanism
configured to determine both of the Minkowski dimension for the
bone and the trabecular orientation of the bone; and a mechanism
configured to input subject data of the patient whose bone is being
analyzed; wherein the estimating mechanism comprises a mechanism
configured to estimate bone strength based on the measure of the
Minkowski dimension, the trabecular orientation, and the subject
data.
52. The system of claim 51, wherein: said determining mechanism
comprises a mechanism configured to determine bone mineral density
(BMD); and said estimating mechanism comprises a mechanism
configured to estimate the strength of the bone based at least in
part upon on the determined BMD.
53. The system of claim 47, further comprising: a mechanism
configured to predict the likelihood of bone fracture from the
estimation of bone strength.
54. The system of claim 52, further comprising: a mechanism
configured to predict the likelihood of bone fracture from the
estimation of bone strength.
55. The system of claim 48, wherein the mechanism configured to
determine BMD comprises: a mechanism configured to determine an
area-based BMD as the measure of BMD.
56. The system of claim 48, wherein the determining mechanism
comprises: a mechanism configured to determine a normalized BMD
corresponding to a volumetric bone mineral density of the bone as
the measure of BMD; and wherein the estimating mechanism comprises
a mechanism configured to estimate the strength of the bone based
at least in part on the normalized BMD.
57. The system of claim 56, wherein the mechanism configured to
determine the normalized BMD comprises: a mechanism configured to
determine an area-based BMD of the bone; a mechanism configured to
perform bone geometry analysis to generate a measure of bone
geometry; and a mechanism configured to determine the normalized
BMD from the area-based BMD and the measure of bone geometry.
58. The system of claim 57, wherein the mechanism configured to
perform bone geometry analysis comprises: a mechanism configured to
determine a neck width of the bone.
59. The system of claim 57, wherein the mechanism configured to
perform bone geometry analysis comprises: a mechanism configured to
determine a shaft width of the bone.
60. The system of claim 47, further comprising: a mechanism
configured to select a region of interest (ROI) within the bone;
and a mechanism configured to perform texture analysis of the image
data within the ROI to determine at least one measure of bone
structure; wherein the mechanism configured to estimate comprises a
mechanism configured to estimate the strength of the bone based at
least in part on the at least one measure of bone structure.
61. The system of claim 60, wherein the a mechanism configured to
perform texture analysis comprises: a mechanism configured to
extract fractal characteristics of the image data within the ROI
using an artificial neural network, said at least one measure of
bone structure including the fractal characteristics.
62. The system of claim 60, wherein the a mechanism configured to
perform texture analysis comprises: a mechanism configured to
determine a directional Minkowsi dimension for the image data
within the ROI, said at least one measure of bone structure
including the directional Minkowski dimension.
63. The system of claim 60, wherein the a mechanism configured to
perform texture analysis comprises: a mechanism configured to
determine a trabecular orientation for the image data within the
ROI, said at least one measure of bone structure including the
trabecular orientation.
64. The system of claim 56, further comprising: a mechanism
configured to predict the likelihood of bone fracture from the
estimation of bone strength.
65. A system for the analysis of bone, comprising: a mechanism
configured to obtain digital image data corresponding to an image
of the bone; a mechanism configured to select a region of interest
(ROI) within the bone; a mechanism configured to extract a fractal
characteristic of the image data within the ROI using an artificial
neural network; and a mechanism configured to estimate the strength
of the bone based at least in part on the extracted fractal
characteristic.
66. The system of claim 65, wherein said extracting mechanism
comprises: a mechanism configured to perform fractal analysis of
the image data within the ROI to generate slope data; and a
mechanism configured to input the slope data into the artificial
neural network to generate information representative of bone
strength.
67. A system for the analysis of bone, comprising: a mechanism
configured to obtain digital image data corresponding to an image
of the bone; a mechanism configured to determine a measure of
normalized bone mineral density (BMD) corresponding to a volumetric
bone mineral density of the bone; and a mechanism configured to
estimate the strength of the bone based based at least in part on
the normalized BMD.
68. The system of claim 67, wherein the mechanism configured to
determine the normalized BMD comprises: a mechanism configured to
determine an area-based BMD of the bone; a mechanism configured to
perform bone geometry analysis to generate a measure of bone
geometry; and a mechanism configured to determine the normalized
BMD from the area-based BMD and the measure of bone geometry.
69. The system of claim 68, further comprising: a mechanism
configured to predict the likelihood of bone fracture from the
estimation of bone strength.
Description
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention:
[0003] The invention relates generally to a method and system for
the computerized analysis of bone mass and structure. Specific
applications are given for the analysis of the trabecular mass and
bone pattern for the assessment of bone strength and/or
osteoporosis and as a predictor of risk of fracture. Novel
techniques involve the merging of various features including those
related to bone mass, bone geometry, bone structural information,
and subject's age. Additional techniques include the application of
Minkowski Dimension and an artificial neural network to aid in the
computerized fractal analysis of the bone structure. In addition,
an estimate of the volumetric BMD is presented incorporating bone
mass and bone geometry.
[0004] The present invention generally relates to computerized
techniques for automated analysis of digital images, for example,
as disclosed in one or more of U.S. Pat. Nos. 4,839,807; 4,841,555;
4,851,984; 4,875,165; 4,907,156; 4,918,534; 5,072,384; 5,133,020;
5,150,292; 5,224,177; 5,289,374; 5,319,549; 5,343,390; 5,359,513;
5,452,367; 5,463,548; 5,491,627; 5,537,485; 5,598,481; 5,622,171;
5,638,458; 5,657,362; 5,666,434; 5,673,332; 5,668,888; and
5,740,268; as well as U.S. patent applications Ser. Nos.
08/158,388; 08/173,935; 08/220,917; 08/398,307; 08/428,867;
08/523,210; 08/536,149; 08/536,450; 08/515,798; 08/562,087;
08/757,611; 08/758,438; 08/900,191; 08/900,361; 08/900,362;
08/900,188; and 08/900,189, 08/900,192; 08/979,623; 08/979,639;
08/982,282; 09/027,468; 09/027,685; 09/028,518; 09/053,798;
09/092,004; 09/098,504; 09/121,719; and 09/131,162 all of which are
incorporated herein by reference.
[0005] The present invention includes use of various technologies
referenced and described in the above-noted U.S. patents and
applications, as well as described in the references identified in
the appended APPENDIX and cross-referenced throughout the
specification by reference to the number, in brackets and bold
print, of the respective reference listed in the APPENDIX, the
entire contents of which, including the related patents and
applications listed above and references listed in the APPENDIX,
are incorporated herein by reference.
[0006] 2. Discussion of the Background:
[0007] Although there are many factors that affect bone quality,
two primary determinants of bone mechanical properties are bone
mineral density (BMD) and bone structure. Among the density and
structural features extracted from bone using various techniques,
researchers agree that BMD is the single most important predictor
of bone strength as well as disease-conditions such as
osteoporosis. Studies have shown correlation between BMD and bone
strength (Carter and Haye, 1977 [4]; Beck et al., 1989 [2]; Keaveny
and Hayes, 1993 [9]). To this purpose, a range of techniques have
been developed to measure BMD to evaluate fracture risk, diagnose
osteoporosis, monitor therapy of osteoporosis, and predict bone
strength (Beck et al., 1989 [2]; Ross et al., 1990 [14]; Adams,
1997 [1]; Grampp et al., 1997 [7]).
[0008] The standard technique for noninvasive evaluation of bone
mineral status is bone densitometry. Among various techniques for
bone densitometric measurement, dual energy X-ray absorptiometry
(DXA) is relatively inexpensive, low in radiation dose (<5
.mu.Sv effective dose equivalent), and of high accuracy
(.apprxeq.1%) and precision (.apprxeq.1%) (Sartoris and Resnick,
1990 [15]; Adams, 1997 [1]; Lang, 1998 [10]). DXA has gained
widespread clinical acceptance for the routine diagnosis and
monitoring of osteoporosis (Adams, 1997 [1]). In addition, DXA can
be directly used to measure whole bone geometric features (Faulkner
et al., 1994 [6]; Sieranen et al., 1994 [17]; Karlsson et al., 1996
[8]; Lang, 1998 [10]). The BMD measurement from DXA, however, is
only moderately correlated to bone mechanical properties and has
limited power in separating the patients with and without
osteoporosis-associated fractures (Cann et al., 1985 [3]). DXA
provides an integral measure of cortical and trabecular bone
mineral content along the X-ray path for a given projected area,
but DXA only measures bone mass, not bone structure. As a
consequence, DXA measurements are bone-size dependent and yield
only bone mineral density per unit area (g/cm.sup.2) instead of
true density, i.e., volumetric bone mineral density (g/cm.sup.3).
Therefore, if the BMD measurements of patients with different bone
sizes are compared the results can be misleading.
[0009] Although the effect of bone size on area BMD using DXA is
apparent (Carter et al., 1992 [5]; Seeman, 1998 [16]), only a few
studies (Nielesn et al., 1980 [13]; Martin and Buff, 1984 [11];
Carter et al., 1992 [5]) have been performed to account for such a
bias. To compensate for the effect of bone size for vertebral
bodies, Carter et al. (1992) [5] developed an analysis method and
suggested a new parameter, bone mineral apparent density (BMAD), as
a measure of volumetric bone mineral density.
[0010] Also, one of the functions of bone is to resist mechanical
failure such as fracture and permanent deformation. Therefore,
biomechanical properties are fundamental measures of bone quality.
The biomechanical properties of trabecular bone are primarily
determined by its intrinsic material properties and the macroscopic
structural properties (Cowin et al., 1987 [24]; Chakkalakl et al.,
1990 [23]; Brandenburger, 1990 [21]; Keaveny and Hayes, 1993 [9]).
Extensive efforts have been made toward the evaluation of bone
mechanical properties by studying bone mineral density (BMD) and
mineral distribution.
[0011] Since bone structural rigidity is derived primarily from its
mineral content (Elliott et al., 1989 [27]), most evaluation
methods have been developed to measure bone mass (mineral content
or density) and to relate these measures to bone mechanical
properties (Carter and Haye, 1977 [4]; Bentzen et al., 1987 [20];
Hvid et al., 1989 [32]; Keaveny and Hayes, 1993 [9]; Keaveny et
al., 1994 [36]). Results from in vivo and in vitro studies suggest
that BMD measurements are only moderately correlated to bone
strength (Carter et al., 1992 [5]). However, studies have shown
changes in bone mechanical properties and structure independent of
BMD (Goldstein, 1987 [30]; Faulkner et al., 1991 [28]). Moreover,
because density is an average measurement of bone mineral content
within bone specimens, density does not include information about
bone architecture or structure.
[0012] Various methods have been developed for in vitro study of
two- or three-dimensional architecture of trabecular bones using
histological and stereological analyses (Whitehouse, 1974 [43];
Feldkamp et al., 1989 [29]; Goulet et al., 1994 [31]; Croucher et
al., 1996 [25]). These studies have shown that, by combining
structural features with bone density, about 72 to 94 percent of
the variability in mechanically measured Young's moduli could be
explained. However, these measurements are invasive.
[0013] For the noninvasive examination of trabecular bone
structure, investigators have developed high-resolution computed
tomography (CT) and magnetic resonance imaging (MRI) (Feldkamp et
al., 1989 [29]; Durand and Ruegsegger, 1992 [26]; Majumder et al.,
1998 [38]). However, due to cost and/or other technical
difficulties, these techniques are currently not in routine
clinical use. The potential of using X-ray radiographs to
characterize trabecular bone structure has also been studied.
Although the appearance of trabecular structure on a radiograph is
very complex, studies have suggested that fractal analysis may
yield a sensitive descriptor to characterize trabecular structure
from x-ray radiographs both in in vitro studies (Majumdar et al,
1993 [37]; Benhamou et al., 1994 [19]; Acharya et al., 1995 [18];
Jiang et al., 1998a [33]) and in an in vivo study (Caligiuri et
al., 1993 [22]).
[0014] Different methods, however, exist with which to compute
fractal dimension. Minkowski dimension, a class of fractal
dimension that is identical to Hausdroff dimension (Mandelbrot,
1982 [39]), is particularly suitable for analyzing the complex
texture of digital images because it can be formally defined
through mathematical morphology and easily computed using
morphological operations (Serra, 1982 [42]; Maragos, 1994 [40]).
The Minkowski dimension computed from an image, regardless of
texture orientation, gives a global dimension that characterizes
the overall roughness of image texture. Similarly, the Minkowski
dimensions computed from different orientations yield directional
dimensions that can be used to characterize the textural anisotropy
of an image (Jiang et al., 1998a [33]).
SUMMARY OF THE INVENTION
[0015] Accordingly, an object of this invention is to provide a
method and system for the computerized analysis of bone mass and/or
structure.
[0016] Another object of this invention is to provide a method and
system for estimating bone strength.
[0017] Another object of this invention is to provide a method and
system for estimating a volumetric bone mass measure using bone
geometry.
[0018] Another object of this invention is to provide a method and
system for incorporating Minkowski Dimension into the analysis of
the bone structure pattern.
[0019] Another object of this invention is to provide a method and
system for extracting information from fractal-based texture
analyses.
[0020] Another object of this invention is to provide a method and
system for merging information on bone mass, bone geometry, bone
structure and/or subject age in order to obtain measures of bone
strength.
[0021] These and other objects are achieved according to the
invention by providing a novel automated method, storage medium
storing a program for performing the steps of the method, and
system in which digital image data corresponding to an image of the
bone are obtained. Next there is determined, based on the digital
images, a measure of bone mineral density (BMD) and at least one of
a measure of bone geometry, a Minkowski dimension, a trabecular
orientation, and subject data. The strength of the bone is
estimated based upon the measure of BMD and at least one of the
measure of bone geometery, the Minkowski dimension, the trabecular
orientation, and the subject data. Preferably, a normalized BMD
corresponding to a volumetric bone mineral density of the bone as
the measure of BMD is determined, and the strength of the bone is
estimated based at least in part on the normalized BMD.
[0022] To improve bone texture analysis, the present invention also
provides a novel automated method, storage medium storing a program
for performing the steps of the method, and system in which digital
image data corresponding to an image of the bone is obtained, and a
region of interest (ROI) is selected within the bone. A fractal
characteristic of the image data within the ROI using an artificial
neural network is extracted. The strength of the bone is estimated
based at least in part on the extracted fractal characteristic.
[0023] To perform bone analysis with an improved measure of bone
mineral density, the present invention also provides a novel
automated method, storage medium storing a program for performing
the steps of the method, and system in which digital image data
corresponding to an image of the bone is obtained. A measure of
normalized bone mineral density (BMD) corresponding to a volumetric
bone mineral density of the bone is determined, and the strength of
the bone based is estimated based at least in part on the
normalized BMD.
BRIEF DESCRIPTION OF THE DRAWINGS
[0024] A more complete appreciation of the invention and many of
the attendant advantages thereof will be readily obtained as the
same becomes better understood by reference to the following
detailed description when considered in connection with the
accompanying drawings, wherein:
[0025] FIG. 1(a) is a flowchart of the inventive method for
analyzing bone mass and structure;
[0026] FIG 1(b) is a schematic showing how the present invention
combines various types of data to analyze bone mass, bone geometry,
and/or structure;
[0027] FIG. 2(a) is a histogram showing the distribution, in an
exemplary database, of diseases leading to total hip
arthroplasty;
[0028] FIG. 2(b) is a histogram showing the distribution of cases
in the exemplary database in terms of bone strength;
[0029] FIGS. 3(a) and 3(b) are schematic diagrams that show the
setups used to radiograph the femoral neck specimens, wherein the
setup in FIG. 3(a) simulates the femoral neck as it would appear in
a clinical hip radiograph, and the setup in FIG. 3(b) was used to
produce a high-resolution radiograph of the specimens;
[0030] FIG. 4(a), FIG. 4(b), and FIG. 4(c) are respective images of
(a) a pre-operative film, (b) a specimen film using the "simulated
clinical" setup, and (c) a specimen film using the "verification"
setup, wherein the regions-of-interest shown in FIG. 4(b) and FIG.
4(c) are the regions from which the texture measures are
calculated;
[0031] FIG. 5(a) and FIG. 5(b) are respective illustrations of (a)
a side view of a specimen showing how, for strength testing, the
bone cube is initially cut into bone disks having a height of 6.5
mm with the most inferior cut aligned with the bottom of the lead
bead placed on the medial surface of the specimen, and (b) a top
view of a bone disk showing how the disk is cut into 6.5 centimeter
thick columns which were subsequently cut into 6.5 centimeter cubes
(the arrows on the left indicate the projection of the ROI that was
selected on the radiograph);
[0032] FIG. 6 is a graph showing the how load-to-failure is
determined from mechanical testing;
[0033] FIG. 7 is an image showing an ROI and several geometric
measures from the proximal femur of a subject;
[0034] FIG. 8 is a graph showing the linear relationship between
femoral neck width (BB) and femoral shaft width (CC);
[0035] FIG. 9(a), FIG. 9(b), and FIG. 9(c) are respective plots
showing (a) the dependency of BMD on bone size, (b) the dependency
of BMD on femoral neck width, and (c) the dependency of BMD on
femoral shaft width;
[0036] FIG. 10(a), FIG. 10(b), and FIG. 10(c) are respective plots
showing (a) the linear relationship between bone strength and the
area-based BMD, (b) the power law relationship between bone
strength and the BMD normalized with the femoral neck width
(nBMD.sub.N), and (c) the power law relationship between bone
strength and the BMD normalized with the femoral shaft width
(nBMD.sub.S);
[0037] FIG. 11(a) and FIG. 11(b) are respective images of (a) a
radiograph of the femoral neck specimen from the femur, and (b) a
selected ROI from the neck radiograph;
[0038] FIG. 12 is a graph showing the relationship between the
normalized volume and the scale and showing the slope used to
determine the Minkowski dimension;
[0039] FIG. 13(a) and FIG. 13(b) are respective illustrations of
(a) a squared structuring element of 3.times.3 pixels used to
compute the global Minkowski dimensions, and (b) a horizontal
structuring element of 3.times.1 pixels used to compute the
directional Minkowski dimensions;
[0040] FIG. 14 is a graph showing the directional Minkowski
dimension as a function of the angle of a structuring element for a
single ROI;
[0041] FIG. 15 is a graph showing the parameters of an ellipse used
in characterizing the plot shown in FIG. 14;
[0042] FIG. 16 is an image of a pelvis radiograph showing the
orientation from the Minkowski dimension analysis relative to the
direction of the ROI submitted for mechanical testing;
[0043] FIG. 17(a) is an image of a representative ROI where
BMD=0.2054, D.sub.M[f]=2.59, and .theta..sub.e=34.degree.;
[0044] FIG. 17(b) is an image of a representative ROI where
BMD=0.2052, D.sub.M[f]=2.73, and .theta..sub.e=149.degree.;
[0045] FIG. 17(c) and FIG. 17(d) are plots of the ellipse fitting
data for FIG. 17(a) and 17(b), respectively;
[0046] FIG. 18 is a plot showing the relationship between bone
strength and global Minkowski dimension where R.sup.2=0.17 and
p=0.016;
[0047] FIG. 19 is a graph showing the relationship between
nBMD.sup.2 and D.sub.M[f] where R.sup.2=0.04 and p=0.10;
[0048] FIG. 20(a) is a graph showing the relationship between log
area and log relative length from the surface area fractal analysis
of an ROI;
[0049] FIG. 20(b) is an illustration showing how the data from the
graph in FIG. 20(a) are used as inputs for an artificial neural
network (ANN);
[0050] FIG. 21 is a graph showing ROC curves that illustrate the
relative performances of the conventional fractal analysis method,
the ANN method, and bone mass alone, for distinguishing between
strong and weak bone;
[0051] FIG. 22 is a block diagram of a system for implementing the
inventive method; and
[0052] FIG. 23 is a schematic illustration of a general purpose
computer 300 programmed according to the teachings of the present
invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0053] Referring now to the drawings, and more particularly to FIG.
1(a) thereof, a flowchart describing an inventive method for the
analysis of bone is shown. FIG. 1(b) is a schematic showing how the
present invention incorporates various types of data to analyze
bone mass, bone geometry, and/or structure.
[0054] With the inventive method described in FIG. 1(a), the
characteristics of the bone, geometry, and trabecular pattern are
extracted using computer analysis of image data from digital images
of bony parts of the body such as the hip. The overall scheme
includes an initial acquisition of a radiographic image of the hip
in step S10. The image is digitized and stored in memory in step
S20. Alternatively, steps S10 and S20 may be combined into a single
step by directly acquiring a digital radiographic image of the hip.
A region of interest (ROI) is then placed over a femoral neck on
the image and the corresponding image data are stored in memory in
step S30. Background trend correction is performed in step S40 to
yield the underlying fluctuations, i.e., the trabecular pattern, in
the bone. In step S41 bone mineral densitometry, including BMD, is
also performed on the bone. Then, in step S42 the results of bone
mineral densitometry are stored in memory. Next, in step S50 the
image data in the ROI are then input to a texture analysis scheme,
and then, in step S60 characteristics of the bone texture are
calculated. In step S70 various texture measures are calculated
using texture schemes such as Minkowski Dimension, and additional
information is obtained from the use of artificial neural networks
(ANNs).
[0055] The image data in memory (from step S20) is also used to
extract bone geometry yielding such features as femoral neck
thickness and femoral shaft thickness. These features can also be
used to normalize BMD and to yield an estimate of volumetric BMD.
In step S80 data corresponding to the features of bone mass, bone
geometry, bone structure, and clinical data (e.g., the subject's
age) are merged/combined using one or more classifiers such as a
linear discriminant function and/or an artificial neural network
(ANN) to yield an estimate of bone strength and thus the likelihood
of risk of future fracture.
[0056] FIG. 22 is a block diagram illustrating a system 1000 for
implementing the inventive method for analysis of bone mass and
bone trabecular structure. The method and the hardware used to
implement the method and system 1000 are discussed in greater
detail below under the various section headings that follow the
description of FIG. 22.
[0057] Referring to FIG. 22, an image acquisition device 2000
inputs a radiographic image of an object into a digitization
circuit 2000a. An image memory 2001 stores the digitized image. If
the radiographic image is obtained with a direct digital device,
then there is no need for the digitization circuit 2000a. The image
memory 2001 sends stored images to an ROI selection circuit 2002
for placing ROIs on images. The ROI selection circuit sends images
with ROIs to a nonlinear detection system correction circuit 2003
for performing background trend correction. The nonlinear detection
system correction circuit 2003 sends image data, for which
background trend correction has been performed, to a bone structure
circuit 2006 for determining structural features of bone (including
the trabecular orientation) represented by the image data. The bone
structure circuit sends the extracted structural features to a
texture circuit 2020 which generates texture information including
the Minkowski dimension. An ANN fractal measure circuit 2040
determines, among other things, the fractal nature of the bone
texture information generated in the texture circuit 2020.
[0058] The image memory 2001 also sends stored image data to a bone
mass circuit 2004 for calculating BMD. Additionally, the image
memory 2001 sends stored image data to a bone geometry circuit 2005
for calculating various measures of bone geometry including femoral
neck width and femoral shaft width. A normalization circuit 2007
calculates the normalized BMD based on the BMD and the bone
geometry information generated in the bone mass circuit 2004 and
bone geometry circuit 2005, respectively. The normalized BMD
provides an estimate of the volumetric bone mineral density.
[0059] A data memory 2009 stores data regarding BMD, normalized
BMD, bone geometry, and the fractal nature of the bone texture.
This data may be weighted in a weighted sum circuit (not shown)
before being stored in the data memory 2009. Patient clinical data
is also input and stored in the data memory 2009.
[0060] A classifier circuit 2050 estimates bone strength (and thus
the likelihood for risk of future fracture) based on the measures
of bone mass, bone geometry, bone structure, and/or patient data.
An image memory (now shown) stores any image data generated by the
various components of the system. A display system (for example,
the monitor 302 in FIG. 23, discussed later) converts the digital
image data generated by the system's components into analog data
and displays the resulting images. A superimposing circuit (not
shown) superimposes the results of the system's calculations onto
the displayed images, stores the results in file format, or
provides the results in a text-only format.
[0061] Database
[0062] FIG. 2(a) is a graph showing the distribution of diseases in
a database on which the present invention was tested. The database
included femoral neck specimens. The specimens were excised from
patients undergoing total hip arthroplasties. The ages ranged from
twenty to ninety-four years with a mean age of fifty-eight years.
Each patient case also contained a standard pre-operative pelvis
radiograph. The clinical findings necessitating hip replacement for
the individuals included osteoarthritis (n=30), avascular necrosis
(n=12), and rheumatoid arthritis (n=2). Since many of the specimens
were obtained from individuals with joint disease, rather than bone
disease, the strengths of the bone ranged from very strong to very
weak. The range of ages of the individuals from which the specimens
were obtained was 20-94 years with a median age of 63 years and an
average age of 59 years. The wide range in age yielded a large
variation in bone mechanical properties.
[0063] FIG. 2(b) is a histogram showing the distribution of cases
in the exemplary database in terms of bone strength.
[0064] Bone Mineral Density and Bone Radiography
[0065] The overall method for calculation of volumetric BMD
includes conventional area-based BMD from DXA and the extraction of
geometric measures from pelvic radiographs. Area-based BMD was
performed on each femoral neck specimen. Each femoral neck specimen
was positioned in a Styrofoam cup by an orthopedic surgeon to match
the angulation and anteversion presented on the standard pelvis
radiograph of the patient. LUCITE with a thickness of five
centimeters was added below each specimen to simulate the soft
tissue in clinical BMD measurements. A Lunar DPX-IQ (Lunar Corp.,
Madison Wis.) densitometer was used to scan each specimen. After a
specimen was scanned, a region of interest (ROI) was identified,
and the area BMD (g/cm.sup.2) within the ROI in the
anteriorposterior direction was obtained using the analysis
software available on the Lunar DPX system. Each of the ROIs was
selected to match the site from where the trabecular bone cubes
would be machined from the femoral neck specimen for mechanical
testing (discussed below).
[0066] The excised femoral neck specimens were radiographically
exposed under two conditions: a "simulated clinical" setup and a
"verification" setup. A schematic diagram of the "simulated
clinical setup used to radiograph the specimens is shown in FIG.
3(a). LUCITE was used as a scattering material to simulate soft
tissue. The geometry of the setup and choice of screen-film system
and grid are those that are currently used in the Department of
Radiology at the University of Chicago Hospitals. A Lanex
medium/TMG (Eastman Kodak; Rochester, N.Y.) screen-film system was
used with an 8:1 focused grid. The distance from the focal spot of
the X-ray tube to the film cassette was 100 cm, and the distance
between the film cassette and the bottom of the first sheet of
LUCITE was 7.6 cm. Placement of the specimens (angulation and
anteversion) was performed by an orthopedic surgeon such that the
orientation of the femoral neck was similar to its position in a
standard pelvis radiograph. The specimens were held in this
orientation by securing them in a polystyrene foam cup. The
specimens were also radiographed using a high-resolution film
(X-Omat TL, Eastman Kodak; Rochester, N.Y.) with the specimen in
direct contact with the film. Direct exposure (i.e., no screen or
grid) was used to produce this high-quality radiograph, referred to
here as the "verification" setup. The "verification" setup is shown
schematically in FIG. 3(b). The "verification" setup yields a high
spatial resolution image with minimal x-ray scatter due to the
absence of LUCITE and no light diffusion due to the absence of a
screen. The pre-operative pelvis films of some patients were
available. However, because the objective of these pre-operative
films was to show the geometry of the hip joint, the films
frequently displayed poor image quality in terms of density and
contrast. An example of a pre-operative film is shown in FIG. 4(a).
FIG. 4(b) shows a "clinical" specimen radiograph corresponding to
the pre-operative film of FIG. 4(a). FIG. 4(c) shows a
"verification" radiograph corresponding to the pre-operative film
shown in FIG. 4(a). From FIGS. 4(a), 4(b), and 4(c), one can
visualize the location of the bone specimen relative to the rest of
the pelvic anatomy. The regions of interest shown in FIG. 4(b) and
FIG. 4(c) are the regions from which the texture measures are
calculated.
[0067] Biomechanical Testing for the Establishment of Bone Strength
(i.e.. "Truth")
[0068] The cancellous (trabecular) bone were precisely cut into 6.5
mm cubes with an Isomet-2000 saw cutting system (Beuler Corp. Lake
Bluff, Ill.). The specimens were first cut into disks in the plane
perpendicular to the axis of the femoral neck specimen. The
inferior cut of the first disk was aligned with the bottom of the
lead bead as shown in FIG. 5(a).
[0069] As depicted in FIG. 5(b), each disk was then cut into 6.5 mm
columns from anterior to posterior (Columns A, B, and C in FIG.
5(b)). Each column was then cut into cubes. Medial femoral cortical
bone was excluded from all specimens. For each femoral neck,
multiple cubic specimens were machined along the anterior-posterior
(AP) direction within a region corresponding to the ROI where the
BMD was initially measured. Specimen cubes that corresponded to the
ROI extracted on the digitized radiograph (discussed in greater
detail in conjunction with computerized analysis below) were used
to determine the strength of the specimen. The method for
compressive strength testing is based on the method described by
Linde et al. (1988) [45]. The compressive strength testing was
performed with an Instron 8500 plus (Instron Corp., Park Ridge,
Ill.) materials testing system. The cubes were placed between the
platens so that compressive testing was performed in the
superior-inferior direction. The specimens were first pre-loaded to
a load of five Newtons. For pre-conditioning, the specimens
underwent twenty cycles of compression to 0.5% strain and then
relaxation at a rate of 0.2 cycles per second. After
preconditioning, the load was returned to five Newtons, and then
destructive testing was performed by increasing the strain at a
rate of 0.1% strain per second until the specimen failed. All
specimens machined from all femoral necks were tested destructively
under compressive load using the same testing conditions, and the
mechanical properties (the Young's modulus and the strength) were
obtained for each bone cube. For each femoral neck, the overall
Young's modulus (E) and strength (S) were computed by averaging the
values obtained from all bone cubes (two to four cubes) within the
corresponding ROIs.
[0070] Using the load-strain information shown in the graph of FIG.
6, the destructive modulus was calculated as the maximal slope of
the load-strain curve divided by the cross-sectional area of the
specimen. The stress to failure of the specimen was obtained from
the peak of the stress-strain curve. The strength value used for
assessing the performance of the texture features was taken to be
the average value of the strength measures of the cubes that had at
least fifty percent of their area within the ROI from the
radiographs.
[0071] Bone Geometry and Volumetric Bone Mineral Density
[0072] Femur geometry was measured from the standard pelvic
radiograph for each patient. The radiographs were digitized with a
laser film digitizer (LD4500, Konica Corp., Tokyo Japan) to a
spatial resolution of 121.times.121 .mu.m and 10-bit quantization
levels. An interactive display program was developed using IDL
(Research Systems, Inc., Boulder Colo.) software in order to
measure femur geometry as suggested by Karlsson et al. (1996) [8].
All the measures were performed by a musculoskeletal radiologist.
The geometric measures shown in FIG. 7 were used to normalize the
area-based BMD. These geometric measures included the femoral neck
width (BB) and the femoral shaft width (CC) measured right below
the lesser trochanter.
[0073] The femoral neck and the femoral shaft from which the widths
were measured are nearly circular, and thus, the values of BB and
CC can be treated as diameters of the corresponding regions. The
normalized BMD (nBMD, g/cm.sup.3) was computed from the measured
area BMD (g/cm.sup.2) normalized by the diameter, i.e. 1 nBMD = BMD
BB , or ( 1 ) nBMD = BMD CC . ( 2 )
[0074] Since the BMD was measured from the femoral neck, it is
desirable to use femoral neck diameter to obtain nBMD.sub.N.
However, in some cases osteophytes were observed on the medial and
lateral sides of the necks. In these cases, the measurement of neck
width could be biased. Specifically, the measured neck width in the
medial-lateral (ML) direction could be greater than the actual neck
width in the AP direction. Therefore, the femur shaft width was
also investigated as a measurement with which to normalize BMD.
[0075] Analysis of variance was performed to show the mean
difference in the measured femoral neck width and shaft width.
Regression analyses were performed between either the BMD or the
normalized BMD values, and the mechanical properties of the bone.
Both linear and squared power law models were used in the
regression analyses. The coefficient of determination (R.sup.2) was
used to measure the explanatory or predictive power of bone
mechanical properties by the area BMD and volumetric BMD.
[0076] The descriptive statistics of measured femoral neck width
(BB) and shaft width (CC) are shown in Table 1. Although, analysis
of variance showed that the measured widths of BB and CC were
significantly different (p-value less than 0.02), the absolute mean
difference in the measured widths were quite small. The average
neck width was only 8% larger than the average shaft width. Table 1
also demonstrates large patient-to-patient variations in the
measured bone size, e.g., the maximum shaft width was 60% larger
than the minimum shaft width and that was nearly twice as large for
the measurement of neck width. FIG. 8 shows strong correlation
between the neck and shaft widths. The coefficient of determination
(R.sup.2) was 0.65. FIGS. 9(a) and (b) show the relationship
between the area-based BMD and bone size. Table 1 shows a
descriptive statistics of the geometrical measurements and BMD's
from the proximal femora.
1 TABLE 1 Standard Variables Means deviation Minimum Maximum BB
(mm) 43.77 6.67 31.57 62.79 CC (mm) 40.50 4.23 32.38 51.81 BMD 0.98
0.22 0.52 1.53 (g/cm.sup.2) nBMD.sub.N 0.23 0.05 0.09 0.35
(g/cm.sup.3) nBMD.sub.S 0.24 0.06 0.10 0.42 (g/cm.sup.3) *
nBMD.sub.N-BMD normalized using femoral neck width (BB);
nBMD.sub.S-BMD normalized using femoral shaft width (CC).
[0077] FIG 10(a) shows the relationship between strength and the
area-based BMD. The coefficients of determination (R.sup.2) of the
generalized linear regressions for the area-based BMD and strength
are shown in Table 2, and for the area-based BMD and Young's
modulus are shown in Table 3. The R.sup.2's for both linear and
power law relationships are presented in the tables. It is clear
that the power law models explain more variability in bone
mechanical properties. Compared with the linear models, the power
law models improved the R.sup.2's by 22% and 13% for predicting
Young's modulus and strength, respectively. Table 2 shows
coefficients of determination (R.sup.2) between strength (S) and
bone density (D) in linear and power law relationships. Table 3
shows coefficients of determination (R.sup.2) between Young'
modulus (E) and bone density (D) in linear and power law
relationships.
2 TABLE 2 Squared Power Predictor Linear Model Law Model BMD
(g/cm.sup.2) 0.238 0.268 nBMD.sub.N (g/cm.sup.3) 0.300 0.363
nBMD.sub.S (g/cm.sup.3) 0.319 0.372 Note: nBMD.sub.N-BMD normalized
using femoral neck width (BB) nBMD.sub.N-BMD normalized using
femoral shaft width (CC). (p-value .ltoreq. 0.001 for all
models)
[0078]
3 TABLE 3 Squared Power Predictor Linear Model Law Model BMD
(g/cm.sup.2) 0.251 0.306 nBMD.sub.N (g/cm.sup.3) 0.291 0.381
nBMD.sub.S (g/cm.sup.3) 0.338 0.431 Note: nBMD.sub.N-BMD normalized
using femoral neck width (BB) nBMD.sub.N-BMD normalized using
femoral shaft width (CC). (p-value .ltoreq. 0.001 for all
models)
[0079] The effects of normalized BMD on the prediction of bone
strength are graphically shown in FIG. 10(b) and FIG. 10(c). It is
apparent that the normalization reduced data variability and
revealed a more linear trend between the strength and either
nBMD.sub.N or nBMD.sub.S. The percent variation in strength
explained by the normalized BMD using both linear and power law
models, as quantified by the R.sup.2's, are also presented in Table
2. For the linear model, normalization increased the R.sup.2's by
26% and 34% for the area-based BMD normalized by the neck width
(nBMD.sub.N) and by the shaft width (nBMD.sub.S), respectively. For
the power law model, the increases in R.sup.2's were 35% and 39%
using nBMD.sub.N and nBMD.sub.S, respectively. As with bone
strength, the normalization caused a similar improvement in the
correlation between bone density and Young's modulus as shown in
Table 3.
[0080] Since the BMD measure produced by DXA is an area-based
density, it is valid to compare the BMDs of patients with similar
bone size. However, test results showed that the variation in bone
size could be very high, e.g. the largest neck width was nearly
twice as large as the the smallest one. In addition, as suggested
by FIG. 9(a) and FIG. 9(b), there is a clear trend that BMD is a
function of bone size. As a consequence, the BMD measurements of
patients with different bone sizes could be misleading. Therefore,
a normalization procedure is useful for relative comparison. Test
results showing increased R.sup.2 between the mechanical properties
and the normalized BMD further verify this argument.
[0081] Osteophytes were observed on femoral necks for some of the
cases. The osteophytes were mainly in the medial and lateral
surfaces of femoral necks. Therefore, the measured neck width could
be larger than the actual width for these cases. The large
variation in the neck width measures (see, for example, the
standard deviations in Table 1) as compared to that of the shaft
width measures may be due to this phenomenon. As a consequence, the
nBMD.sub.N (using femoral neck width) was expected to be less
accurate than the nBMD.sub.S. Since a normalization method was
sought for relative comparison rather than measuring true
volumetric density, femoral shaft width appeared to be a better
measure for the normalization. The justification for this choice is
based on the following reasons: (1) femoral neck width and shaft
width are virtually identical (8% difference in the means) so that
shaft width represents bone thickness in the neck region; (2)
femoral neck width and shaft width are linearly correlated (FIG. 8,
R.sup.2=0.65) even with the inclusion of osteophytes in the
measurement of neck width; (3) no osteophytes were observed in the
lesser trochanter region from where the shaft width is extracted;
and (4) femoral shaft width can be measured either from pelvis
radiographs or directly from DXA scans (Faulkner et al., 1994 [61];
Karlsson et al., 1996 [8]) so that a noninvasive evaluation is
possible.
[0082] The results obtained from analyzing the database suggest two
ways in which the clinical evaluation of bone quality can be
improved. First, BMD can be normalized using a squared power law
relationship. Substantial improvement was achieved by simply
normalizing the measured BMD with bone size. In the prediction of
bone strength, the R.sup.2 was 0.372 when normalized BMD with the
power law model was used. Using R.sup.2 as a basis for comparison,
the use of normalized BMD with the power law resulted in a 56%
improvement over the simple model that did not use normalization
(R.sup.2 was only 0.238). Although in the setup, the BMD measured
in the femoral neck region was normalized, the results strongly
support the analytic approach developed by Carter et al. (1992) [5]
for predicting BMD of whole vertebral bodies.
[0083] Although various power law relationships with different
exponents have been reported in the literature, our data are best
described by a squared power law relationship. Many reports (e.g.,
Carter and Haye, 1977 [4]; McBroom et al., 1985 [12]) have shown
that, using BMD as a single predictor, the squared power law
relationship best describes both modulus and strength. With the
present invention, the power law models improved the R.sup.2's from
13% to 30% in comparison to the simple linear models,.
[0084] With the present invention, R.sup.2 values between bone
mineral density and mechanical properties ranged from 0.24 to 0.31
for both linear and squared models. In comparison with the typical
R.sup.2 values reported in literature (which range from 0.4 to 0.8
as summarized by Keaveny and Hayes (1993) [9]), the R.sup.2's
obtained with the present invention were quite low. This is not
surprising because, in most of the reports, both the BMD and
mechanical testing were conducted on the cubic specimens as opposed
to the simulated femoral neck setup. The present invention
incorporates the femoral neck setup to measure the BMD. As a
result, the BMD obtained by the present invention is an integral
measurement of area density that includes both cortical and
trabecular bone in the entire thickness of the femoral neck.
Further, mechanical testing was performed only on the trabecular
bone cubes machined from the bone region that corresponded to the
ROI where the BMD was measured. Consequently, both the bone size
variation and the misalignment between the ROI and the cubes may
have contributed to lower R.sup.2's.
[0085] The purpose of the present invention is not to develop a
method for measuring true volumetric bone mineral density. Instead
the inventors of the present invention have tried to (1) emphasize
the problem of using area-based BMD, and (2) establish the
feasibility of using DXA and radiography to assess bone quality in
clinical applications. Standard clinical pelvis radiographs were
used for the measurement of the bone geometry. However, because of
the high spatial resolution obtained from DXA (Lang, 1998 [10]),
DXA can be directly used to measure both BMD and the bone geometry
so that the need for an additional imaging modality can be
avoided.
[0086] Using BMD and geometric bone data, the results obtained with
the inventive method suggests that the use of DXA-based bone
densitometry to predict bone mineral status can be improved with
the inventive method. The area-based BMD obtained using DXA was
normalized by a geometric measure obtained from standard pelvic
radiographs. Results show notable improvement in predicting bone
mechanical properties using the normalized bone mineral density
(i.e., volumetric BMD). The inventors have concluded that the
inventive method, which is essentially a simulated in vivo method,
is a simple and cost-effective modification of bone densitometry,
and holds potential for enhancing the performance of DXA for
clinical applications.
[0087] Analysis of Bone Structure Pattern
[0088] Radiographs were digitized with a Konica LD4500 laser film
digitizer (Konica Corp.; Tokyo, Japan) with 0. 12 1-mm pixel size
and 10-bit quantization. Regions-of-interest (ROIs) of dimension
64.times.64 pixels were selected in the medial portion of the
femoral neck by an orthopedic surgeon. An example of ROI placement
is shown in FIG. 4(b). The ROIs were positioned to avoid
overlapping structures (e.g. osteophytes). Correction was performed
for the possible nonlinear nature of the detector's characteristic
response (the H&D curve for radiographic films as detector) and
for the background trend within the ROI image data. Background
trend correction is necessary since the variation in optical
density within the ROI in hip images includes variations due to the
gross anatomy of the human body (background trends) and variations
due to the fine underlying texture which is related to the
trabecular pattern of the bone. The nonuniform background trend can
be determined using a 2-dimensional surface fitting technique (such
as one with a second degree polynomial function) (Katsuragawa et
al., 1988 [13]). The fitted trend is subtracted from each ROI in
order to yield the underlying fluctuations, i.e., the trabecular
pattern. Prior to any computerized texture analysis, this
background correction was performed on the ROIs.
[0089] The ROI was selected in the medial portion of the neck where
the cubic bone specimens were machined for mechanical testing (FIG.
11(a)). FIG. 11(b) shows a selected ROI from the neck radiograph in
FIG. 11(a).
[0090] Fractal analysis was performed on the ROIs using either
Minkowski dimension or surface area based methods.
[0091] For a ROI image f of 64.times.64 pixels in size, the global
Minkowski dimension, D.sub.M[f], is computed by (Maragos, 1994
[40]), 2 D [ f ] = lim - log [ V ( ) / I ] log ( 1 / ) , ( 3 )
[0092] where for a structuring element g at scale .epsilon.,
V.sub.g(.epsilon.) is the "volume" between two processed versions
of f obtained using morphological operators. The volume
V.sub.g(.epsilon.) is computed by 3 V ( ) = = i = i { ( f g ) - ( f
g ) } , ( 4 )
[0093] where (f.sym..epsilon.g) and (f.sym..epsilon.g) are the
dilated version and the eroded version, respectively, of the image
obtained using a structuring element g at scale .epsilon.. Note
that V.sub.g(.epsilon.) is the volume arising from the difference
between the dilated and eroded surfaces. Finding the slope of the
least-squared fitted line between
log[V.sub.g(.epsilon.)/.epsilon..sup.3] and log(1/.epsilon.) gives
the estimated fractal dimension as shown in FIG. 12.
[0094] To compute the directional Minkowski dimension, the ROI
image is rotated from .theta.=0.degree. to 360.degree. with a
10.degree. increment (Jiang et al. 1998b [34]). For each rotation
.theta., the volume, V.sub.g(.epsilon.).sub..theta., is calculated
by 4 V ( ) = = i = i { ( f g ) - ( f g ) } , ( 5 )
[0095] where f.sub..theta. is the original ROI image rotated by 0.
The directional Minkowski dimension as a function of .theta.,
D.sub.M[f].sub..theta., is then computed from Equation (3) using
the calculated volume from Equation (5) for each rotation.
[0096] A squared structuring element of 3.times.3 pixels (FIG. 13a)
and a horizontal structuring element of 3.times.1 pixels (FIG. 13b)
were used to compute the global (Equation (2)) and directional
(Equation (3)) Minkowski dimension, respectively (Jiang et al.,
1998a [33]). The resulting plot of .theta. vs. the directional
Minkowski dimension is shown in FIG. 14. The directional fractal
dimension as a function of .theta. was fit to an ellipse using a
least-square fitting method to describe the textural anisotropy of
the X-ray images. The ellipse parameters, the major and minor
diameters (a and b), eccentricity (e=sqrt (a.sup.2-b.sup.2)/a), and
ellipse orientation (.theta..sub.e), were used to describe the
image texture which, in turn, characterizes trabecular structure
(FIG. 15).
[0097] Since the machined bone cube and the selected ROI from the
neck radiograph were at different orientations as shown in FIG.
11(a), the actual ellipse orientation (.theta..sub.a) was computed
relative to the direction of mechanical testing. Thus,
.theta..sub.a varies from 0 to 90 degrees based on the original
ellipse orientation (.theta..sub.e) and the angle (T) of the
femoral neck axis. T was determined by a radiologist for each case
using the pelvic radiographs (FIG. 16).
[0098] Overall, the various computer-extracted, fractal-based
features obtained from each ROI image included a global description
of image roughness, D.sub.M[f], and the measures, a, b, e, and
.theta..sub.a, to characterize the anisotropy of the image
texture.
[0099] The ROI's from two different cases that have identical BMD's
are shown in FIG. 17 (the nBMD's are 0.2054 and 0.2052 for the
cases in FIGS. 17(a) and 17(b), respectively). However, the global
Minkowski dimension (D.sub.M[f]) and the orientation (q.sub.e) are
quite different for the ROI's. The D.sub.M[f] and .theta..sub.e are
2.59 and 34.degree., respectively, for the ROI in FIG. 17(a), and
the D.sub.M[f] and .theta..sub.e are 2.73 and 149.degree.,
respectively, for the ROI in FIG. 17(b). The mechanical strengths
are also different, the bone cubes corresponding to the ROI's in
FIGS. 17(a) and 17(b) having strengths of 0.93 and 7.47 MPa,
respectively. The results of ellipse fitting show that the
directional Minkowski dimensions fit to the ellipses very well. The
coefficient of determination, R.sup.2, used to measure the goodness
of fit of the ellipse fitting, yielded a mean of 0.966 with a
minimum, maximum, and standard deviation of 0.917, 0.990 and 0.016,
respectively. FIG. 17(c) and 17(d) show the fitted ellipse data for
the ROI's in FIGS. 17(a) and 17(b), respectively.
[0100] Pearson correlations (r) among the mechanical properties,
BMD, and image texture features are shown in Table 4. The following
relationships were observed. Among density and structural features,
the nBMD.sup.2 had the highest correlation with both strength and
modulus; followed by Minkowski dimension, orientation
(.theta..sub.a), and age in a decreasing order. The relationship
between the strength and D.sub.M[f] is shown in FIG. 18. Trabecular
bone gets stiffer and stronger with an increase in both BMD and
D.sub.M[f] (positive correlation coefficients), and with a decrease
in both age and trabecular orientation (negative correlation
coefficients). Although D.sub.M[f] had some correlation with BMD,
it became quite independent when the BMD was normalized and squared
(r=0.30) as suggested by FIG. 19. BMD was found to be nearly
uncorrelated with both age and trabecular orientation (r=-0.2).
Table 4 shows correlation (Pearson) coefficients among the
mechanical properties and the density and computer-extracted
structural image features.
4TABLE 4 r Strength Modulus BMD nBMD nBMD.sup.2 Age D.sub.M[f]
.theta..sub.a a b Modulus 0.92.sup.1 BMD 0.51.sup.2 0.52.sup.2
nBMD.sup.2 0.58.sup.1 0.60.sup.1 0.92.sup.1 Age 0.63.sup.1
0.67.sup.1 0.89.sup.1 0.95.sup.1 D.sub.M[f] -0.26.sup.4 -0.36.sup.3
-0.07.sup.4 -0.10.sup.4 -0.12.sup.4 .theta..sub.a 0.41.sup.3
0.38.sup.3 0.42.sup.3 0.31.sup.3 0.30.sup.3 0.11.sup.4 a (ellipse
-0.28.sup.4 -0.28.sup.4 -0.14.sup.4 -0.19.sup.4 -0.20.sup.4
0.22.sup.4 0.23.sup.4 major axis) a (ellipse -0.19.sup.4
-0.21.sup.4 -0.19.sup.4 -0.24.sup.4 -0.26.sup.4 -0.31.sup.3
0.10.sup.4 0.24.sup.4 major axis) a (ellipse 0.02.sup.4 -0.11.sup.4
-0.13.sup.4 -0.08.sup.4 -0.07.sup.4 0.19.sup.4 -0.01.sup.4
0.07.sup.4 0.43.sup.2 major axis) e (eccentricity) -0.23.sup.4
-0.31.sup.2 -0.12.sup.4 -0.22.sup.4 -0.25.sup.3 0.24.sup.3
0.09.sup.4 0.17.sup.4 0.76.sup.1 -0.24.sup.2 Note: .sup.1p-value
<0.001; .sup.2p-value <0.01; .sup.3p-value <0.1;
.sup.4p-value .gtoreq.0.1.
[0101] Merging of Bone Mass, Bone Geometry, Bone Structure, and/or
Clinical Information to Yield Estimates of Bone Strengths
[0102] Statistical analyses including general linear regression,
stepwise regression, best subset selection, and correlation, were
performed between the various descriptors of bone quality including
BMD, age, computer-extracted radiographic features, and
biomechanical properties (S and E). Stepwise regression and best
subset selection were used to select and merge the various
descriptors of bone mineral density and structural features into a
single index, which was then evaluated as a predictor of the
biomechanical properties. Although linear combinations of features
have been described above, artificial neural networks can also be
used to merge the information corresponding to each of the various
features, as illustrated in FIG. 1(a) and FIG. 1(b).
[0103] For unbiased comparisons, the coefficients of determination
were adjusted by the number of predictors and the sample size
(Neter et al., 1990 [41]) and the adjusted R.sup.2's were used for
all subsequent comparisons. Stepwise regression and best subset
were used to select the best predictors for the models (Neter et
al., 1990 [41]). From the computer-extracted structural features,
the global Minkowski dimension and trabecular orientation were
selected as the best structural features in predicting both modulus
and strength. In addition to these two structural features and
density, patient age was also selected as a good predictor.
[0104] Table 5 shows the best regression models and R.sup.2's for
predicting the Young's modulus. The squared relationship using
normalized BMD (nBMD.sup.2) showed substantial improvement over the
model using area BMD directly. By adding more predictors to the
model using nBMD.sup.2 alone (R.sup.2=0.431), one at a time using
stepwise regression, the R.sup.2's were improved by 16%, 25%, and
29% using two, three, and four predictors, respectively. By
including nBMD.sup.2, age, Minkowski dimension, and trabecular
orientation into the model, an R.sup.2 of 0.554 was achieved.
Compared with the model using just area BMD, the four-predictor
model (nBMD.sup.2, age, D.sub.M[f], .theta..sub.a) improved the
R.sup.2 by more than 120%. Table 5 shows regression equations and
the coefficients of determination (R.sup.2) between Young's modules
(E) and bone density & structural features.
5 TABLE 5 Predictors R.sup.2 R.sup.2 (adjusted) p-value BMD 0.274
0.251 <0.002 nBMD 0.358 0.338 <0.001 nBMD.sup.2 0.448 0.431
<0.001 nBMD.sup.2, D.sub.M[f] 0.481 0.447 <0.001 nBMD.sup.2,
Age 0.531 0.501 <0.001 nBMD.sup.2, D.sub.M[f], .theta.a 0.525
0.477 <0.001 nBMD.sup.2, Age, D.sub.M[f] 0.583 0.541 <0.001
nBMD.sup.2, Age, D.sub.M[f], .theta.a 0.608 0.554 <0.001
[0105] Similar results were also obtained in the regression for the
prediction of bone strength as shown in Table 6. Squared
relationship using normalized BMD also showed substantial
improvement over the model using area BMD directly. Adding more
predictors into the model using nBMD.sup.2 alone (R.sup.2=0.372)
improved the R.sup.2's by 5%, 20%, and 29% using two, three, and
four predictors, respectively. The highest R.sup.2, which was 0.48,
was achieved by incorporating nBMD.sup.2, age, Minkowski dimension
and trabecular orientation into the model. The improvement in
R.sup.2 using the four-predictor model over the single predictor
model of just area BMD was approximately 100%. Table 6 is a
regression equations and the coefficients of determination
(R.sup.2) between strength (S) and bone density & structural
features.
6 TABLE 6 Predictors R.sup.2 R.sup.2 (adjusted) p-value BMD 0.261
0.238 <0.002 nBMD 0.340 0.319 <0.001 nBMD.sup.2 0.391 0.372
<0.001 nBMD.sup.2, D.sub.M[f] 0.445 0.409 <0.001 nBMD.sup.2,
Age 0.426 0.389 <0.001 nBMD.sup.2, D.sub.M[f], .theta.a 0.501
0.451 <0.001 nBMD.sup.2, Age, D.sub.M[f] 0.496 0.446 <0.001
nBMD.sup.2, Age, D.sub.M[f], .theta.a 0.538 0.480 <0.001
[0106] In Tables 5 and 6, the best two- and three-predictor models
without using patient age are also presented. For predicting
Young's modulus, both two- and three-predictor models with age
performed better than models that did not use age. However, for
predicting strength, the models without age performed slightly
better than the models with age. For both modulus and strength,
adding more predictors into the four-predictor models made a
negligible improvement in the models' predictive power. Positive
regression coefficients for density and Minkowski dimension were
found for all models, and negative regression coefficients for age
and orientation were found for all models. Residual analyses showed
that the data used in all models were nearly normally distributed
and had a random nature.
[0107] An attempt was made to integrate a normalized BMD
(representing volumetric BMD) with computer-extracted structural
features to yield a potentially relevant method for bone quality
evaluation. The results of the attempt suggest the potential of
using these bone features for clinical application since good
correlation with bone strength was obtained.
[0108] Among all features investigated, bone density was the
strongest single predictor in the prediction of bone mechanical
properties (Table 1). Normalization of area BMD with bone size has
been shown to be very important, and the power law relationship
(i.e., nBMD.sup.2) further improved the correlation between bone
strength and density.
[0109] Among the fractal-based structural features evaluated, the
global Minkowski dimension, D.sub.M[f], yielded the highest
predictor for bone mechanical properties. The global Minkowski
dimension, in principle, characterizes the textural roughness of an
image. The textural roughness is a function of the trabecular
elements projected onto the X-ray image plane. Therefore,
trabecular bone with a higher global Minkowski dimension or rougher
image texture is healthier and stronger.
[0110] Trabecular bone possesses strong anisotropy and bone
mechanical properties are related to trabecular orientation. Thus,
trabecular bone is expected to be stiffer and stronger in the
direction where most trabecular elements are aligned, but more
susceptible to crushing in other directions. Although
three-dimensional trabecular orientation of in vitro bone (Jiang et
al. (1998b) [34]), is more closely related to bone strength, such
methods are invasive or destructive. With the present invention,
texture orientation, as calculated from a projection radiograph
(i.e. from a two-dimensional image), was used to characterize the
three-dimensional orientation of the trabecular network. The
results suggest that the texture orientation extracted from a
radiograph is related to bone strength, and the global Minkowsici
dimension and texture orientation together, better describe
trabecular structure.
[0111] Using multiple-predictor models, analysis of the database in
accordance with the present invention showed that both density and
structural features contribute to bone mechanical properties.
Although bone density is the most important feature, only a portion
of the variability in bone modulus and strength can be explained by
the normalized BMD (i.e., volumetric BMD). The structural features
extracted from bone radiographs and age explain the additional
variation in bone quality that can not be explained by bone density
alone. Age may contain additional information on mechanical
properties that cannot be explained by either the noninvasively
measured density and/or structural predictors. The independence of
the structural features from bone density as seen in FIG. 19 and
the progressively improved R.sup.2's in the multi-predictor models
validate the importance of the inventive models.
[0112] The resultant R.sup.2's in this example were lower than
those reported in literature as summarized by Keaveny and Hayes
(1993) [9]. Several factors may be responsible for this difference.
First, the whole bone thickness was used to measure bone mineral
density. Even though area BMD is normalized, the volumetric density
is a gross measure because it integrates bone minerals from the
entire thickness of the femoral neck which includes cortical bone.
Note, however, that bone mechanical properties were only obtained
from the trabecular bone cubes. Therefore, the measured BMD of the
femoral neck is not exactly the BMD of the bone cubes. Second,
although careful attention is given to matching the locations for
measuring BMD, selecting the ROI on the radiographs, and machining
bone cubes, it is impossible to match these locations exactly.
Because the amount of trabecular bone and trabecular arrangement
may vary dramatically in the neck region, slight mismatching could
change the actual BMD, D.sub.M[f] and/or trabecular orientation.
Third, to estimate trabecular orientation, it was assumed that the
femoral neck axis as measured from the pelvic radiograph coincided
with the loading direction in the mechanical testing. However, due
to anteversion and rotation shown on the radiograph and the
presence of osteophytes around the neck in some of the cases, the
femoral neck axis measured from the pelvic radiograph potentially
may not agree with the direction for mechanical testing. Such
misalignment can introduce error in the estimation of trabecular
orientation, and therefore decrease the predictive power of
trabecular orientation.
[0113] Analysis of Fractal-Based Systems Using Artificial Neural
Networks
[0114] The fractal dimension of the bone ROIs can be estimated by
the Minkowski Dimension, as discussed above, or by using a surface
area technique, as described elsewhere (Caliguiri et al., 1994)
[44]. In the surface area based technique, the gray level of each
pixel is regarded as a "height" with pixel size as "length" and
"width" to calculate a "surface area" for each ROI. Adjacent pixels
are then combined to yield an effectively larger pixel size with a
new gray level averaged from these combined pixels. A new "surface
area" is then calculated for each ROI, and the process is
successively repeated, combining adjacent pixels from earlier
steps, and calculating the resultant surface area for each new
effective pixel size (FIG. 20). The fractal dimension (D) for each
ROI is calculated using D=2-H, where H is the slope of a
least-squares line fitted to the relationship of log surface area
versus log pixel size for each ROI. The number 2 is the topological
dimension of the gray level surface.
[0115] With both of these fractal based technique, one is required
to determine a slope (FIG. 12) or multiple slopes (FIG. 206) if the
texture is multifractal in nature. This may be difficult due to the
number of limited data points used in determining the slope (see
FIGS. 12 and 20(a). However, we present here a technique for the
incorporation of an ANN to determine the fractal nature of the
texture and relate it to bone strength and risk of fracture. A
feed-forward back-propagation neural network is demonstrated for
the surface-area technique. (Similar use can be performed with the
Minkowski dimension volume technique.) The data points from the
surface area vs. effective pixel size plot of FIG. 20(a) are used
as the input nodes to an ANN as shown in FIG. 20(b) (six input
nodes are used in this example). There exists one hidden layer with
three nodes and a single output node trained on the truth data,
i.e., the bone mechanical strength data. Continuous load-to-failure
data are used as the desired output for the ANN. Using round-robin
testing, specimens were classified as strong or weak based on the
load-to-failure values. Table 7 shows the correlation of the
conventional calculation of slope method and the ANN method with
load-to-failure, which yields correlation coefficients of -0.53 and
0.77, respectively. The correlation of bone mass (BMD) with
strength is also given (0.51) for comparison. Table 8 and FIG. 21
show the performances of the conventional fractal method and the
new ANN method in terms of ROC analysis. A cutoff of 300 Newtons
was used to divide the specimens into 7 strong and 27 weak bones.
Again, the ANN method of extracting the fractal dimension from the
surface (or volume) plots outperformed the conventional method as
well as the use of BMD alone. These results indicate that
computerized texture analysis of trabecular bone pattern on
digitized radiographs can provide information on bone strength. A
statistically significant improvement over BMD was found using a
fractal-based neural network system in the task of distinguishing
between strong and weak bone.
7TABLE 7 Correlation with load to failure Method Correlation with
strength p-value Slope Method -0.53 0.0010 ANN 0.77 <0.0001 BMD
0.51 0.0018
[0116]
8TABLE 8 In distinguishing between strong and weak bone Method
A.sub.z p-value* Slope Method 0.85 .+-. 0.06 0.126 ANN 0.88 .+-.
0.07 0.007 BMD 0.72 .+-. 0.11 -- *p-value in comparison with
BMD
[0117] Computer Implementation
[0118] This invention may be conveniently implemented using a
conventional general purpose digital computer or micro-processor
programmed according to the teachings of the present specification,
as will be apparent to those skilled in the computer art.
Appropriate software coding can readily be prepared by skilled
programmers based on the teachings of the present disclosure, as
will be apparent to those skilled in the software art.
[0119] The present invention includes a computer program product
which is a storage medium including instructions which can be used
to program a computer to perform processes of the invention. The
storage medium can include, but is not limited to, any type of disk
including floppy disks, optical discs, CD-ROMs, and magneto-optical
disks, ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, or
any type of media, including hard drives, suitable for storing
electronic instructions.
[0120] FIG. 23 is schematic diagram of a general purpose computer
300 which can be used to implement the present invention. In FIG.
23, the computer 300, for example, includes a display device 302
(such as a touch screen monitor with a touch-screen interface), a
keyboard 304, a pointing device 306, a mouse pad or digitizing pad
308, a hard disk 310 (or other fixed, high density media drives,
connected using an appropriate device bus, such as a SCSI bus, an
Enhanced IDE bus, a PCI bus, etc.), a floppy drive 312, a tape or
CD ROM drive 314 with tape or CD media 316 (or other removable
media devices, such as magneto-optical media, etc.), and a mother
board 318. The motherboard 318 includes, for example, a processor
320, a RAM 322, and a ROM 324. The computer 300 also includes I/O
ports 326 and optional specialized hardware 328 for performing
specialized hardware/software functions (such as sound processing,
image processing, signal processing, neural network processing,
etc.), a microphone 330, and a speaker or speakers 340.
[0121] Stored on any one of the above described storage media
(computer readable media), the present invention includes
programming for controlling both the hardware of the computer 300
and for enabling the computer 300 to interact with a human user.
Such programming may include, but is not limited to, software for
implementation of device drivers, operating systems, and user
applications. Such computer readable media further includes
programming or software instructions to direct the general purpose
computer 300 to perform tasks in accordance with the present
invention.
[0122] The programming of general purpose computer 300 may include
a software module for digitizing and storing images obtained from
an image acquisition device. Alternatively, it should be understood
that the present invention can also be implemented to process
digital image data obtained by other means, for example from a
PACS.
[0123] The invention may also be implemented by the preparation of
application specific integrated circuits or by interconnecting an
appropriate network of conventional component circuits, as will be
readily apparent to those skilled in the art.
[0124] In clinical application, because of bone size variation, it
is impossible to measure true volumetric BMD with DXA.
Nevertheless, for the purpose of comparing individuals with
different bone sizes, it is possible to normalize the area-based
BMD with a geometric dimension that is proportional to bone
thickness in a noninvasive manner. In the present invention,
area-based BMD and volumetric BMD are used as predictors of bone
mechanical properties. Further a method for noninvasively
normalizing the BMD values for use in clinical applications is
provided.
[0125] The present invention provides a new and improved method and
system for the analysis of bone. Specific applications are given
for the analysis of regions within the femoral hip. The techniques
employed include novel features that characterize the volumetric
bone mineral density (BMD) of bone and allow extraction of bone
geometry features. The techniques also include incorporation of
Minkowski Dimension in the analysis of the bone structure pattern
and extraction of information from fractal-based texture analyses.
These features of bone mass, bone geometry, bone structure, and/or
subject age are then merged using artificial neural networks in
order to yield an estimate of bone strength. Incorporation of these
features make the system desirable for in vivo screening (for
osteoporosis, bone strength, and risk of future fracture).
[0126] The results obtained from implementing the present invention
demonstrate the important contributions of normalized BMD,
structural features, and age to bone mechanical properties, e.g.,
bone strength. In addition, the limitation of fractal-based
analyses is overcome with the use of an ANN to extract fractal
information.
[0127] Obviously, numerous modifications and variations of the
present invention are possible in light of the above technique. It
is therefore to be understood that within the scope of the appended
claims, the invention may be practiced otherwise than as
specifically described herein. Although the current application is
focused on radiographic medical images, the concept can be expanded
to analysis in other images of the human body. APPENDIX
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