U.S. patent application number 14/797321 was filed with the patent office on 2015-11-05 for system and method for detecting a problem tooth.
This patent application is currently assigned to VISIONARY TECHNOLOGIES, INC.. The applicant listed for this patent is Roger Daugherty, Mireya Ortega. Invention is credited to Roger Daugherty, Mireya Ortega.
Application Number | 20150313559 14/797321 |
Document ID | / |
Family ID | 40641999 |
Filed Date | 2015-11-05 |
United States Patent
Application |
20150313559 |
Kind Code |
A1 |
Ortega; Mireya ; et
al. |
November 5, 2015 |
SYSTEM AND METHOD FOR DETECTING A PROBLEM TOOTH
Abstract
A system and method for visualizing a dental image that includes
a plurality of high resolution dental data, a plurality of tooth
objects, at least one threshold and a processing module is
described. The plurality of high resolution dental data that is
generated using computed tomography. The plurality of tooth objects
selected for each tooth from the dental data includes at least one
of an enamel object, a dentin object, a pulp object, a root object,
and a nerve object. The at least one threshold is used to detect at
least one problem tooth. The processing module detects at least one
problem tooth. Additionally, the processing module mathematically
models the growth of at least one tooth object, the decay for at
least one tooth object, or the combination thereof. Furthermore,
the processing module determines the effect the problem tooth has
on at least one other tooth object.
Inventors: |
Ortega; Mireya; (Stateline,
NV) ; Daugherty; Roger; (Los Angeles, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Ortega; Mireya
Daugherty; Roger |
Stateline
Los Angeles |
NV
CA |
US
US |
|
|
Assignee: |
VISIONARY TECHNOLOGIES,
INC.
South Lake Tahoe
CA
|
Family ID: |
40641999 |
Appl. No.: |
14/797321 |
Filed: |
July 13, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
11890533 |
Aug 6, 2007 |
9111372 |
|
|
14797321 |
|
|
|
|
60837311 |
Aug 11, 2006 |
|
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Current U.S.
Class: |
433/29 ;
433/215 |
Current CPC
Class: |
A61B 6/032 20130101;
G06T 2200/24 20130101; G06T 19/00 20130101; G06T 7/0012 20130101;
A61B 6/467 20130101; A61B 6/14 20130101; G06T 2207/10072 20130101;
A61B 6/463 20130101; A61B 6/5217 20130101; G06K 2209/05 20130101;
G06T 2207/30036 20130101; A61B 6/5223 20130101 |
International
Class: |
A61B 6/14 20060101
A61B006/14; A61B 6/00 20060101 A61B006/00; A61B 6/03 20060101
A61B006/03 |
Claims
1. A system for visualizing a dental image, comprising: a plurality
of high resolution dental data generated using computed tomography;
a plurality of tooth objects selected for each tooth from the
dental data from the group consisting of an enamel object, a dentin
object, a pulp object, a root object, and a nerve object; at least
one threshold to detect at least one problem tooth; a processing
module that detects at least one problem tooth; the processing
module mathematically models the growth of at least one tooth
object; and the processing module determines the effect the problem
tooth has on at least one other tooth object.
2. The system of claim 1 further comprising a database that
includes a database that further includes a plurality of data
fields that include a plurality of standard shapes associated with
each tooth and a plurality of bone density data for each section of
tooth.
3. The system of claim 2 wherein the database includes a plurality
of normative standards and at least one statistical standard for
anomaly detection.
4. The system of claim 2 further comprising a common boundary
between at least two tooth objects that is identified by the
system.
5. The system of claim 2 further comprising a particular tooth that
is identified by the system for further analysis.
6. The system of claim 5 wherein the processing module analyzes the
tooth objects for the particular tooth.
7. The system of claim 6 wherein the processing module analyzes the
particular tooth object by slicing the tooth object at one or more
locations.
8. A system for visualizing a dental image, comprising: a plurality
of high resolution dental data generated using computed tomography;
a plurality of tooth objects selected for each tooth from the
dental data from the group consisting of an enamel object, a dentin
object, a pulp object, a root object, and a nerve object; at least
one threshold to detect at least one problem tooth; a processing
module that detects at least one problem tooth; the processing
module mathematically models the decay for at least one tooth
object; and the processing module determines the effect the problem
tooth has on at least one other tooth object.
9. The system of claim 8 further comprising a database that
includes a database that further includes a plurality of data
fields that include a plurality of standard shapes associated with
each tooth and a plurality of bone density data for each section of
tooth.
10. The system of claim 9 wherein the database includes a plurality
of normative standards and at least one statistical standard for
anomaly detection.
11. The system of claim 9 further comprising a common boundary
between at least two tooth objects that is identified by the
system.
12. The system of claim 9 further comprising a particular tooth
that is identified by the system for further analysis.
13. The system of claim 5 wherein the processing module analyzes
the tooth objects for the particular tooth and analyzes the
particular tooth object by slicing the tooth object at one or more
locations.
14. A method for visualizing a dental image, comprising: receiving
a plurality of high resolution dental data that is generated using
computed tomography; identifying a plurality of tooth objects for
each tooth from the dental data, wherein the plurality of tooth
objects is selected from the group consisting of an enamel object,
a dentin object, a pulp object, a root object, and a nerve object;
providing at least one threshold to detect at least one problem
tooth; detecting the at least one problem tooth; mathematically
modeling at least one of the growth or decay of the at least one
tooth object; and determining the effect the problem tooth has on
at least one other tooth object.
15. The method of claim 14 further comprising providing a database
that includes a database that further includes a plurality of data
fields that include a plurality of standard shapes associated with
each tooth and a plurality of bone density data for each section of
tooth.
16. The method of claim 15 wherein the database includes a
plurality of normative standards and at least one statistical
standard for anomaly detection.
17. The method of claim 15 further comprising identifying a common
boundary between at least two tooth objects.
18. The method of claim 15 further comprising identifying a
particular tooth for further analysis.
19. The method of claim 18 further comprising analyzing the tooth
objects for the particular tooth.
20. The method of claim 19 further comprising analyzing the
particular tooth object by slicing the tooth object at one or more
locations.
Description
CROSS-REFERENCE
[0001] This patent application is a continuation of patent
application Ser. No. 11/890,533 filed on Aug. 6, 2007, which claims
the benefit of provisional patent application 60/837,311, filed
Aug. 11, 2006, all of which are incorporated herein by reference in
their entirety.
FIELD
[0002] The present invention relates to a system and method for
detecting a problem tooth. More specifically, the present invention
is related to mathematically modeling the growth of at least one
tooth object, the decay for at least one tooth object, or the
combination thereof.
BACKGROUND
[0003] Generally, dental images are displayed in two-dimensions
using light tables, e.g. X-rays. These two dimensional views
provide a single perspective of the image. Three-dimensional (3-D)
imaging systems have also been developed. These systems provide
high-definition digital imaging with relatively short scan times,
e.g. 20 seconds. The image reconstruction takes less than two
minutes. The X-ray source is typically a high frequency source with
a cone x-ray beam, and employs an image detector with an amorphous
silicon flat panel. The images are 12-bit gray scale and may have a
voxel size of 0.4 mm to 0.1 mm. Image acquisition is performed in a
single session and is based on a 360 degree rotation of the X-ray
source. The output data are digital images that are stored using
conventional imaging formats such as the Digital Imaging and
Communications in Medicine (DICOM) standard.
[0004] The 3-D volumetric imaging system provides complete views of
oral and maxillofacial structures. The volumetric images provide
complete 3-D views of anatomy for a more thorough analysis of bone
structure and tooth orientation. These 3-D images are frequently
used for implant and oral surgery, orthodontics, and TMJ analysis.
There are a variety of different software solutions that can be
integrated into the 3-D dental imaging systems. These third party
solutions are generally related to implant planning, and assist in
planning and placement of the implants. Additionally, the 3-D
dental images can be used for developing models to assist in
planning an operation.
[0005] In spite of the advances in the 3-D imaging systems and the
3-D imaging software, the software techniques for visualization of
the dental images do not provide a dentist with sufficient
flexibility to manipulate the 3-D image. Additionally, the
visualization features provided by current third party solutions
lack the ability to detect objects, detect irregularities, and
detect anomalies.
SUMMARY
[0006] A system and method for visualizing a dental image that
includes a plurality of high resolution dental data, a plurality of
tooth objects, at least one threshold and a processing module is
described. The plurality of high resolution dental data is
generated using computed tomography. The plurality of tooth objects
selected for each tooth from the dental data includes at least one
of an enamel object, a dentin object, a pulp object, a root object,
and a nerve object. The at least one threshold is used to detect at
least one problem tooth. The processing module detects at least one
problem tooth. Additionally, the processing module mathematically
models the growth of at least one tooth object, the decay for at
least one tooth object, or the combination thereof. Furthermore,
the processing module determines the effect the problem tooth has
on at least one other tooth object.
[0007] In one illustrative embodiment, the system and method
includes a database that further includes a plurality of data
fields that include a plurality of standard shapes associated with
each tooth and a plurality of bone density data for each section of
tooth. Additionally, the database includes a plurality of normative
standards and at least one statistical standard for anomaly
detection.
[0008] In another illustrative embodiment, the system and method
includes identifying a common boundary between at least two tooth
objects.
[0009] In yet another illustrative embodiment, the system and
method includes identifying a particular tooth for further
analysis. Also, the system and method includes analyzing the tooth
objects for the particular tooth. Furthermore, the system and
method includes analyzing the particular tooth object by slicing
the tooth object at one or more locations.
FIGURES
[0010] Embodiments for the following description are shown in the
following drawings:
[0011] FIG. 1A is shows an illustrative system overview.
[0012] FIG. 1B is an illustrative general purpose computer.
[0013] FIG. 1C is an illustrative client-server system.
[0014] FIG. 2 is an illustrative raw image.
[0015] FIG. 3 is an illustrative object identification
flowchart.
[0016] FIG. 4A is an illustrative drawing showing jaw object
identification.
[0017] FIG. 4B is an illustrative drawing showing tooth object
identification.
[0018] FIG. 5 is an illustrative 3-D image of a tooth object.
[0019] FIG. 6 is an illustrative first slice of the tooth object in
FIG. 5.
[0020] FIG. 7 is an illustrative second slice of the tooth object
in FIG. 5.
[0021] FIG. 8 is an illustrative third slice of the tooth object in
FIG. 5.
[0022] FIG. 9 is an illustrative flowchart for anomaly detection
and for modeling growth rates.
[0023] FIGS. 10A and 10B shows a normal orientation for a wisdom
tooth.
[0024] FIGS. 11A and 11B shows the beginning phase of horizontal
impaction.
[0025] FIGS. 12A and 12B shows an illustrative example of cyst
formation.
[0026] FIGS. 13A and 13B shows an illustrative example of cyst
growth.
[0027] FIGS. 14A and 14B shows the resulting tooth decay and
continuing cyst growth.
DESCRIPTION
[0028] In the following detailed description, reference is made to
the accompanying drawings, which form a part hereof, and in which
is shown by way of illustration specific embodiments in which the
invention may be practiced. These embodiments are described in
sufficient detail to enable those skilled in the art to practice
the invention, and it is to be understood that other embodiments
may be utilized and that structural, logical and electrical changes
may be made without departing from the spirit and scope of the
claims. The following detailed description is, therefore, not to be
taken in a limited sense.
[0029] Note, the leading digit(s) of the reference numbers in the
Figures correspond to the figure number, with the exception that
identical components which appear in multiple figures are
identified by the same reference numbers.
[0030] The systems and methods described herein are generally
related to visualization tools that operate with 3-D images
generated using tomography. Tomography is imaging by sections or
sectioning. The mathematical procedures for imaging are referred to
as tomographic reconstruction. Imaging is the process of creating a
virtual image of a physical object, its detailed structure, its
substructure or any combination thereof. Those skilled in the art
shall appreciate that tomographic imaging includes analyzing the
attenuation of the captured image using the Radon transform and
filtered back projection. There are a variety of different types of
tomography including but not limited to Atom Probe Tomography,
Computed Tomography, Electrical Impedance Tomography, Magnetic
Resonance Tomography, Optical Coherence Tomography, Positron
Emission Tomography, Quantum Tomography, Single Photon Emission
Computed Tomography, and X-Ray Tomography. Attenuation refers to
any reduction in signal strength.
[0031] The systems and methods described herein allow improved
visualization, object identification, anomaly detection, and
predictive growth rate features. Visualization refers to the
process of taking one or more images and incorporates a
comprehension of the physical relationship or significance of the
features contained in the images. An object is a physical
relationship within an image that is capable of being grasped
through visualization and an object is comprised of a plurality of
voxels that presume a common basis. A variety of techniques,
methods, algorithms, mathematical formulae, or any combination
thereof may be used to identify a common basis. In the illustrative
examples, elements such as location, bone density, shape or a
combination thereof may be used to identify at least one common
basis that is used for object identification. Bone density is the
measure of mass of bone in relation to volume. Therefore, one or
more common basis may be used for object identification.
[0032] It shall be appreciated by those of ordinary skill in the
art that the systems and methods described herein can be applied to
a plurality of different modalities. A modality in a medical image
is any of the various types of equipment or probes used to acquire
images of the body. Magnetic Resonance Imaging is an example of a
modality in this context.
[0033] Referring to FIG. 1A there is shown an illustrative system.
The illustrative system 200 receives a 3-D dental image 202 that is
stored in a first database 204 that stores archived images. A
digital acquisition and processing component 208 processes received
3-D dental images. Particular information that is used to process
the 3-D dental images is stored in the second database 206. An
interactive graphical user interface 210 permits a user to
manipulate the processed images and to interact with each
illustrative dental object. By way of example and not of
limitations, the 3-D dental image is generated by a medical imaging
device such as an i-CAT 3-D Imaging System from Imaging Sciences
International.
[0034] The databases 204 and 206 comprise a plurality of data
fields including, but not limited to, data fields that correspond
to the location for a plurality of teeth, a plurality of locations
for each section of tooth, a plurality of standard shapes
associated with each tooth, a plurality of standard shapes
associated with each of the sections of tooth, and a plurality of
bone density data for each section of tooth.
[0035] The digital processing component 208 is configured to
process the 3-D image, and is in operative communication with the
database. The digital processing component is configured to provide
improved visualization of the medical image. The digital processing
component 208 is configured to identify an object by combining a
plurality of voxels having a common density and tagging the object
using the methods described herein. A voxel is a volume element
that represents a value in 3-D space. Common density is a density
associated with a particular object in an image, in which a degree
of attenuation within the image is associated with density.
[0036] Additionally, the digital processing component 208 is also
configured to permit modifying the shape of at least one object.
Furthermore, the digital processing component 208 is configured to
provide a method for detecting anomalies and mathematically
modeling growth rates.
[0037] In one embodiment the digital processing component 208 is a
computer having a processor as shown in FIG. 1B. The illustrative
general purpose computer 10 is suitable for implementing the
systems and methods described herein. The general purpose computer
10 includes at least one central processing unit (CPU) 12, a
display such as monitor 14, and an input device 15 such as cursor
control device 16 or keyboard 17. The cursor control device 16 can
be implemented as a mouse, a joy stick, a series of buttons, or any
other input device which allows user to control the position of a
cursor or pointer on the display monitor 14. Another illustrative
input device is the keyboard 17. The general purpose computer may
also include random access memory {RAM) 18, hard drive storage 20,
read-only memory (ROM) 22, a modem 26 and a graphic co-processor
28. All of the elements of the general purpose computer 10 may be
tied together by a common bus 30 for transporting data between the
various elements.
[0038] The bus 30 typically includes data, address, and control
signals. Although the general purpose computer 10 illustrated in
FIG. 1B includes a single data bus 30 which ties together all of
the elements of the general purpose computer 10, there is no
requirement that there be a single communication bus which connects
the various elements of the general purpose computer 10. For
example, the CPU 12, RAM 18, ROM 22, and graphics co-processor
might be tied together with a data bus while the hard disk 20,
modem 26, keyboard 24, display monitor 14, and cursor control
device are connected together with a second data bus (not shown).
In this case, the first data bus 30 and the second data bus could
be linked by a bi-directional bus interface (not shown).
Alternatively, some of the elements, such as the CPU 12 and the
graphics coprocessor 28 could be connected to both the first data
bus 30 and the second data bus and communication between the first
and second data bus would occur through the CPU 12 and the graphics
co-processor 28. The methods of the present invention are thus
executable on any general purpose computing architecture, but there
is no limitation that this architecture is the only one which can
execute the methods of the present invention.
[0039] Various visualization and analysis application may be run on
the illustrative general purpose computer 10. For example, BioImage
and BioPSE Power App is a visualization and analysis application
developed by the University of Utah that may run on the computer
10. The software programs explore scalar data sets such as medical
imaging volumes. In operation, the user chooses an input data set.
BioImage supports a variety of different industry standard formats
including DICOM and Analyze. For example, a dental data set
containing a single tooth may be loaded into these programs.
[0040] After the data is loaded, the illustrative software program
permits the user to resample, crop, histogram or median filter the
data. Using a cropping filter permits visually removing the excess
data from the borders of the volume. The GUI permits the user to
explore the data volume in both 2-D and 3-D using the rendering
panes in the software.
[0041] The software also permits slice views wherein the user can
change slices and can adjust the contrast and brightness of the
data. Yet another feature of BioImage is the volume rendering
engine. From the volume rendering tab, the user turns on the direct
volume rendering visualization. The volume rendering algorithm uses
a transfer function to assign color and opacity based on both data
values and gradient magnitudes of the volume. Thus, the interface
between the dentin and pulp of the tooth may be colored
differently. The dentin is a calcified tissue of the body, and
along with enamel, cementum and pulp are the four major components
of teeth. Pulp is the part in the center of a tooth make up of
living soft tissue and cells called odontoblasts.
[0042] Alternatively, the methods described herein may use a
client/server architecture which is shown in FIG. 1C. It shall be
appreciated by those of ordinary skill in the art that the
client/server architecture 50 can be configured to perform similar
functions as those performed by the general purpose computer 10. In
the client-server architecture communication generally takes the
form of a request message 52 from a client 54 to the server 56
asking for the server 56 to perform a server process 58. The server
56 performs the server process 58 and sends back a reply 60 to a
client process 62 resident within client 54. Additional benefits
from use of a client/server architecture include the ability to
store and share gathered information and to collectively analyze
gathered information. In another alternative embodiment, a
peer-to-peer network (not shown) can used to implement the methods
described herein.
[0043] In operation, the general purpose computer I 0,
client/server network system 50, or peer-to-peer network system
execute a sequence of machine-readable instructions. These machine
readable instructions may reside in various types of signal bearing
media. In this respect, one aspect of the present invention
concerns a programmed product, comprising signal-bearing media
tangibly embodying a program of machine-readable instructions
executable by a digital data processor such as the CPU 12 for the
general purpose computer 10.
[0044] It shall be appreciated by those of ordinary skill that the
computer readable medium may comprise, for example, RAM 18
contained within the general purpose computer 10 or within a server
56. Alternatively, the computer readable medium may be contained in
another signal-bearing media, such as a magnetic data storage
diskette that is directly accessible by the general purpose
computer 10 or the server 56. Whether contained in the general
purpose computer or in the server, the machine readable
instructions within the computer readable medium may be stored in a
variety of machine readable data storage media, such as a
conventional "hard drive" or a RAID array, magnetic tape,
electronic read-only memory (ROM), an optical storage device such
as CD-ROM, DVD, or other suitable signal bearing media including
transmission media such as digital and analog and communication
links. In an illustrative embodiment, the machine-readable
instructions may comprise software object code from a programming
language such as C++, Java, or Python.
[0045] Referring to FIG. 2 there is shown an illustrative raw image
of a mouth and a tooth. In general, FIG. 2 provides a visual aid of
the basic anatomy of the mouth and the tooth similar to what may be
generated using the illustrative i-CAT imaging system described
above. This visual aid has many limitations, namely, the multiple
objects in the image have not been identified. Additionally, the
image is essentially a raw image that has not been standardized
using some type of calibrated sample.
[0046] Referring to FIG. 3 there is shown an illustrative flowchart
of a method for visualizing objects in a 3-D image 70. The
illustrative method 70 for visualizing a 3-D medical image
comprises receiving a plurality of high resolution 3-D medical data
at block 72 that are generated using a tomography technique, e.g.
computed tomography (CT) scans. By way of example and not of
limitation, the method for visualizing a dental image comprises
receiving a plurality of high resolution 3-D dental data associated
with a patient's mouth that is generated using x-ray
tomography.
[0047] The high resolution 3-D data received at block 72 includes a
standard for calibration purposes. For example, with respect to CT
scans, the standard may have a particular density that can be
associated with a bone density. The standard is composed of a
material that can be associated with the bone density of an
illustrative tooth. The standard may placed adjacent to the patient
and held physically or mechanically in place. Alternatively, the
patient may place the standard in the mouth and bite the
standard.
[0048] The method then proceeds to block 74 where the high
resolution data is converted into an image comprised of a plurality
of cubic voxels. At block 76, the method proceeds to identify the
location for each cubic voxel. The method then proceeds to identify
a degree of attenuation for the voxels at block 78. Attenuation is
the reduction in amplitude and intensity of a signal.
[0049] At block 80, the method associates a common density with the
degree of attenuation. The method also associates the degree of
attenuation for the standard with the previously determined
standard density related to block 72. For example, the degree of
attenuation for each voxel is associated with at least one of a
plurality of common bone densities.
[0050] The method then proceeds to block 82 that identifies an
object by combining the voxels having the common density and
determines an initial shape for the object. By way of example and
not of limitation, the identifying of the object may comprise
comparing the initial shape of the object to a standard shape. The
common density may also be modified by a user, thereby resulting in
the object having a different shape. For example, the 3-D data may
be dental data and the common density is a bone density associated
with teeth, mouth, or jaw. By way of example and not of limitation,
the method then proceeds to generate dental objects by combining
voxels having one of the common bone densities. The boundaries for
each dental object are also determined. Various opportunities may
be presented where each dental object is compared to a standard
shape to confirm identification of each dental object.
[0051] The method then proceeds to block 84 where at least one
object is then tagged for further analysis 78. For example, the
tagged objects in the tooth may be associated with a "metatag" so
that each object can be quickly identified and viewed. A "metatag"
as used herein refers to a "tag" that is associated with each
object, wherein the "tag" is searchable and is used to provide a
structured means for identifying object so that the "tagged" object
or objects can be viewed. The tagged object may then be extracted
for further analysis. The extracted object may then be viewed using
a plurality of different perspectives. The tagged and extracted
object can then be viewed by slicing the object at desired
locations. An illustrative object may be a particular tooth object,
an enamel object, a dentin object, a pulp object, a root object, a
nerve object, or any other such dental object associated with mouth
and jaw. The plurality of objects may also be identified such as
teeth objects, a plurality of nerve objects, and a plurality of
bone objects. More generally, a plurality of objects may also be
identified by combining the voxels having one of a plurality of
different common densities and by determining the boundaries for
each object.
[0052] At block 86, each of the objects is then compared to a
standard shape, and each of the objects is then tagged to permit
one or more objects to be combined. The plurality of objects may
also be identified such as teeth objects, a plurality of nerve
objects, a plurality of bone objects, or any other such object.
[0053] The flowchart also describes modifying the shape of at least
one object in a scanned 3-D medical image at block 88. After the
method proceeds to tag a first object and a second object, a common
boundary between the first object and the second object is
identified. The common boundary is configured to identify a change
in bone density between the first object and the second object. The
method permits a user to modify the common boundary by permitting
the user to modify the apparent bone density of the first object.
The method also provides for coloring each voxel according to each
of the bone densities.
[0054] The common boundary spans a relatively broad area when there
is little change in bone density between the first object and the
second object. The method also permits evaluating a plurality of
standard shapes when generating the first object and the second
object. For example, each of the plurality of objects may have a
plurality of tags, in which each tag may be extracted from the
image as represented by block 90. By way of example and not of
limitation, the first object is tagged as a first tooth and the
second object is a second tooth. In another illustrative example,
the first object and said second object is selected from a group
consisting of a tooth object, an enamel object, a dentin object, a
pulp object, a root object, a nerve object, a plurality of teeth
objects, a plurality of nerve objects, or a plurality of bone
objects. Additionally, the method 70 also supports performing
imaging operation such as slicing objects as represented by block
92 and described in further detail below.
[0055] In operation, the method involves known physiologies
discovered by the method above and supports analyzing relevant
materials. After a 3-D DICOM file is converted to 3-D volumetric
image, if the process has not already been completed, the volume is
oriented according to axes, body portion contained, scale, etc.
Object identification may be performed as a function of common
densities, density transitions, and known or standard shape
similarities. A map of the objects can then be created and
displayed.
[0056] The systems and method described may be applied to
non-specific objects, issue identification by density, adjacent
material, and general location. Margin (junction) shape
determination, specific material shape (object) determination based
on material profile, and cataloging of same may also be performed.
For example, the identification of objects, passageways, etc. (e.g.
teeth, nerve canals, implants, vertebrae, jaw, etc.) is performed.
The objective is to identify recurring examples of similar objects
such as teeth, and to catalog their identification, both by
normative standards and by reference to statistically compiled
identifiers and shape.
[0057] Referring to FIG. 4A there is shown an illustrative drawing
100 with dental and jaw object identification. As presented, the
tagged objects in the tooth are associated with a "metatag" or
"searchable tag" so that each object can be quickly identified and
viewed. A variety of soft tissues objects such as nerve objects are
shown. The nerve objects refer to sensitive tissue in the pulp of a
tooth, or any bundle of nerve fibers running to various organs in
the body. Additionally, a standard 102 for calibration purposes is
shown. By way of example and not of limitation, these nerve objects
are typically identified using MRI or CT scans.
[0058] Referring to FIG. 4B there is shown an exploded view of a
third molar tooth object 110, which is identified using the systems
and methods described herein. The tooth is a set of hard, bone-like
structures rooted in sockets in the jaws of vertebrates, typically
composed of a core of soft pulp surrounded by a layer of hard
dentin that is coated with cementum or enamel at the crown and used
for biting or chewing foods or as a means of attack or defense. The
tooth object is composed of a variety of different objects. One
such object is an enamel object which is the hard, calcareous
substance covering the exposed portion of a tooth. Another object
is dentin, which is the main, calcareous part of a tooth, beneath
the enamel, and surrounding the pulp chamber and root canals. The
pulp object is the soft tissue forming the inner structure of a
tooth and containing nerves and blood vessels. The root object is
the embedded part of an organ or structure such as a tooth, or
nerve, and includes the part of the tooth that is embedded in the
jaw and serves as support. The root canal objects refers to the
portion of the pulp cavity inside the root of the tooth, namely,
the chamber within the root of the tooth that contains the pulp.
The Gingiva or Gum object is the firm connective tissue covered by
mucous membrane that envelops the alveolar arches of the jaw and
surrounds the neck of the teeth. The neck object is the
constriction between the root and the crown and can also be
referred to as the Cemental-Enamel-Junction.
[0059] Referring to FIG. 5 there is shown an illustrative 3-D image
of an illustrative tooth object 120. The tooth object 120 comprises
each of the objects described above such as the enamel, dentin and
pulp. A variety of different slices of the 3-D image are presented.
For example FIG. 6 provides an illustrative first slice 122 of the
tooth object in FIG. 5. FIG. 7 provides an illustrative second
slice 124 of the tooth object in FIG. 5, and FIG. 8 is an
illustrative third slice 126 of the tooth object. Each of these
drawings depict that the tagged objects can be "sliced" to provide
a clearer view of the particular tooth. This slicing process may
also be used for anomaly detection as described below.
[0060] Referring to FIG. 9 there is shown an illustrative flowchart
for anomaly detection and for modeling growth rates that is a
continuation of the flowchart in FIG. 3. An anomaly is a deviation
or departure from a normal, or common order, or form, or rule, and
is generally used to refer to a substantial defect. An
"irregularity" is distinguishable from an anomaly since "irregular"
simply means lacking symmetry, evenness, or having a minor defect.
The flowchart describes a method for identifying anomalies in a
scanned 3-D dental image. The method accesses a database 206 (shown
in FIG. IA) having a plurality of data fields related to location
for a plurality of teeth, a plurality of locations for each section
of tooth, a plurality of standard shapes associated with the teeth,
a plurality of standard shapes associated with each of the sections
of tooth, and a plurality of bone density data for each section of
tooth.
[0061] As previously described in FIG. 3, a 3-D image having a
plurality of cubic voxels is generated, and the location for each
voxel is identified. For the illustrative example described herein,
the method then proceeds to identify a signal strength for each
cubic voxel, and associates the signal strength for each voxel with
the bone density data. Signal strength refers to the total amount
of power of RF received by the receiver. This is divided into
useful signal, referred to as EC/IO, and the noise floor.
[0062] The method at block 92 performs object identification at
block 132 where an illustrative first object is generated by
combining a first grouping of voxels having a first bone density.
At block 134, the illustrative first object is compared to objects
in the database 206 (shown in FIG. IA). The method then proceeds to
identify irregularities at block 136. At block 138, anomalies are
identified after comparing the first object to one or more fields
in the database 206. The database comprises a plurality of
normative standards and statistical standards for anomaly detection
that distinguished between anomalies and irregularities.
[0063] The anomaly detection at block 138 may also comprise
generating a plurality of other objects and tagging the objects so
that one or more objects may be combined. The method may then
proceed to identify one or more anomalies associated with the
plurality of objects. For example, the method supports identifying
one or more anomalies associated with at least one object that is
tagged as a tooth object, in which the tooth object further
comprises a plurality of tagged objects selected from a group
consisting of an enamel object, a dentin object, a pulp object, a
root object, and a nerve object.
[0064] The method then proceeds to block 140 and performs the
process of mathematically modeling growth rates. Although the
illustrative example of teeth is described herein, teeth are not
the only objects that grow and it shall be appreciated that the
systems and methods described herein may be used to model bone
growth and bone decay in general. Growth rate projections may be
based on such parameters as age, gender, height, weight, ethnicity,
and other such parameters that may be valuable to mathematically
modeling growth rates. Those skilled in the art shall appreciate
that measurements such as bone growth are also primary indicators
and are provided for illustrative purposes only.
[0065] At block 142, the relational effects resulting from having
modeled the growth of a particular object are determined. Thus, the
modeled growth results in changes to the local conditions, and
these changes are presented to the user.
[0066] The method then proceeds to decision diamond 144 where the
determination of whether to change any of the parameters described
above is necessary. Therefore, modeled growth rates may be changed,
thresholds for anomaly detection may be changed, and the basis for
object identification may also be modified.
[0067] In operation, at least one expected growth rate is provided
for at least one tooth. After the first tooth object is compared to
the first tooth object data in the database, the method then
proceeds to mathematically model a growth rate for the first tooth
object using the expected growth rate, and modifies the location of
a plurality of objects surrounding the first tooth object due to
the growth of the first tooth object. The method may then proceed
to identify an anomaly after comparing the first object to one or
more fields in the database. Objects may then be tagged so that so
that one or more objects may be combined. Anomalies may then be
associated with one or more tooth objects selected from a group
consisting of an enamel object, a dentin object, a pulp object, a
root object, and a nerve object.
[0068] By way of example and not of limitation, anomaly detection
may be performed by identifying at least one threshold for anomaly
detection. The gathered data is then compared to the threshold to
determine if one or more anomalies have been detected.
[0069] The potential anomaly may also be associated with a first
mathematical model, which is then compared to a second "normative"
mathematical model using recently extracted data. The first
mathematical model may have variables that can be modified, which
mirrors the ability to modify the object. The correlation between
the first mathematical model and second mathematical model is
determined by a correlation estimate that may be based on the
concordances of randomly sampled pairs.
[0070] Additionally, the method may also provide for the use of
clustering analysis. Clustering provides an additional method for
analyzing the data. Spatial cluster detection has two objectives,
namely, to identify the locations, shapes and sizes of potentially
anomalous spatial regions, and to determine whether each of these
potential clusters is more likely to a valid cluster or simply a
chance cluster. The process of spatial cluster detection can
separated into two parts: first, determining the expected result,
secondly, determining which regions deviate from the expected
result.
[0071] The process of determining which regions deviate from the
expected result can be performed using a variety of techniques. For
example, simple statistics can be used to determine a number of
spatial standard deviations, and anomalies simply fall outside the
standard deviations. Alternatively, spatial scan statistics can be
used as described by Kulldorff. (M. Kulldorff. A Spatial Scan
Statistic. Communications in Statistics: Theory and Methods 26(6),
1481-1496, 1997.) In this method, a given set of spatial regions
are searched and regions are found using hypothesis testing. A
generalized spatial scan framework can also be used. (M. R.
Sabhnani, D. B. Neill, A. W. Moore, F.-C. tsui, M. M. Wagner, and
J. U. Espino. Detecting anomalous patterns in pharmacy retail data.
KDD Workshop on Data Mining Methods for Anomaly Detection,
2005.)
[0072] It shall be appreciated by those skilled in the art that the
particular algorithm that is used for anomaly detection will depend
on the particular application and be subject to system limitations.
Thus, a variety of different algorithms for anomaly detection may
be used.
[0073] An illustrative method for anomaly detection for a tooth
object is shown in FIG. 10 through FIG. 14. The anomaly detection
also includes modeling the crown of a tooth object and the effect
the tooth object has on surrounding objects. Referring to FIG. 10A
and the exploded view in FIG. 10B, there is shown a normal
orientation for a wisdom tooth. In this orientation, the wisdom
tooth in question has sufficient space so that there will be no
horizontal impaction.
[0074] With respect to another patient, an anomaly 150 is detected
in FIG. 11A and the exploded view in FIG. 11B. The anomaly reflects
that this is a problem tooth, and this anomaly can immediately be
brought to the physician's attention using the systems and method
described herein. This image also shows the beginning phase of
horizontal impaction, and the formation of a cyst. A cyst is an
abnormal membranous sac containing a gaseous, liquid, or semisolid
substance. Additionally, at location there is shown a dental
cavity/caries that are just starting.
[0075] Referring to FIG. 12A and the exploded view in FIG. 12B
there is shown an illustrative example of the progression, i.e.
growth, of the cyst and cavity/caries after the appropriate growth
models have been associated with the particular tooth 150 and the
surrounding teeth. At FIG. 13A and the exploded view in 13B, there
is shown the effect of cyst growth, and the initial stages of tooth
decay on tooth 150. Tooth decay is an infectious, transmissible,
disease caused by bacteria. The damage done to teeth by this
disease is commonly known as cavities. Tooth decay can cause pain
and lead to infections in surrounding tissues and tooth loss if not
treated properly. The progression of the tooth decay and cyst
formation is then shown in FIG. 14A and exploded view in FIG.
14B.
[0076] The illustrative systems and methods described above have
been developed to assist in visualizing objects, permitting a user
to modify objects, anomaly detection for objects, and for modeling
growth rates associated with particular objects. It shall be
appreciated by those of ordinary skill in the various arts having
the benefit of this disclosure that the system and methods
described can be applied to many disciplines outside of the field
of dentistry. Furthermore, alternate embodiments of the invention
which implement the systems in hardware, firmware, or a combination
of hardware and software, as well as distributing the modules or
the data in a different fashion will be apparent to those skilled
in the art. Further still, the illustrative methods described may
vary as to order and implemented algorithms.
[0077] Although the description above contains many limitations in
the specification, these should not be construed as limiting the
scope of the claims but as merely providing illustrations of some
of the presently preferred embodiments of this invention. Many
other embodiments will be apparent to those of skill in the art
upon reviewing the description. Thus, the scope of the invention
should be determined by the appended claims, along with the full
scope of equivalents to which such claims are entitled.
* * * * *