U.S. patent application number 14/783347 was filed with the patent office on 2016-02-04 for device and method for blood analysis by image processing.
This patent application is currently assigned to Universidade do Minho. The applicant listed for this patent is UNIVERSIDADE DO MINHO. Invention is credited to Ana Patricia DA SILVA FERRAZ, Victor Hugo MENDES DA COSTA CARVALHO.
Application Number | 20160035090 14/783347 |
Document ID | / |
Family ID | 50842276 |
Filed Date | 2016-02-04 |
United States Patent
Application |
20160035090 |
Kind Code |
A1 |
DA SILVA FERRAZ; Ana Patricia ;
et al. |
February 4, 2016 |
DEVICE AND METHOD FOR BLOOD ANALYSIS BY IMAGE PROCESSING
Abstract
The present application describes a new device and method of use
thereof, which allows identifying certain antigens and antibodies
present in the blood. The device of the present invention is a
closed device consisting of two parts, wherein the upper part (1)
comprises a chamber (3) surrounded by LEDs (4) illuminating the
analysis plate (8), which is supported by the rotating platform
(6). In turn, the rotating platform is connected to a motor (7)
that will promote the rotation thereof for mixing reagents with
blood. After a period of time, the camera (3) will capture and send
the resulting image to a computer program that will analyze the
sample, using image processing techniques.
Inventors: |
DA SILVA FERRAZ; Ana Patricia;
(Arcozelo-Barcelos, PT) ; MENDES DA COSTA CARVALHO;
Victor Hugo; (Braga, PT) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
UNIVERSIDADE DO MINHO |
Braga |
|
PT |
|
|
Assignee: |
Universidade do Minho
Braga
PT
|
Family ID: |
50842276 |
Appl. No.: |
14/783347 |
Filed: |
April 8, 2014 |
PCT Filed: |
April 8, 2014 |
PCT NO: |
PCT/IB2014/060531 |
371 Date: |
October 8, 2015 |
Current U.S.
Class: |
435/29 ; 356/39;
382/128 |
Current CPC
Class: |
G06K 2009/4666 20130101;
G06T 7/90 20170101; G01N 21/51 20130101; G01N 2035/0475 20130101;
G06T 7/70 20170101; G06T 5/00 20130101; G06K 9/4652 20130101; G06K
9/4604 20130101; G01N 2201/062 20130101; G06T 7/13 20170101; G01N
33/86 20130101; G01N 2015/0092 20130101; G06T 7/0012 20130101; G06K
9/6267 20130101; G01N 2035/00524 20130101; G06K 9/52 20130101; G01N
15/00 20130101; G06T 7/62 20170101 |
International
Class: |
G06T 7/00 20060101
G06T007/00; G01N 33/86 20060101 G01N033/86; G06K 9/62 20060101
G06K009/62; G06T 5/00 20060101 G06T005/00; G06T 7/40 20060101
G06T007/40; G06T 7/60 20060101 G06T007/60; G06K 9/52 20060101
G06K009/52; G01N 21/51 20060101 G01N021/51; G06K 9/46 20060101
G06K009/46 |
Foreign Application Data
Date |
Code |
Application Number |
Apr 8, 2013 |
PT |
106871 |
Claims
1. Portable device for detecting immunological agglutination of
blood samples comprising: an upper part closed with a lid,
comprising a camera which is centered, surrounded by LEDs and
connected to a laptop computer or other mobile device which
analyzes the captured images by image processing techniques; a
lower part, which in turn comprises a rotating platform connected
to a motor where the analysis plate is fixed; a power supply.
2. Device according to claim 1, wherein the connection to the
laptop computer or other mobile device is carried out via USB,
Wireless or Bluetooth.
3. Device according to claim 1, wherein the other mobile device
comprises a mobile phone (smartphone) or a tablet.
4. Device according to claim 1, wherein the camera focuses directly
on the rotating platform.
5. Device according to claim 1, wherein the upper part and the
lower part of the device are connected by a hinge on one side and a
lock on the opposite side.
6. Device according to claim 1, wherein the camera focuses directly
on the analysis plate.
7. Device according to claim 1, wherein the analysis plate is
sealed and transparent, is a whole piece and comprises separate
circular containers, which are made of a sealing and impermeable
material and having holes.
8. Device according to claim 1, wherein the analysis plate is
sealed and transparent and has a removable lid, which is fitted on
the base having the containers by a thread mechanism that joins and
fixes the two parts (lid and base with containers) and seals the
liquid outlet.
9. Device according to claim 1, wherein the analysis plate used has
6 containers.
10. Device according to claim 1, wherein the analysis plate is also
a spinning one having circular and deep containers.
11. Device according to claim 1, wherein the number of LEDs ranges
between 4 and 6.
12. Device according to claim 1, wherein the power supply is a
battery.
13. Method for detecting immunological agglutination of blood
samples, using the device according to claim 1, comprising the
following steps: a) placing each of the reagents in the respective
containers of the analysis plate, and then the patient's blood to
be analyzed, both in their respective proportions; b) Then, placing
the analysis plate in the device, fixing it to the rotating
platform; c) closing the device by joining the upper part of the
device with the lower part and start the device. d) activating the
camera, LEDs and motor, according to the following steps: i. moving
rotationally the platform, via the motor for a time between 60 and
130 seconds, during which the reaction takes place; ii. stopping
the motor and turning on the LEDs; iii. after 2 minutes, capturing
the image with the camera; e) turning off the LEDs; f) sending the
camera's image to the mobile device, which in turn stores this
image; g) treating the image by image processing techniques; h)
classifying the occurrence or non-occurrence of agglutination via
the classification algorithm according to the standard deviation
value obtained for each of the test containers.
14. Method for detecting immunological agglutination of blood
samples according to claim 3, wherein the blood to reagent ratio
consists of a drop of whole blood having one-forth of the reagent
drop size.
15. Method for detecting immunological agglutination of blood
samples according to claim 13, wherein the image processing
techniques comprise the following steps: a) extracting the green
color planes of the captured image by transforming the original
32-bit image into an 8-bit image, so it can be used; b) separating
the blood and reagent mixtures into two regions, designated
particle region and background region, by assigning the value 1
(one) to all pixels belonging to a range of established values and
assigning the value 0 (zero) to all other pixels in the image that
does not belong to such established range; c) calculating the
threshold value for each pixel based on statistics of the adjacent
pixel, using a 32-width and 32-height default matrix (kernal), with
a deviation factor which by default is 0.20; d) in the image,
assigning the pixel value 1 (one) to existing holes in the
particles corresponding to the blood and reagent mixtures; then,
removing the particles with the value of 1 (one) pixel to remove
background noise from the image and ensure that at the end only
remain particles related to test containers; f) removing the
particles which are in the borders of the image, filling in the
position with the same value of the adjacent pixel, in order to
ensure that only remain for analyzing particles related to test
containers; g) calculating the metrics on the CenterofMassX and
CenterofMassY image, which together provide the coordinates of the
center of mass of each particle in the image; h) extracting the
light planes from the original image and transform the image into
an 8-bit image, which can now be used by other functions; i)
referencing the object in the image that is an identifying mark of
the order in which the test was performed, keeping a profile of
such object and searching for such object in each image analyzed by
the program, giving the coordinates and calculating the distances
to other objects; j) identifying in each image six containers and
presenting the coordinates of each one in order to calculate the
aforementioned distances; k) quantifying a given image region
defined by the programmer, using each of the container's
coordinates given in the previous function to quantify a set of
metrics as average of pixels, minimum value, maximum value,
standard deviation and analyzed area, because the standard
deviation value determines whether or not agglutination has
occurred in each test container.
16. Method for detecting immunological agglutination of blood
samples according to claim 12, wherein if standard deviation is
higher than 16, the classification algorithm classifies as
agglutinated.
17. Method for detecting immunological agglutination of blood
samples according to claim 12, wherein if standard deviation is
less than 16, the classification algorithm classifies as not
agglutinated.
18. Method for detecting immunological agglutination of blood
samples according to claim 12, wherein results are sent by SMS or
email.
19. Method for detecting immunological agglutination of blood
samples according to claim 13, wherein the blood type is detected
by determining ABO and Rh; tick fever, Syphilis; Mononucleosis;
hospital infections; Streptococci bacteria; Meningitis and
Pneumonia.
Description
TECHNICAL FIELD
[0001] The present invention relates to a device for detecting
immunological agglutination. More specifically, it is a device that
enables detection of the different blood groups, as well as certain
blood diseases via agglutination of blood cells by image processing
techniques.
BACKGROUND
[0002] Currently, blood grouping, in emergency situations, is a
lengthy test in view of the immediate need for blood. In these
situations, the current practice is to administer the blood type O
negative, given the lower risk of incompatibility as it is
considered the universal donor. However, although the risk is
lower, there are still possible reactions which can be prevented by
administering a blood type compatible with the patient's one,
starting from the first unit of blood transfusion.
[0003] The present invention is in the field of determining some
antigens and antibodies present in the patient's blood, using
methods that are suited to emergency situations in order to speed
up the determination.
[0004] Red blood cells have on their surface a variety of relevant
antigens for blood transfusion. These antigens are grouped into
systems such as ABO, D (Rh), Kell, Duffy, Kidd, Lewis, P, MNS,
Lutheran, Kidd and Xg, being the ABO and D (Rh) systems the most
relevant in the context of transfusions. Decades ago, blood
transfusion from one individual to another have shown the
importance of transfusions and existing incompatibilities. The
person transfused often got sick and occasionally died after
transfusion. Accordingly, it was found that there were different
antigens on the surface of red blood cells and antibodies in plasma
and that blood transfusion in patients with different antigens
resulted in blood agglutination due to the occurrence of
antigen-antibody reaction. The ABO system is then described, and
included types A, B, AB and O. Subjects with type A have in their
red blood cells the antigen A, while their plasma contains anti-B
antibodies. Individuals with type B have red blood cells with B
antigens and in their plasma anti-A type antibodies. Individuals
with type AB have both antigens of type A and type B and do not
have antibodies in their plasma. Finally, the individuals of type
O, or more correctly type zero (0), have not antigens on the red
blood cells and have anti-A and anti-B antibodies in their
plasma.
[0005] D (Rh) system is the second most importance in the context
of blood transfusion, and it is usually described in conjunction
with the ABO system with the "positive" or "negative" suffix, such
as for example, AB positive, O negative. Its importance stems from
its large ability for producing agglutination when administered to
an incompatible type, such as for example, administering a positive
Rh to a negative Rh individual.
[0006] In addition to these two most significant systems for blood
transfusion, many others are present on the surface of red blood
cells. There are some extremely rare antigens, while others can be
found in the general population. The Kell system is among the least
rare, followed by Duffy, Kidd, Lewis, P, MNS, Lutheran and Xg.
[0007] Tests for determining blood types are based on the
agglutination reaction. In these tests, an individual's blood is
mixed with specific reagents that, for example, identify the
antigen present in the blood, such as reagents anti-A, anti-B,
anti-AB and anti-D. Thus, red blood cells in the individual's blood
will have certain antigens that in contact with each
antibody-containing reagent will trigger antigen type-dependent
reactions. In this study, each of the reactions taking place is
observable by naked eye and through image processing.
[0008] To avoid problems during blood transfusion, a number of
different solutions have been developed.
[0009] According to U.S. Pat. No. 6,330,058 patent, ABO and D (Rh)
blood analysis is performed by a system developed using a
spectrophotometric method.
[0010] U.S. Pat. No. 8,318,439 patent application discloses a
system capable of performing blood type analyzes such as ABO and D
(Rh) determinations, phenotypes, minor cross-match, cross-matching,
antibody screening and identification of some diseases such as
malaria, typhoid fever. For this, it uses optical detection which
could include transmittance and reflectance spectroscopy,
turbidimetry--where light is measured at a 180 degrees angle from
the incident --nephelometry--where light is measured at 90 degrees
from the incident, or any other by an angle from the incident beam,
including the front and back scattering--laser scattering
spectroscopy, or visual observation. It has been found that this
system, besides being different from the present inventions, is a
more time consuming process and does not identify the antigens
present in the blood of the individual. It has also been found that
this system requires its use only in the laboratory, not suiting
emergencies.
[0011] Patent document U.S. Pat. No. 8,053,226 discloses a system
that performs phenotype and antibody identifications with lateral
flow tests. Reading of the results is performed by naked eye or
with a CCD camera. The difference between the new invention and
this document is that it describes a lateral flow test while the
new invention refers to a slide test.
[0012] Patent document EP 1397679, relates to a clinically
intelligent diagnostic device for diagnosing diseases according to
related symptoms but also for performing ABO and Rh blood analysis.
Furthermore, it performs phenotype and disease identification tests
such as HIV, and syphilis. This device uses microarrays and suits
to the technologies currently used in laboratories. It has been
found that the methodology and related technology are different
from the new invention.
[0013] Patent document EP 1797426 shows a system that uses a quartz
crystal microbalance detection-based technology (quartz crystal
microbalance QCM). This technology is used for rapid monitoring of
direct and indirect blood types, performing the hepatitis test as
well. The system is totally different from what is intended to
patent.
[0014] In turn, Moreira, V. et al, documents refer to a portable
blood analysis device which incorporates therein a camera that
captures the image which is reflected by a mirror which
is-laterally positioned to the analysis plate. The analysis plate
is in turn moved by a cam--placed vertically, coupled to a
horizontal shaft of a rotary electrical motor--which by rotating,
at constant speed, the respective profile causes the horizontal
plate to oscillate in an up and down movement so mixing occurs. The
system differs from the present invention as image acquisition is
made using a mirror placed between the camera and the sample plate.
This may lid to reading inaccuracies (acquired image distortion
which is not compensated by the system), which may then lid to an
analysis with possible failures. Furthermore, plate placement has
to follow a mechanical guiding of the plate and part of the
analysis plate is not covered in full and so the reactions for the
analysis are not fully covered, resulting in a misleading and
problematic analysis for the patient. Moreover, the invention
described in these documents mentions that the selection of the
area to be examined is performed manually.
[0015] The present invention relates to a new system that automates
the reading and interpretation of results, which are a major source
of errors in the administration of incompatible blood. The
determination time is smaller as compared to the above identified
documents, since it uses the slide test in conjunction with image
processing techniques that do not increase the test time.
[0016] The slide test is the blood type determination test
displaying results in a shorter time, with a very simple test
procedure: [0017] 1. Label clean glass slides with convenient
letters to identify the test to be carried out; [0018] 2. Pipette
one drop of each reagent to the slides; [0019] 3. Next to each drop
of reagent, add a drop of whole blood, having 1/4 of the reagent
drop size, or plasma depending on the test concerned; [0020] 4.
Using a mixing rod, mix uniformly the reagent and the blood or
plasma in a 2.5 cm.sup.2 area; [0021] 5. Results are verified
macroscopically to detect signs of agglutination, while rotating
the slide.
[0022] In most cases, agglutination occurs within a few seconds,
but not to ignore weaker antigens or antibodies, the results should
only be interpreted after 2 minutes, such as indicated in the
package insert of the procedure for the slide test.
[0023] Interpretation of results depends on the test type under
analysis, but basically it is determined by combining the
occurrence or non-occurrence of agglutination.
[0024] The occurrence of agglutination identifies the antigen or
antibody under analysis, while the non-occurrence means that the
antigen or antibody under study is not present in the blood
analyzed. In the cross-matching, the occurrence of agglutination
means that reaction will occur between the donor and the receiver
and, therefore, such blood transfusion should not be performed.
Since results are currently analyzed by naked eye, that is, in a
visual way, the possibility of misinterpretation of results may
arise and, therefore, it is proposed the developed device, such
that by capturing the results in an image form and a computer
program developed for this purpose, the correct identification of
agglutination is obtained.
[0025] Nevertheless, contemplation of new tests, such as minor
cross-match, cross-matching, phenotypes, research and
identification of antibodies, which are also necessary and
essential for performing a blood transfusion, even in emergency
situations, neither have been published, nor disclosed to the
scientific community, and in spite of the several efforts to
automate these techniques, as disclosed in the above-mentioned
patents, these systems still have a longer time for results than
those expected for the presented methodology.
[0026] This technology also provides for identifying some diseases,
such as, Typhoid Fever, Brucella, Tick Fever, Syphilis,
Mononucleosis, Hospital infections, Streptococcus bacteria,
Meningitis and Pneumonia, which albeit being in some cases
identified by other systems, the methodology and equipment are
different and in the case of Brucella disease, Tick Fever,
Mononucleosis, Meningitis and Pneumonia, no system was found
allowing such a fast analysis.
Abstract
[0027] In the present application, a portable device for detecting
the immunological agglutination of blood samples is described,
which allows identifying certain antigens and antibodies in blood,
characterized by comprising: [0028] an upper part (1) closed with a
lid (2), comprising a camera (3) which is centered surrounded by
LEDs (4) and connected to a laptop computer or other mobile device
which analyzes the captured images by image processing techniques;
[0029] a lower part (5), which in turn comprises a rotating
platform (6) connected to a motor (7) where the analysis plate (8)
is fixed; [0030] a power supply.
[0031] In a preferred embodiment, the device still has a connection
to a laptop computer or other mobile device performed via USB,
Wireless or Bluetooth, including a mobile phone (smartphone) or
tablet.
[0032] In another preferred embodiment, the camera (3) of said
device focuses directly on the rotating platform (6).
[0033] In yet another preferred embodiment, the upper part (1) and
the lower part (5) of the device are connected by a hinge on one
side and a lock on the opposite side.
[0034] In a preferred embodiment, the camera (3) focuses directly
on the analysis plate (8).
[0035] In another preferred embodiment, the analysis plate (8) is
sealed and transparent, and is a whole piece and comprises separate
circular containers (9), which are made of a sealing and
impermeable material and having holes (10).
[0036] In yet another preferred embodiment, the analysis plate (8)
is sealed and transparent, having a removable lid (11) which fits
on the base with the containers by a thread mechanism that joins
and fixes the two parts (lid and base with containers) and seals
the liquid outlet.
[0037] In a preferred embodiment, the analysis plate (8) used has 6
containers (9).
[0038] In another preferred embodiment, the analysis plate is also
a spinning one (12) having circular and deep containers (13).
[0039] In a yet preferred embodiment, the number of LEDs (4) ranges
between 4 and 6.
[0040] In a preferred embodiment, the power supply of the above
described device is a battery.
[0041] In a preferred embodiment, the method for detecting
immunological agglutination of blood samples, using the above
described device comprises the following steps:
a) Place each of the reagents in their respective containers of the
analysis plate, and then the patient's blood to be analyzed, both
in their respective proportions; b) Then, place the analysis plate
(8) in the device, fixing it to the rotating platform (6); c) Close
the device by joining the upper part of the device (1) with the
lower part (5) and start the device; d) The device activates the
camera (3), LEDs (4) and motor (7), according to the following
steps: [0042] i. The motor (7) moves rotationally the platform (6)
for a time between 60 and 130 seconds, during which the reaction
takes place; [0043] ii. The motor (7) stops and the LEDs (4) are
turned on; [0044] iii. After 2 minutes, the camera (3) captures the
image; e) LEDs (4) are turned off; f) The camera's image is sent to
the mobile device, which in turn stores this image; g) The image is
treated by image processing techniques; h) The classification
algorithm classifies the occurrence or non-occurrence of
agglutination according to the standard deviation value obtained
for each of the test containers.
[0045] In another preferred embodiment, the blood to reagents ratio
consists of a drop of whole blood having one-forth of the reagent
drop size.
[0046] In yet another preferred embodiment, the image processing
techniques used comprise the following steps:
a) Extracting the green color planes of the captured image, by
transforming the original 32-bit image into an 8-bit image so it
can be used; b) Separating the blood and reagent mixtures into two
regions, designated particle region and background region, by
assigning the value 1 (one) to all pixels belonging to a range of
established values and assigning the value 0 (zero) to all other
pixels in the image that does not belong to such established range;
c) Calculating the threshold value for each pixel based on
statistics of the adjacent pixel, using a 32-width and 32-height
default matrix (kernel), with a deviation factor which by default
is 0.20; d) In the image, assigning the value 1 (one) of pixel to
existing holes in the particles corresponding to the blood and
reagent mixtures; e) Then, removing the particles with the value of
1 (one) pixel to remove background noise from the image and ensure
that at the end only remain the particles related to the test
containers; f) Removing the particles which are on the borders of
the image, filling in the position with the same value of the
adjacent pixel in order to ensure that only remain for analyzing
the particles related to the test containers; g) Calculating the
metrics on the CenterofMassX and CenterofMassY image, which
together provide the coordinates of the center of mass of each
particle in the image; h) Extracting the light planes from the
original image and transforming the image into an 8-bit image,
which can now be used by other functions; i) Referencing the object
in the image that is an identifying mark of the order in which the
test was performed, keeping a profile of such object and searching
for such object in each of the images analyzed by the program,
giving the coordinates and calculating distances to other objects;
j) Identifying in each image six containers and presenting of the
coordinates of each one in order to calculate the aforementioned
distances; k) Quantifying a given image region defined by the
programmer, using each of the container's coordinates given in the
previous function to quantify a set of metrics as average of
pixels, minimum value, maximum value, standard deviation and
analyzed area, because the standard deviation values determines
whether or not agglutination has occurred in each test
container.
[0047] In a preferred embodiment, if the standard deviation is
higher than 16, the classification algorithm classifies as
agglutinated.
[0048] In another preferred embodiment, if the standard deviation
is less than 16, the classification algorithm classifies as not
agglutinated.
[0049] In another preferred embodiment, the results are sent by SMS
or email.
[0050] In a preferred embodiment, the blood type detection by
determining ABO and Rh; Tick Fever, Syphilis, Mononucleosis,
Hospital infections, Streptococcus bacteria, Meningitis and
Pneumonia.
General Description
[0051] The present invention is a new device and a method of use
thereof which allows identifying certain antigens and antibodies
present in the blood.
[0052] As is known to anyone skilled in the field of blood
transfusion, before administering a blood transfusion, it is
essential to perform some pre-transfusion testing, such as for
example, ABO and D (Rh) determination; ABO minor cross-matching; Rh
(C, c, E and e) and Kell (K) phenotyping; complete phenotyping
(Duffy, Kidd, Lewis, P, MNS, Lutheran, Kidd and Xg); antibody
research; antibody identification (for positive antibody research
results) and cross-matching. These tests are crucial for avoiding
any transfusion-derived problem.
[0053] Having as an object the development of a system enabling
fast results from the above mentioned tests and obtaining results
for the testing of typhoid fever, brucela, tick fever, syphilis,
mononucleosis, hospital infections, streptococcus bacteria,
meningitis and pneumonia as well, a new device using slide
methodology has been developed. These diseases can be detected in
this new device and methodology, resorting also to existing tests
for each disease, including: Widal test; Wright test; Weil-Felix
test (tick fever); VDRL (syphilis); Mononucleosis; MRSA and MSSA
SLIDEX (hospital infections); Stepto Plus SLIDEX (streptococcus
bacteria); SLIDEX Meningitis-Kit5 (meningitis) and SLIDEX
pneumo-Kit (Pneumonia).
[0054] As compared to existing documents and systems, this
methodology will reduce the time for results for each of these
tests, enabling its suitability for emergency situations where time
is a factor of paramount importance; it will also enable the
physical down-sizing of the ultimate system do be developed.
[0055] It also enables fast tests for some diseases which are not
included in the current systems. Furthermore, the fact that the
application has been developed for different operating systems, it
will enable its use by a wider range of devices, assisting to the
worldwide suitability of this methodology, either in an
underdeveloped or developed region. Thus, it is intended to have in
a few minutes a complete profile of the individual's blood type and
blood compatibilities, as well as the study of some diseases that
might be useful depending on the clinical scenario.
[0056] The developed system is limited in size to be portable and
inexpensive. Thus, this system has been developed with two
approaches, one incorporating a laptop, such as a tablet or
mini-pc, and another having a mobile phone (smartphone)
operating-based possibility, which together with a developed
application will perform the full test analysis and results
achievement. This application can be used with the system, which
will perform automatically all the tests or can also be used
without the system, where, for example, underdeveloped countries
could purchase the application and, even when performing the test
manually, would always have a non-subjective outcome--, devoid of
human interpretation error.
Device
[0057] The device of the present invention is a portable device
consisting of two parts, the upper part (1) closed by a lid (2) and
the lower part (5). The upper part (1) comprises a digital camera
(3) which is fixed in the center of the upper part, directly
focusing on the region of the sample to be analyzed, surrounded by
lighting, which may have between 4 and 6 LEDs (4) for a good image
view and, consequently, whether or not agglutination has occurred.
The LEDs will illuminate the analysis plate (8) located in the
lower part of the device (5), more specifically the rotating
platform (6). The lower part of the device (5) comprises a motor
(7), which is connected to the rotating platform (6) where the
plate having a test sample (8) is securely fitted.
[0058] The rotating platform (6) securely fits the closed analysis
plate (8) containing six separate containers, consisting of sealing
and impermeable material and having a hole (10).
[0059] The camera is connected to a laptop computer or another
mobile device such as a phone (smartphone) or a tablet via USB,
Wireless or Bluetooth, which analyzes the captured images through
image processing techniques.
[0060] The incorporation of a camera connected to the Internet via
USB, Wireless or Bluetooth enables sending the captured image to
the equipment referred in the previous paragraph. Through an
application developed for different operating systems, the image
can be used in any such equipment.
Methods of Analysis
[0061] The method for detecting immunological agglutination of
blood samples uses the above described device and comprises the
following steps: [0062] a) Place each of the reagents in their
respective containers of the analysis plate (8), and then the
patient's blood to be analyzed, both in their respective
proportions, i. e. 50 .mu.L of each reagent and 1 small drop of
whole blood having 1/4 of reagent drop size; [0063] b) Then, place
the analysis plate (8) in the device, fixing it to the rotating
platform (6), in order to avoid any displacement possibility during
processing due to the high motor (7) speeds. [0064] c) Close the
device by joining the upper part of the device (1) with the lower
part (5) and start the device; [0065] d) The device activates the
camera (3), LEDs (4) and motor (7), according to the following
steps: [0066] i. The motor (7) moves rotationally the platform (6)
for a time between 60 and 130 seconds, during which the reaction
takes place; [0067] ii. The motor (7) stops and the LEDs (4) are
turned on; [0068] iii. The camera (3) captures the image after 2
minutes only, so that weaker reactions are not hidden; [0069] e)
LEDs (4) are turned off; [0070] f) The camera's image is sent to
the mobile device, which in turn stores this image; [0071] g) The
image is treated by image processing techniques; [0072] h) The
classification algorithm classifies the occurrence or
non-occurrence of agglutination according to the standard deviation
value obtained for each of the test containers.
Image Processing Techniques
[0073] The image processing techniques used in the blood analysis
method through the above described device comprise the following
steps: [0074] a) Extract the green color planes of the captured
image by transforming the original 32-bit image into an 8-bit image
so it can be used; [0075] b) Separate the blood and reagent
mixtures into two regions, designated particle region and
background region, by assigning the value 1 (one) to all pixels
belonging to a range of established values and assigning the value
0 (zero) to all other pixels in the image that does not belong to
such established range; [0076] c) Calculate the threshold value for
each pixel based on statistics of the adjacent pixel, using a
32-width and 32-height default matrix (kernel), with a deviation
factor which by default is 0.20; [0077] d) In the image, assign the
value 1 (one) to existing holes in the particles corresponding to
the blood and reagent mixtures; [0078] e) Then, remove the
particles with the value 1 (one) to remove background noise from
the image and ensure that at the end only remain the particles
related to the test containers; [0079] f) Remove the particles
which are on the borders of the image, filling in the position with
the same value of the adjacent pixel, in order to ensure that only
remain for analyzing the particles related to test containers;
[0080] g) Calculate the metrics on the CenterofMassX and
CenterofMassY image, which together provide the coordinates of the
center of mass of each particle in the image; [0081] h) Extract the
light planes from the original image and transform the image into
an 8-bit image, which can now be used by other functions; [0082] i)
Reference the object in the image that is an identifying mark of
the order in which the test was performed, keeping a profile of
such object and searching for such object in each image analyzed by
the program, giving the coordinates and calculating the distances
to other objects; [0083] j) Identify in each image six containers
and present the coordinates of each in order to calculate the
aforementioned distances; [0084] k) Quantify a given image region
defined by the programmer, using each of the container's
coordinates given in the previous function to quantify a set of
metrics as average of pixels, minimum value, maximum value,
standard deviation and analyzed area, because the standard
deviation value determines whether or not agglutination has
occurred in each test container;
Classification Algorithm and Results
[0085] If the calculated standard deviation is less than 16, the
classification algorithm classifies as not agglutinated.
[0086] On the other hand, if the calculated standard deviation is
greater than 16, the classification algorithm classifies as
agglutinated.
System Advantages
[0087] The major advantages of this system are based on the
following features: [0088] uses the slide test which is a fast test
as regards to results achievement and that perfectly fits to
emergency situations; [0089] mixing is fully automatically
performed, having neither user intervention nor related human
errors; [0090] there are no contaminations between samples, given
the analysis plate and containers construction; [0091] whole
process of reading and interpretation of results is also automated
and optimized, which again reduces human errors associated with the
test procedure.
[0092] Thus, entire procedure takes approximately 3 minutes and the
results are reliable and accurate, with no associated human errors.
Furthermore, due to its construction, the device has a small size,
fitting perfectly to emergency situations.
BRIEF DESCRIPTION OF THE FIGURES
[0093] For an easier understanding of the invention, figures
representing preferred embodiments of the invention area appended
which, however, do not intend to limit the subject matter of this
application.
[0094] FIG. 1 illustrates a representation of the device wherein
(5) is the lower part, (1) the upper part; (3) the camera; (4) the
LEDs; (8) the analysis plate; (6) the rotating platform; (7) the
motor and (2) the lid.
[0095] FIG. 2 illustrates a representation of the analysis plate
(8).
[0096] FIG. 3 illustrates a representation of the analysis plate
(8) and the respective lid (11).
[0097] FIG. 4 illustrates a representation of the spinning plate
(12) with the respective containers (13).
[0098] FIG. 5 illustrates a representation of a spinning plate with
a lid (11).
DESCRIPTION OF EMBODIMENTS
Device
[0099] The device of the present invention is a portable device
consisting of two parts, the upper (1) and lower (5). The upper
part (1) comprises a digital camera (3) which is fixed in the
center of the upper part, directly focusing on the region of the
sample to be analyzed, surrounded by lighting, which may have
between 4 and 6 LEDs (4) for a good image view and, consequently,
whether or not agglutination has occurred. The LEDs will illuminate
the analysis plate (8) located in the lower part of the device (5),
more specifically in the rotating platform (6). The lower part of
the device (5) comprises a motor (7) which is connected to the
rotating platform (6) where the respective plate having a test
sample (8) is securely fitted. The plate may be of tests (8) or
spinning (12), i. e. containers are deeper.
[0100] The camera is connected to a laptop computer or another
mobile device such as a phone (smartphone) or tablet via USB,
Wireless or Bluetooth, which analyzes the captured images through
image processing techniques.
[0101] The incorporation of a camera connected to the internet via
USB, Wireless or Bluetooth enables sending the captured image to
the equipment referred in the previous paragraph. Through an
application developed for different operating systems, the image
can be used in any such equipment.
[0102] The device is closed, due to the existence of a lid (2),
with no ambient light input, which prevents the existence or
interference of artifacts in the image, which could compromise the
entire analysis performed, providing a wrong blood type result.
[0103] The fact that the camera focus directly on the samples
enables capture of a whole image and therefore a complete analysis
of all reactions.
[0104] The rotating platform (6) securely fits the respective
closed analysis plate (8) having six separate containers, which
have holes made of a sealable and impermeable material (10).
[0105] The upper part (1) and the lower part (5) of the device may
be connected by a hinge on one side and a lock on the opposite
side.
[0106] Motor can reach speeds between 0 and 13446 rpm.
[0107] The mixing and motor starting is made through a switch and
there is a potentiometer for regulating the motor speed, depending
on whether an analysis or spinning is performed, and a timer for
controlling the run time of each test.
[0108] The rotating platform is the basic part of the system which
assists in promoting the mixing of the components that are on the
board, since it is directly connected to the motor. This basic part
has a simple fitting system to allow entry and exit of the test and
spinning plates.
[0109] The camera and LEDs are properly protected by a fitting that
allows easy access to both for future repairs and replacement of
LEDs, if necessary.
[0110] Importantly, both system and camera require a power supply,
which is easily provided by a battery.
[0111] The plates have two possibilities for introducing liquids:
[0112] one in which the plate is a whole with a fixed lid and has
in each container a small hole sealed by an impermeable material,
allowing only the passage of a needle for introducing blood and
reagent, and preventing discharge of blood even during the mixing
process where the speeds are high; [0113] one in which the plate is
dismountable and has a removable lid that allows the introduction
of blood and reagent and may be fitted again by means of a thread,
being the parts fixed by rotating the lid on the plate, in such a
way that there is neither a leakage of blood and reagent, nor
mixing between containers.
[0114] In the latter plate, the sealing mechanism is a thread that
allows joining both parts (lid and base with containers) completely
sealing liquid spillage.
[0115] Both plates are properly sealed for having no contamination
or mixing between blood and reagent containers and are transparent
for easily capturing the image.
[0116] Containers are separate and sealed (isolated), enabling no
contamination between samples--in the claimed device the blood will
be introduced through the small holes present in each container
only, not being necessary to open the analysis plate;
[0117] Given the speed that the motor can reach, if blood spinning
is required, it can be performed on the device, in order to obtain
plasma segregated from its components, which might be used to
perform some tests. For this, a spinning plate (12) is used in
which containers must be deeper (13) for accommodating a larger
amount of blood (total liquid) than the plate used in tests.
[0118] The container walls are circular, such that, in the event of
blood and reagents deposition, these will always have the tendency
to drain/go down to the bottom of the container and
deposit/accumulate there. Thus, the liquid will always be deposited
at the base of the container and with a good area with the reaction
to analyze.
[0119] The base of containers can be not completely round. The base
of the container is flat or planar to facilitate visualization of
the reactions between blood and reagent. Thus, although the plate
is currently in the format shown, having some concavity, the same
plate completely straight might be used with flat-based
containers.
[0120] According to the methodology of the slide test, a drop of
blood having 1/4 of the reagent drop size or plasma should be
inserted, depending on the test concerned.
Method of Analysis
[0121] The method of analysis of the blood sample comprises the
following steps: [0122] a) Place each of the reagents in their
respective containers (9) of the analysis plate (8), and then the
blood to be analyzed, both in their respective proportions; [0123]
b) Then, place the analysis plate (8) in the device, by fixing it
to the rotating platform (6), in order to avoid any displacement
possibility during processing due to the high motor (7) speeds;
[0124] c) Close the device by joining the upper part of the device
(1) with the lower part (5) and start the device, adjusting the
speed according to that recommended for the test; [0125] d) The
device activates the camera (3), LEDs (4) and motor (7), according
to the following steps: [0126] i. The motor (7) moves rotationally
the platform (6) for a time between 60 and 130 second, during which
the reactions takes place; [0127] ii. The motor (7) stops and the
LEDs (4) are turned on; [0128] iii. The camera (3) captures the
image after 2 minutes only, so that weaker reactions are not
hidden; [0129] e) LEDs (4) are turned off; [0130] f) The camera's
image is sent to the mobile device, which in turn stores this
image; [0131] g) The image is treated by image processing
techniques; [0132] h) The classification algorithm classifies the
occurrence or non-occurrence of agglutination according to the
standard deviation value obtained in each of the test
containers.
[0133] In the event of performing a spinning, proceed as follows:
[0134] In a spinning plate place the recommended blood amount in
each of the required containers; [0135] Open up the system and
place the spinning plate (12) therein, well fixed for preventing
any displacement from its place; [0136] Then, close the system,
adjust the speed according to the one recommended for the test and
press the button to turn on the system and promote shaking; [0137]
After spinning, open the system for removing the spinning plate and
extracting the plasma; [0138] Finally, discharge the spinning plate
in a proper place.
[0139] In the case of ABO group and RhD testing 4 containers are
used and for RhD phenotype 6 containers are used.
Image Processing Techniques
[0140] The image processing techniques to detect the occurrence of
agglutination and, therefore, determine the result of the test
under analysis comprise the following steps: [0141] a) Extract the
green color planes of the captured image by transforming the
original 32-bit image into an 8-bit image so it can be used; [0142]
b) Separate the blood and reagent mixtures into two regions,
designated particle region and background region, by assigning the
value 1 (one) to all pixels belonging to a range of established
values and assigning the value 0 (zero) to all other pixels in the
image that does not belong to such established range; [0143] c)
Calculate the threshold value for each pixel based on statistics of
the adjacent pixel, using a 32-width and 32-height default matrix
(kernel), with a deviation factor which by default is 0.20; [0144]
d) In the image, assign the value 1 (one) to existing holes in the
particles corresponding to blood and reagent mixtures; [0145] e)
Then, remove the particles with the value 1 (one) to remove
background noise from the image and ensure that at the end only
remain the particles related to test containers; [0146] f) Remove
the particles which are on the borders of the image, filling in the
position with the same value of the adjacent pixel in order to
ensure that only remain for analyzing particles related to test
containers; [0147] g) Calculate the metrics on the CenterofMassX
and CenterofMassY image, which together provide the coordinates of
the center of mass of each particle in the image; [0148] h) Extract
the light planes from the original image and transform the image
into an 8-bit image, which can now be used by other functions;
[0149] i) Reference the object in the image that is an identifying
mark of the order in which the test was performed, keeping a
profile of such object and searching for such object in each image
analyzed by the program, giving the coordinates and calculating the
distances to other objects; [0150] j) Identify in each image six
containers and present the coordinates of each in order to
calculate the aforementioned distances; [0151] k) Quantify a given
image region defined by the programmer, using each of the
container's coordinates given in the previous function to quantify
a set of metrics as average of pixels, minimum value, maximum
value, standard deviation and analyzed area, because the standard
deviation value determines whether or not agglutination has
occurred in each test container.
[0152] The image processing techniques have been developed using
the Labview software and also with the programming languages C# and
C, such that they can be used by different mobile devices. The
possibility of having the application in a mobile device enables
its worldwide use. The developed software, as mentioned, uses image
processing techniques to detect agglutination and classification
algorithms to determine the result of the tests performed.
[0153] The application's main functions are: [0154] Image Buffer:
Store a copy--which allows saving the original image captured by
the camera in order to keep it intact for further use later on;
[0155] Color Plane Extraction: RGB Green Plane--extracts green
planes from the captured image, allowing transforming the original
32-bit image into an 8-bit image, such that it can be used by
subsequent functions required for processing; [0156] Auto Threshold
Clustering--this function applies a threshold (threshold, in
English threshold) based on statistical techniques called
clustering and is used to separate the blood and reagent mixtures
into two regions, designated "particle region" and "background
region". This process consists of changing all pixels belonging to
a certain range of established values (designated threshold range)
by changing all other pixels in the image to zero (0). It is
important to note that the function is automatic and the users need
not to specify the range values. To set the threshold, the function
automatically uses the histogram values; [0157] Local Threshold:
Niblack--in this function the threshold value for each pixel is
calculated based on statistics of the adjacent pixel. A 32-width
and 32-height default matrix (kernel) is used, with a deviation
factor which by default is 0.20. This function is extremely
important to isolate particles to be analyzed. After applying this
function, particles corresponding to blood and reagent mixtures are
then isolated from the rest of the image; [0158] Adv. Morphology:
Fill holes--which allow completely filling the existing holes in
the particles; [0159] Adv. Morphology: Remove small objects--as the
name indicates, it removes small particles, by removing trash
background that is spoiling the image and ensuring that ultimately
only remain particles relating to test containers; [0160] Adv.
Morphology: Remove border objects--removes particles that are on
the borders of the image, ensuring once again that remain for
analysis particles relating to test containers only; [0161]
Particle Analysis--this function is extremely useful since it
allows obtaining a series of metrics about the image, such as
CenterofMassX and CenterofMassY, which together provide the
coordinates of the center of mass of each particle in the image;
the center of Mass X is a coordinate that together with the center
of Mass Y provide a position in the particle (blood/reagent
mixture) which corresponds to the center of mass of such
particle--the mass of the particle pixels is averaged and the value
obtained according to the following formulae:
[0161] CenterofmassX = m 1 x 1 + m 2 x 2 + m 3 x 3 + + m n x n m 1
+ m 2 + m 3 + + m n ##EQU00001## CenterofmassY = m 1 y 1 + m 2 y 2
+ m 3 y 3 + + m n y n m 1 + m 2 + m 3 + + m n ##EQU00001.2## [0162]
Image Buffer: Retrieve Copy--to retrieve the original image saved
in the first function presented in such a way that it can be used
by the following functions; [0163] Color Plane Extraction: HSL
Luminance Plane--extracts light planes from the original image and
allows once again transforming the image into an 8-bit image which
can now be used by other functions; [0164] Pattern Matching--this
function is crucial for determining the test result. Basically, it
consists in referencing an object in the image that actually is an
identifying mark of the order in which the test was performed. The
function save a profile of such object and will try to search for
such an object in each of the images that the program analyzes.
Once the reference object is found, it returns its coordinates and,
based on these, it allows calculating distances to other objects
(in this case, to each of the particles corresponding to the test
containers). Knowing the distances, these are ordered and the
correct order of test analysis obtained, as well as the result of
the test performed, which will then be provided by the
classification algorithm; [0165] Geometric Matching--this function
associated with the previous one help in determining the result of
the test. In this case, provided the profile of each test
container, the function will identify in each image six containers
and will return the coordinates of each container. Through the
coordinates of each of them, the aforementioned distances are
calculated (from the reference object to each of the containers).
In this way, the correct order of the test analysis is known;
[0166] Quantify--quantifies a particular image region defined by
the programmer, using each of the container's coordinates provided
in the previous function. Quantification allows obtaining a set of
metrics such as average of pixels, minimum value, maximum value,
standard deviation and analyzed area. In this case, the standard
deviation value is the important metric for the work, as it is
based on this value that it is determined whether or not
agglutination has occurred in each test container. [0167]
Classification Algorithm--the classification algorithm classifies
the occurrence, or not, of agglutination in accordance with the
standard deviation value obtained for each of the test containers.
If the standard deviation is greater than 16, classifies as
agglutinated, if the standard deviation is less than 16 classifies
as non-agglutinated. In addition, combination of results according
to agglutination and no agglutination allows to identify the test
result for each of the tests performed, being either a blood group,
an antibody, a compatibility or a disease.
[0168] The function that removes border particles eliminates
particles that touch the border of the image, that is, the outer
boundaries of the image. In other words, if the particle touches
the image borders, on the sidelines, it is eliminated. This is used
to eliminate the circle made by the system base that is captured by
the camera and does-not account for image analysis. No values are
used, it is just enough to touch on said image boundaries.
[0169] The Image Processing techniques developed and remaining
algorithms are capable of being used in mobile devices such as
tables and mobile phones with Windows Phone, Android and iOS
operating system. These applications are primarily based on
capturing an image by the mobile device and processing of such an
image by the image processing techniques developed; or image
capturing can be performed by the system camera and sent to the
mobile device via Bluetooth/Wireless, being the Image Processing of
the sent image performed therein by the developed application.
[0170] The above described software also allows sending electronic
mail and short messages (sms) to a mobile phone with the results of
the tests performed, allowing, in the event of tests performed
outside the laboratory, to prepare in advance a compatible blood
unit.
Examples
[0171] In the following example, results are presented for ABO
group and RhD testing, and for RhD phenotyping. Taking into account
that occurrence of agglutination identifies the antigen present, in
the case of ABO group and RhD testing, there is a range of possible
results, some of which are shown in Table 1. Analyzing Table 1, it
follows that, for example, Example 1 Agglutinated in the presence
of anti-A, anti-AB and anti-D reagents, indicating the presence of
antigens A and D. Since D indicates whether it is Rh positive or Rh
negative, the occurrence of agglutination indicates positiveness,
and therefore the result of this test is A Positive. The same
reasoning will be applied to the other examples. For example,
Example 4 has O Positive as its result, because the single reagent
which agglutinated the blood was in the presence of Anti-D reagent,
indicating the positiveness of Rh and indicating that no other
antigens are present, hence it is a O or zero positive.
TABLE-US-00001 TABLE 1 Expected results with classification
algorithm for ABO group and Rh testing AntiA Anti-B Anti-AB Anti-D
Reagent Reagent Reagent Reagent Result Example 1 Agglutinated Not
Agglutinated Agglutinated A Agglutinated Positive Example 2 Not
Agglutinated Agglutinated Not B Agglutinated Agglutinated Negative
Example 3 Agglutinated Agglutinated Agglutinated Not AB Negative
Agglutinated Example 4 Not Not Not Agglutinated Agglutinated O
agglutinated Agglutinated Positive
[0172] In case of RhD phenotype testing, the procedure is similar.
The agglutination identifies the presence of the antigen and as
such, analyzing one of the examples, e. g. Example 2, taking into
account that agglutinated in the presence of anti-D, anti-C, anti-c
and anti-E reagents, with no agglutination in the others, the
present phenotype is DcCe.
TABLE-US-00002 TABLE 2 Results for the phenotype testing with the
classification algorithm Anti-D Anti-C Anti-c Anti-E Anti-e Anti-K
Re- Reagent Reagent Reagent Reagent Reagent Reagent sult Exam-
Agglu- Not Agglu- Agglu- Agglu- Not DcEe ple 1 tinated Agglu-
tinated tinated tinated Agglu- tinated tinated Exam- Agglu- Agglu-
Agglu- Not Agglu- Not DcCe ple 2 tinated tinated tinated Agglu-
tinated Agglu- tinated tinated
* * * * *