CA2377602A1 - Multi-neural net imaging apparatus and method - Google Patents
Multi-neural net imaging apparatus and method Download PDFInfo
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- CA2377602A1 CA2377602A1 CA002377602A CA2377602A CA2377602A1 CA 2377602 A1 CA2377602 A1 CA 2377602A1 CA 002377602 A CA002377602 A CA 002377602A CA 2377602 A CA2377602 A CA 2377602A CA 2377602 A1 CA2377602 A1 CA 2377602A1
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- classification
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- extracted features
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- 238000000034 method Methods 0.000 title claims abstract 18
- 238000003384 imaging method Methods 0.000 title claims abstract 4
- 239000002245 particle Substances 0.000 claims abstract 7
- 238000013528 artificial neural network Methods 0.000 claims 6
- 238000000605 extraction Methods 0.000 claims 4
- 230000001537 neural effect Effects 0.000 claims 2
- 238000005192 partition Methods 0.000 abstract 1
- 238000012805 post-processing Methods 0.000 abstract 1
- 238000007781 pre-processing Methods 0.000 abstract 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/10—Investigating individual particles
- G01N15/14—Optical investigation techniques, e.g. flow cytometry
- G01N15/1429—Signal processing
- G01N15/1433—Signal processing using image recognition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/01—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials specially adapted for biological cells, e.g. blood cells
- G01N2015/016—White blood cells
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/10—Investigating individual particles
- G01N15/14—Optical investigation techniques, e.g. flow cytometry
- G01N2015/1488—Methods for deciding
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- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Physics & Mathematics (AREA)
- Dispersion Chemistry (AREA)
- Pathology (AREA)
- Theoretical Computer Science (AREA)
- Signal Processing (AREA)
- Molecular Biology (AREA)
- Biomedical Technology (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- Immunology (AREA)
- Multimedia (AREA)
- Image Analysis (AREA)
- Image Processing (AREA)
- Investigating Or Analysing Biological Materials (AREA)
- Apparatus Associated With Microorganisms And Enzymes (AREA)
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
- Apparatus For Radiation Diagnosis (AREA)
- Closed-Circuit Television Systems (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
A multi-neural net imaging apparatus (2) and method for classification of image elements, such as biological particles. The multi-net structure utilizes subgroups of particle features to partition the decision space by an attribute or physical characteristic of the particle and/or by individual and group particle classification that includes an unknown category. Preprocessing (6) classifies particles as artifacts based on certain physical characteristics.
Post processing (6) enables the use of contextual information either available from other sources or gleaned from the actual decision making process to further process the probability factors and enhance the decisions.
Post processing (6) enables the use of contextual information either available from other sources or gleaned from the actual decision making process to further process the probability factors and enhance the decisions.
Claims (24)
1. A method of classifying an element in an image into one of a plurality of classification classes, wherein the element has a plurality of features, the method comprising the steps of:
extracting the plurality of features from the image of the element;
determining a classification class of the element by at least one of:
selecting and processing a first subgroup of the extracted features to determine a physical characteristic of the element, and selecting and processing a second subgroup of the extracted features in response to the physical characteristic determined to determine a classification class of the element;
and selecting and processing a third subgroup of the extracted features to determine a group of classification classes of the element, and selecting and processing a fourth subgroup of the extracted features in response to the determined classification class group to determine a classification class of the element; and modifying the determined classification class of the element based upon a plurality of previously determined classification class determinations.
extracting the plurality of features from the image of the element;
determining a classification class of the element by at least one of:
selecting and processing a first subgroup of the extracted features to determine a physical characteristic of the element, and selecting and processing a second subgroup of the extracted features in response to the physical characteristic determined to determine a classification class of the element;
and selecting and processing a third subgroup of the extracted features to determine a group of classification classes of the element, and selecting and processing a fourth subgroup of the extracted features in response to the determined classification class group to determine a classification class of the element; and modifying the determined classification class of the element based upon a plurality of previously determined classification class determinations.
2. The method of claim 1, wherein the element is a biological particle.
3. The method of claim 1, wherein each of the determinations includes assigning a probability factor, and further including the step of modifying the determined classification class to an artifact classification in the event one or more of the probability factors used to classify the element fails to exceed a predetermined threshold value.
4. The method of claim 1, further comprising the step of:
classifying the element as an artifact based on a physical characteristic of the element, wherein the artifact element bypasses the determination of the classification class of the element.
classifying the element as an artifact based on a physical characteristic of the element, wherein the artifact element bypasses the determination of the classification class of the element.
5. The method of claim 1, further comprising the steps of:
determining whether a boundary of the element intersects a border of an image containing the element, and modifying the determined classification class of the element to an artifact classification in the event the element boundary and image border are determined to intersect.
determining whether a boundary of the element intersects a border of an image containing the element, and modifying the determined classification class of the element to an artifact classification in the event the element boundary and image border are determined to intersect.
6. The method of claim 1, wherein the processing of the first, second, third and fourth subgroups of the extracted features is performed using neural nets.
7. The method of claim 6, further comprising the steps of:
training the neural nets by selecting and processing the first, second, third and fourth subgroups of the extracted features using a training set of known elements along with a test set of elements, wherein the training of the neural nets is repeatedly performed until the accuracy rate of the determination of classification class of the test set of elements reaches a predetermined value.
training the neural nets by selecting and processing the first, second, third and fourth subgroups of the extracted features using a training set of known elements along with a test set of elements, wherein the training of the neural nets is repeatedly performed until the accuracy rate of the determination of classification class of the test set of elements reaches a predetermined value.
8. The method of claim 6, wherein the first, second, third and fourth subgroups of the plurality of features are selected by modifying each of the feature values by a predetermined amount, and selecting those features that affect the output of the respectively neural net the most.
9. The method of claim 1, wherein one of the plurality of extracted features is symmetry of the element, and the extraction of the symmetry feature includes:
defining a first line segment that crosses a centroid of the element;
defining a second and third line segments for points along the first line segment that orthogonally extend away from the first line segment in opposite directions;
utilizing the difference between the lengths of the second and third line segments to calculate the extracted symmetry feature of the element.
defining a first line segment that crosses a centroid of the element;
defining a second and third line segments for points along the first line segment that orthogonally extend away from the first line segment in opposite directions;
utilizing the difference between the lengths of the second and third line segments to calculate the extracted symmetry feature of the element.
10. The method of claim 1, wherein one of the plurality of extracted features is skeletonization of the element image, and the extraction of the skeletonization feature includes orthogonally collapsing a boundary of the element to form one or more line segments.
11. The method of claim 1, wherein at least one of the plurality of extracted features is a measure of a spatial distribution of the element image, and at least another one of the plurality of extracted features is a measure of a spatial frequency domain of the element image.
12. An imaging apparatus for classifying an element in an image into one of a plurality of classification classes, wherein the element has a plurality of features, the apparatus comprising:
means for extracting the plurality of features from the image of the element;
means for determining a classification class of the element, the determining means including at least one of:
means for selecting and processing a first subgroup of the extracted features to determine a physical characteristic of the element, and means for selecting and processing a second subgroup of the extracted features in response to the physical characteristic determined to determine a classification class of the element; and means for selecting and processing a third subgroup of the extracted features to determine a group of classification classes of the element, and means for selecting and processing a fourth subgroup of the extracted features in response to the determined classification class group to determine a classification class of the element; and means for modifying the determined classification class of the element based upon a plurality of previously determined classification class determinations.
means for extracting the plurality of features from the image of the element;
means for determining a classification class of the element, the determining means including at least one of:
means for selecting and processing a first subgroup of the extracted features to determine a physical characteristic of the element, and means for selecting and processing a second subgroup of the extracted features in response to the physical characteristic determined to determine a classification class of the element; and means for selecting and processing a third subgroup of the extracted features to determine a group of classification classes of the element, and means for selecting and processing a fourth subgroup of the extracted features in response to the determined classification class group to determine a classification class of the element; and means for modifying the determined classification class of the element based upon a plurality of previously determined classification class determinations.
13. The apparatus of claim 12, wherein the element is a biological particle.
14. The apparatus of claim 12, wherein each of the determinations includes assigning a probability factor, and wherein the determining means further includes means for modifying the determined classification class to an artifact classification in the event one or more of the probability factors used to classify the element fails to exceed a predetermined threshold value.
15. The apparatus of claim 12, further comprising:
means for classifying the element as an artifact based on a physical characteristic of the element, wherein the artifact element bypasses the determining means.
means for classifying the element as an artifact based on a physical characteristic of the element, wherein the artifact element bypasses the determining means.
16. The apparatus of claim 12, further comprising:
means for determining whether a boundary of the element intersects a border of an image containing the element, and means for modifying the determined classification class of the element to an artifact classification in the event the element boundary and image border are determined to intersect.
means for determining whether a boundary of the element intersects a border of an image containing the element, and means for modifying the determined classification class of the element to an artifact classification in the event the element boundary and image border are determined to intersect.
17. The apparatus of claim 12, wherein the processing of the first, second, third and fourth subgroups of the extracted features is performed using neural nets.
18. The apparatus of claim 17, further comprising:
means for training the neural nets by selecting and processing the first, second, third and fourth subgroups of the extracted features using a training set of known elements along with a test set of elements, wherein the training means repeatedly trains the neural nets until the accuracy rate of the determination of classification class of the test set of elements reaches a predetermined value.
means for training the neural nets by selecting and processing the first, second, third and fourth subgroups of the extracted features using a training set of known elements along with a test set of elements, wherein the training means repeatedly trains the neural nets until the accuracy rate of the determination of classification class of the test set of elements reaches a predetermined value.
19. The apparatus of claim 17, wherein the first, second, third and fourth subgroups of the plurality of features are selected by modifying each of the feature values by a predetermined amount, and selecting those features that affect the output of the respectively neural net the most.
20. The apparatus of claim 12, wherein one of the plurality of extracted features is symmetry of the element, and the extraction means includes:
means for defining a first line segment that crosses a centroid of the element;
means for defining a second and third line segments for points along the first line segment that orthogonally extend away from the first line segment in opposite directions;
means for utilizing the difference between the lengths of the second and third line segments to calculate the extracted symmetry feature of the element.
means for defining a first line segment that crosses a centroid of the element;
means for defining a second and third line segments for points along the first line segment that orthogonally extend away from the first line segment in opposite directions;
means for utilizing the difference between the lengths of the second and third line segments to calculate the extracted symmetry feature of the element.
21. The apparatus of claim 12, wherein one of the plurality of extracted features is skeletonization of the element image, and the extraction means further includes means for orthogonally collapsing a boundary of the element to form one or more line segments.
22. The apparatus of claim 12, wherein at least one of the plurality of extracted features is a measure of a spatial distribution of the element image, and at least another one of the plurality of extracted features is a measure of a spatial frequency domain of the element image.
23. A method of classifying an element in an image into one of a plurality of classifications, wherein the element has a plurality of features, the method comprising the steps of:
extracting the plurality of features from the image;
determining a classification of the element based upon the plurality of features extracted by a first determination criteria, wherein the first determination criteria includes the classification of the element as an unknown classification;
determining a classification of the element by a second determination criteria, different from the first determination criteria, in the event the element is classified as an unknown classification by the first determination criteria; and determining the classification of the element by a third determination criteria, different from the first and second determination criteria, in the event the element is classified as one of a plurality of classifications by the first determination criteria.
extracting the plurality of features from the image;
determining a classification of the element based upon the plurality of features extracted by a first determination criteria, wherein the first determination criteria includes the classification of the element as an unknown classification;
determining a classification of the element by a second determination criteria, different from the first determination criteria, in the event the element is classified as an unknown classification by the first determination criteria; and determining the classification of the element by a third determination criteria, different from the first and second determination criteria, in the event the element is classified as one of a plurality of classifications by the first determination criteria.
24. An imaging apparatus for classifying an element in an image into one of a plurality of classification classes, wherein the element has a plurality of features, the apparatus comprising:
an extractor for extracting the plurality of features from the image of the element;
a first processor that determines a classification class of the element by at least one of:
selecting and processing a first subgroup of the extracted features to determine a physical characteristic of the element, and selecting and processing a second subgroup of the extracted features in response to the physical characteristic determined to determine a classification class of the element;
and selecting and processing a third subgroup of the extracted features to determine a group of classification classes of the element, and selecting and processing a fourth subgroup of the extracted features in response to the determined classification class group to determine a classification class of the element; and a second processor that modifies the determined classification class of the element based upon a plurality of previously determined classification class determinations.
an extractor for extracting the plurality of features from the image of the element;
a first processor that determines a classification class of the element by at least one of:
selecting and processing a first subgroup of the extracted features to determine a physical characteristic of the element, and selecting and processing a second subgroup of the extracted features in response to the physical characteristic determined to determine a classification class of the element;
and selecting and processing a third subgroup of the extracted features to determine a group of classification classes of the element, and selecting and processing a fourth subgroup of the extracted features in response to the determined classification class group to determine a classification class of the element; and a second processor that modifies the determined classification class of the element based upon a plurality of previously determined classification class determinations.
Applications Claiming Priority (5)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
USUNKNOWN | 1999-10-26 | ||
US19923700P | 2000-04-24 | 2000-04-24 | |
US60/199,237 | 2000-04-24 | ||
US09/841,941 US6947586B2 (en) | 2000-04-24 | 2001-04-24 | Multi-neural net imaging apparatus and method |
PCT/US2001/013451 WO2001082216A1 (en) | 2000-04-24 | 2001-04-24 | Multi-neural net imaging apparatus and method |
Publications (2)
Publication Number | Publication Date |
---|---|
CA2377602A1 true CA2377602A1 (en) | 2001-11-01 |
CA2377602C CA2377602C (en) | 2012-04-10 |
Family
ID=46149960
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CA2377602A Expired - Lifetime CA2377602C (en) | 2000-04-24 | 2001-04-24 | Multi-neural net imaging apparatus and method |
Country Status (6)
Country | Link |
---|---|
JP (1) | JP4945045B2 (en) |
AT (1) | ATE434799T1 (en) |
AU (1) | AU2001257299A1 (en) |
CA (1) | CA2377602C (en) |
DK (1) | DK1301894T3 (en) |
ES (1) | ES2328450T3 (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP2902769A3 (en) * | 2014-01-29 | 2015-09-02 | Sysmex Corporation | Blood cell analyzer |
Families Citing this family (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
GB0218909D0 (en) * | 2002-08-15 | 2002-09-25 | Qinetiq Ltd | Histological assessment |
JP5321145B2 (en) * | 2009-03-04 | 2013-10-23 | 日本電気株式会社 | Image diagnosis support apparatus, image diagnosis support method, image diagnosis support program, and storage medium thereof |
ES2388169B1 (en) * | 2010-02-25 | 2013-08-19 | Universidad De León | ARTIFICIAL VISION PROCEDURE FOR THE DETECTION OF DISTAL CYTOPLASMATIC DROPS IN ESPERMATOZOIDS. |
EP3500964A1 (en) | 2016-08-22 | 2019-06-26 | Iris International, Inc. | System and method of classification of biological particles |
WO2019241471A1 (en) * | 2018-06-13 | 2019-12-19 | Klaris Corporation | Compositions and methods for cellular phenotype assessment of a sample using confined volume arrays |
EP3859304B1 (en) * | 2018-09-27 | 2024-09-18 | HORIBA, Ltd. | Method for generating data for particle analysis, program for generating data for particle analysis, and device for generating data for particle analysis |
Family Cites Families (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH03131756A (en) * | 1989-10-18 | 1991-06-05 | Hitachi Ltd | Automatic classifying device for blood cell |
JPH07286954A (en) * | 1994-04-19 | 1995-10-31 | Hitachi Ltd | Cell automatic classification device |
JP3050046B2 (en) * | 1994-07-18 | 2000-06-05 | 株式会社日立製作所 | Automatic particle classification system |
JPH0991430A (en) * | 1995-09-27 | 1997-04-04 | Hitachi Ltd | Pattern recognition device |
JPH10302067A (en) * | 1997-04-23 | 1998-11-13 | Hitachi Ltd | Pattern recognition device |
JPH11132984A (en) * | 1997-10-24 | 1999-05-21 | Fuji Electric Co Ltd | Method for manufacturing detection element for gas sensor |
JPH11132932A (en) * | 1997-10-29 | 1999-05-21 | Hitachi Ltd | System for classifying particle image on organism and method for reclassifying particle |
-
2001
- 2001-04-24 JP JP2001579226A patent/JP4945045B2/en not_active Expired - Lifetime
- 2001-04-24 AU AU2001257299A patent/AU2001257299A1/en not_active Abandoned
- 2001-04-24 CA CA2377602A patent/CA2377602C/en not_active Expired - Lifetime
- 2001-04-24 DK DK01930796T patent/DK1301894T3/en active
- 2001-04-24 AT AT01930796T patent/ATE434799T1/en active
- 2001-04-24 ES ES01930796T patent/ES2328450T3/en not_active Expired - Lifetime
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP2902769A3 (en) * | 2014-01-29 | 2015-09-02 | Sysmex Corporation | Blood cell analyzer |
Also Published As
Publication number | Publication date |
---|---|
ATE434799T1 (en) | 2009-07-15 |
ES2328450T3 (en) | 2009-11-13 |
JP4945045B2 (en) | 2012-06-06 |
AU2001257299A1 (en) | 2001-11-07 |
CA2377602C (en) | 2012-04-10 |
JP2004505233A (en) | 2004-02-19 |
DK1301894T3 (en) | 2009-08-24 |
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MKEX | Expiry |
Effective date: 20210426 |