CA2377602A1 - Multi-neural net imaging apparatus and method - Google Patents

Multi-neural net imaging apparatus and method Download PDF

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Publication number
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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Prior art keywords
classification
features
extracted features
image
selecting
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Granted
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CA002377602A
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French (fr)
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CA2377602C (en
Inventor
Harvey L. Kasdan
Michael R. Ashe
Minn Chung
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Iris International Inc
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Individual
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Priority claimed from US09/841,941 external-priority patent/US6947586B2/en
Priority claimed from PCT/US2001/013451 external-priority patent/WO2001082216A1/en
Publication of CA2377602A1 publication Critical patent/CA2377602A1/en
Application granted granted Critical
Publication of CA2377602C publication Critical patent/CA2377602C/en
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10Investigating individual particles
    • G01N15/14Optical investigation techniques, e.g. flow cytometry
    • G01N15/1429Signal processing
    • G01N15/1433Signal processing using image recognition
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00Scenes; Scene-specific elements
    • G06V20/60Type of objects
    • G06V20/69Microscopic objects, e.g. biological cells or cellular parts
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/01Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials specially adapted for biological cells, e.g. blood cells
    • G01N2015/016White blood cells
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10Investigating individual particles
    • G01N15/14Optical investigation techniques, e.g. flow cytometry
    • G01N2015/1488Methods 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.

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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
CA2377602A 2000-04-24 2001-04-24 Multi-neural net imaging apparatus and method Expired - Lifetime CA2377602C (en)

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

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CA2377602A Expired - Lifetime CA2377602C (en) 2000-04-24 2001-04-24 Multi-neural net imaging apparatus and method

Country Status (6)

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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)

* Cited by examiner, † Cited by third party
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)

* Cited by examiner, † Cited by third party
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)

* Cited by examiner, † Cited by third party
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

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2902769A3 (en) * 2014-01-29 2015-09-02 Sysmex Corporation Blood cell analyzer

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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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