Automated Classification for Breast Cancer Histopathology Images

Is Stain Normalization Important?

Vibha Gupta, Apurva Singh, Kartikeya Sharma, Arnav Bhavsar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Breast cancer is one of the most commonly diagnosed cancer in women worldwide. A popular diagnostic method involves histopathological microscopy imaging, which can be augmented by automated image analysis. In histopathology image analysis, stain normalization is an important procedure of color transfer between a source (reference) and the test image, that helps in addressing an important concern of stain color variation. In this work, we hypothesize that if color-texture information is well captured with suitable features using data containing sufficient color variation, it may obviate the need for stain normalization. Considering that such an image analysis study is relatively less explored, some questions are yet unresolved such as (a) How can texture and color information be effectively extracted and used for classification so as to reduce the burden on the uniform staining or stain normalization. (b) Are there good feature-classifier combinations which work consistently across all magnifications? (c) Can there be an automated way to select reference image for stain normalization? In this work, we attempt to address such questions. In the process, we compare the independent texture and color channel information with that of some more sophisticated features which consider jointly color-texture information. We have extracted above features using images with and without stain normalization to validate the above hypothesis. Moreover, we also compare different types of contemporary classification in conjunction with the above features. Based on the results of our exhaustive experimentation we provide some useful indications.

Original languageEnglish
Title of host publicationComputer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures - 4th International Workshop, CARE 2017 and 6th International Workshop, CLIP 2017 Held in Conjunction with MICCAI 2017, Proceedings
PublisherSpringer Verlag
Pages160-169
Number of pages10
ISBN (Print)9783319675428
DOIs
Publication statusPublished - 01-01-2017
Externally publishedYes
Event4th International Workshop on Computer Assisted and Robotic Endoscopy, CARE 2017 and 6th International Workshop on Clinical Image-Based Procedures, CLIP 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 14-09-201714-09-2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10550 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Workshop on Computer Assisted and Robotic Endoscopy, CARE 2017 and 6th International Workshop on Clinical Image-Based Procedures, CLIP 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period14-09-1714-09-17

Fingerprint

Breast Cancer
Normalization
Color
Texture
Textures
Image Analysis
Image analysis
Classifier Combination
Microscopy
Experimentation
Diagnostics
Cancer
Microscopic examination
Classifiers
Imaging
Sufficient
Imaging techniques

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Gupta, V., Singh, A., Sharma, K., & Bhavsar, A. (2017). Automated Classification for Breast Cancer Histopathology Images: Is Stain Normalization Important? In Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures - 4th International Workshop, CARE 2017 and 6th International Workshop, CLIP 2017 Held in Conjunction with MICCAI 2017, Proceedings (pp. 160-169). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10550 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-67543-5_16
Gupta, Vibha ; Singh, Apurva ; Sharma, Kartikeya ; Bhavsar, Arnav. / Automated Classification for Breast Cancer Histopathology Images : Is Stain Normalization Important?. Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures - 4th International Workshop, CARE 2017 and 6th International Workshop, CLIP 2017 Held in Conjunction with MICCAI 2017, Proceedings. Springer Verlag, 2017. pp. 160-169 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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title = "Automated Classification for Breast Cancer Histopathology Images: Is Stain Normalization Important?",
abstract = "Breast cancer is one of the most commonly diagnosed cancer in women worldwide. A popular diagnostic method involves histopathological microscopy imaging, which can be augmented by automated image analysis. In histopathology image analysis, stain normalization is an important procedure of color transfer between a source (reference) and the test image, that helps in addressing an important concern of stain color variation. In this work, we hypothesize that if color-texture information is well captured with suitable features using data containing sufficient color variation, it may obviate the need for stain normalization. Considering that such an image analysis study is relatively less explored, some questions are yet unresolved such as (a) How can texture and color information be effectively extracted and used for classification so as to reduce the burden on the uniform staining or stain normalization. (b) Are there good feature-classifier combinations which work consistently across all magnifications? (c) Can there be an automated way to select reference image for stain normalization? In this work, we attempt to address such questions. In the process, we compare the independent texture and color channel information with that of some more sophisticated features which consider jointly color-texture information. We have extracted above features using images with and without stain normalization to validate the above hypothesis. Moreover, we also compare different types of contemporary classification in conjunction with the above features. Based on the results of our exhaustive experimentation we provide some useful indications.",
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Gupta, V, Singh, A, Sharma, K & Bhavsar, A 2017, Automated Classification for Breast Cancer Histopathology Images: Is Stain Normalization Important? in Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures - 4th International Workshop, CARE 2017 and 6th International Workshop, CLIP 2017 Held in Conjunction with MICCAI 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10550 LNCS, Springer Verlag, pp. 160-169, 4th International Workshop on Computer Assisted and Robotic Endoscopy, CARE 2017 and 6th International Workshop on Clinical Image-Based Procedures, CLIP 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017, Quebec City, Canada, 14-09-17. https://doi.org/10.1007/978-3-319-67543-5_16

Automated Classification for Breast Cancer Histopathology Images : Is Stain Normalization Important? / Gupta, Vibha; Singh, Apurva; Sharma, Kartikeya; Bhavsar, Arnav.

Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures - 4th International Workshop, CARE 2017 and 6th International Workshop, CLIP 2017 Held in Conjunction with MICCAI 2017, Proceedings. Springer Verlag, 2017. p. 160-169 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10550 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Bhavsar, Arnav

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N2 - Breast cancer is one of the most commonly diagnosed cancer in women worldwide. A popular diagnostic method involves histopathological microscopy imaging, which can be augmented by automated image analysis. In histopathology image analysis, stain normalization is an important procedure of color transfer between a source (reference) and the test image, that helps in addressing an important concern of stain color variation. In this work, we hypothesize that if color-texture information is well captured with suitable features using data containing sufficient color variation, it may obviate the need for stain normalization. Considering that such an image analysis study is relatively less explored, some questions are yet unresolved such as (a) How can texture and color information be effectively extracted and used for classification so as to reduce the burden on the uniform staining or stain normalization. (b) Are there good feature-classifier combinations which work consistently across all magnifications? (c) Can there be an automated way to select reference image for stain normalization? In this work, we attempt to address such questions. In the process, we compare the independent texture and color channel information with that of some more sophisticated features which consider jointly color-texture information. We have extracted above features using images with and without stain normalization to validate the above hypothesis. Moreover, we also compare different types of contemporary classification in conjunction with the above features. Based on the results of our exhaustive experimentation we provide some useful indications.

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Gupta V, Singh A, Sharma K, Bhavsar A. Automated Classification for Breast Cancer Histopathology Images: Is Stain Normalization Important? In Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures - 4th International Workshop, CARE 2017 and 6th International Workshop, CLIP 2017 Held in Conjunction with MICCAI 2017, Proceedings. Springer Verlag. 2017. p. 160-169. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-67543-5_16