Automated malignancy detection in breast histopathological images

Andrei Chekkoury; Parmeshwar Khurd; Jie Ni; Claus Bahlmann; Ali Kamen; Amar Patel; Leo Grady; Maneesh Singh; Martin Groher; Nassir Navab; Elizabeth Krupinski; Jeffrey Johnson; Anna Graham; Ronald Weinstein

doi:10.1117/12.911643

Automated malignancy detection in breast histopathological images

Andrei Chekkoury
, Parmeshwar Khurd
, Jie Ni
, Claus Bahlmann
, Ali Kamen
, Amar Patel
, Leo Grady
, Maneesh Singh
, Martin Groher
, Nassir Navab
, Elizabeth Krupinski
, Jeffrey Johnson
, Anna Graham
, Ronald Weinstein

Informatics 16 - Chair of Computer Aided Medical Procedures

Siemens AG
Technical University of Munich
University of Arizona

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

56 Scopus citations

Abstract

Detection of malignancy from histopathological images of breast cancer is a labor-intensive and error-prone process. To streamline this process, we present an efficient Computer Aided Diagnostic system that can differentiate between cancerous and non-cancerous H&E (hemotoxylin&eosin) biopsy samples. Our system uses novel textural, topological and morphometric features taking advantage of the special patterns of the nuclei cells in breast cancer histopathological images. We use a Support Vector Machine classifier on these features to diagnose malignancy. In conjunction with the maximum relevance-minimum redundancy feature selection technique, we obtain high sensitivity and specificity. We have also investigated the effect of image compression on classification performance.

Original language	English
Title of host publication	Medical Imaging 2012
Subtitle of host publication	Computer-Aided Diagnosis
DOIs	https://doi.org/10.1117/12.911643
State	Published - 2012
Event	Medical Imaging 2012: Computer-Aided Diagnosis - San Diego, CA, United States Duration: 7 Feb 2012 → 9 Feb 2012

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	8315
ISSN (Print)	1605-7422

Conference

Conference	Medical Imaging 2012: Computer-Aided Diagnosis
Country/Territory	United States
City	San Diego, CA
Period	7/02/12 → 9/02/12

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Breast cancer
Breast histopathology
CAD histology
Cancer in histopathology images
Detecting malignancy

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10.1117/12.911643

Cite this

Chekkoury, A., Khurd, P., Ni, J., Bahlmann, C., Kamen, A., Patel, A., Grady, L., Singh, M., Groher, M., Navab, N., Krupinski, E., Johnson, J., Graham, A., & Weinstein, R. (2012). Automated malignancy detection in breast histopathological images. In Medical Imaging 2012: Computer-Aided Diagnosis Article 831515 (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 8315). https://doi.org/10.1117/12.911643

@inproceedings{7b19bae0b8ea4fecbf412f10f8fa952e,

title = "Automated malignancy detection in breast histopathological images",

abstract = "Detection of malignancy from histopathological images of breast cancer is a labor-intensive and error-prone process. To streamline this process, we present an efficient Computer Aided Diagnostic system that can differentiate between cancerous and non-cancerous H\&E (hemotoxylin\&eosin) biopsy samples. Our system uses novel textural, topological and morphometric features taking advantage of the special patterns of the nuclei cells in breast cancer histopathological images. We use a Support Vector Machine classifier on these features to diagnose malignancy. In conjunction with the maximum relevance-minimum redundancy feature selection technique, we obtain high sensitivity and specificity. We have also investigated the effect of image compression on classification performance.",

keywords = "Breast cancer, Breast histopathology, CAD histology, Cancer in histopathology images, Detecting malignancy",

author = "Andrei Chekkoury and Parmeshwar Khurd and Jie Ni and Claus Bahlmann and Ali Kamen and Amar Patel and Leo Grady and Maneesh Singh and Martin Groher and Nassir Navab and Elizabeth Krupinski and Jeffrey Johnson and Anna Graham and Ronald Weinstein",

year = "2012",

doi = "10.1117/12.911643",

language = "English",

isbn = "9780819489647",

series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",

booktitle = "Medical Imaging 2012",

note = "Medical Imaging 2012: Computer-Aided Diagnosis ; Conference date: 07-02-2012 Through 09-02-2012",

}

Chekkoury, A, Khurd, P, Ni, J, Bahlmann, C, Kamen, A, Patel, A, Grady, L, Singh, M, Groher, M, Navab, N, Krupinski, E, Johnson, J, Graham, A & Weinstein, R 2012, Automated malignancy detection in breast histopathological images. in Medical Imaging 2012: Computer-Aided Diagnosis., 831515, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 8315, Medical Imaging 2012: Computer-Aided Diagnosis, San Diego, CA, United States, 7/02/12. https://doi.org/10.1117/12.911643

TY - GEN

T1 - Automated malignancy detection in breast histopathological images

AU - Chekkoury, Andrei

AU - Khurd, Parmeshwar

AU - Ni, Jie

AU - Bahlmann, Claus

AU - Kamen, Ali

AU - Patel, Amar

AU - Grady, Leo

AU - Singh, Maneesh

AU - Groher, Martin

AU - Navab, Nassir

AU - Krupinski, Elizabeth

AU - Johnson, Jeffrey

AU - Graham, Anna

AU - Weinstein, Ronald

PY - 2012

Y1 - 2012

N2 - Detection of malignancy from histopathological images of breast cancer is a labor-intensive and error-prone process. To streamline this process, we present an efficient Computer Aided Diagnostic system that can differentiate between cancerous and non-cancerous H&E (hemotoxylin&eosin) biopsy samples. Our system uses novel textural, topological and morphometric features taking advantage of the special patterns of the nuclei cells in breast cancer histopathological images. We use a Support Vector Machine classifier on these features to diagnose malignancy. In conjunction with the maximum relevance-minimum redundancy feature selection technique, we obtain high sensitivity and specificity. We have also investigated the effect of image compression on classification performance.

AB - Detection of malignancy from histopathological images of breast cancer is a labor-intensive and error-prone process. To streamline this process, we present an efficient Computer Aided Diagnostic system that can differentiate between cancerous and non-cancerous H&E (hemotoxylin&eosin) biopsy samples. Our system uses novel textural, topological and morphometric features taking advantage of the special patterns of the nuclei cells in breast cancer histopathological images. We use a Support Vector Machine classifier on these features to diagnose malignancy. In conjunction with the maximum relevance-minimum redundancy feature selection technique, we obtain high sensitivity and specificity. We have also investigated the effect of image compression on classification performance.

KW - Breast cancer

KW - Breast histopathology

KW - CAD histology

KW - Cancer in histopathology images

KW - Detecting malignancy

UR - https://www.scopus.com/pages/publications/84869987301

U2 - 10.1117/12.911643

DO - 10.1117/12.911643

M3 - Conference contribution

AN - SCOPUS:84869987301

SN - 9780819489647

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Medical Imaging 2012

T2 - Medical Imaging 2012: Computer-Aided Diagnosis

Y2 - 7 February 2012 through 9 February 2012

ER -

Automated malignancy detection in breast histopathological images

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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