@inproceedings{8b9887b95c4747f9a74c4b293d67c7be,
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 (US)",
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",
}