Evaluation of feature descriptors for cancerous tissue recognition

Panagiotis Stanitsas, Anoop Cherian, Xinyan Li, Alexander Truskinovsky, Vassilios Morellas, Nikolaos P Papanikolopoulos

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

7 Scopus citations


Computer-Aided Diagnosis (CAD) has witnessed a rapid growth over the past decade, providing a variety of automated tools for the analysis of medical images. In surgical pathology, such tools enhance the diagnosing capabilities of pathologists by allowing them to review and diagnose a larger number of cases daily. Geared towards developing such tools, the main goal of this paper is to identify useful computer vision based feature descriptors for recognizing cancerous tissues in histopathologic images. To this end, we use images of Hematoxylin & Eosin-stained microscopic sections of breast and prostate carcinomas, and myometrial leiomyosarcomas, and provide an exhaustive evaluation of several state of the art feature representations for this task. Among the various image descriptors that we chose to compare, including representations based on convolutional neural networks, Fisher vectors, and sparse codes, we found that working with covariance based descriptors shows superior performance on all three types of cancer considered. While covariance descriptors are known to be effective for texture recognition, it is the first time that they are demonstrated to be useful for the proposed task and evaluated against deep learning models. Capitalizing on Region Covariance Descriptors (RCDs), we derive a powerful image descriptor for cancerous tissue recognition termed, Covariance Kernel Descriptor (CKD), which consistently outperformed all the considered image representations. Our experiments show that using CKD lead to 92.83%, 91.51%, and 98.10% classification accuracy for the recognition of breast carcinomas, prostate carcinomas, and myometrial leiomyosarcomas, respectively.

Original languageEnglish (US)
Title of host publication2016 23rd International Conference on Pattern Recognition, ICPR 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781509048472
StatePublished - Jan 1 2016
Event23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico
Duration: Dec 4 2016Dec 8 2016

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Other23rd International Conference on Pattern Recognition, ICPR 2016

Bibliographical note

Funding Information:
This material is based upon work supported by the National Science Foundation through grants #IIP-0934327, #CNS-1039741, #SMA-1028076, #CNS-1338042, #IIP-1439728, #OISE-1551059, and #CNS-1514626. Dr. Cherian is funded by the Australian Research Council Centre of Excellence for Robotic Vision (project number CE140100016).

Publisher Copyright:
© 2016 IEEE.

Copyright 2019 Elsevier B.V., All rights reserved.

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