Detection and Classification of Cancer from Microscopic Biopsy Images Using Clinically Significant and Biologically Interpretable FeaturesReportar como inadecuado

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Journal of Medical Engineering - Volume 2015 2015, Article ID 457906, 14 pages -

Research ArticleDepartment of Computer Science and Engineering, Indian Institute of Technology Banaras Hindu University, Varanasi 221005, India

Received 11 May 2015; Revised 3 July 2015; Accepted 12 July 2015

Academic Editor: Ying Zhuge

Copyright © 2015 Rajesh Kumar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A framework for automated detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features is proposed and examined. The various stages involved in the proposed methodology include enhancement of microscopic images, segmentation of background cells, features extraction, and finally the classification. An appropriate and efficient method is employed in each of the design steps of the proposed framework after making a comparative analysis of commonly used method in each category. For highlighting the details of the tissue and structures, the contrast limited adaptive histogram equalization approach is used. For the segmentation of background cells -means segmentation algorithm is used because it performs better in comparison to other commonly used segmentation methods. In feature extraction phase, it is proposed to extract various biologically interpretable and clinically significant shapes as well as morphology based features from the segmented images. These include gray level texture features, color based features, color gray level texture features, Law’s Texture Energy based features, Tamura’s features, and wavelet features. Finally, the -nearest neighborhood method is used for classification of images into normal and cancerous categories because it is performing better in comparison to other commonly used methods for this application. The performance of the proposed framework is evaluated using well-known parameters for four fundamental tissues connective, epithelial, muscular, and nervous of randomly selected 1000 microscopic biopsy images.

Autor: Rajesh Kumar, Rajeev Srivastava, and Subodh Srivastava



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