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BioMed Research International
Volume 2014, Article ID 851582, 9 pages
Research Article

Automated Tissue Classification Framework for Reproducible Chronic Wound Assessment

1School of Medical Science & Technology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
2Department of Dermatology, Midnapore Medical College Hospital, Midnapore, West Bengal 721101, India

Received 24 February 2014; Revised 30 May 2014; Accepted 4 June 2014; Published 8 July 2014

Academic Editor: Stephen M. Cohn

Copyright © 2014 Rashmi Mukherjee 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.


The aim of this paper was to develop a computer assisted tissue classification (granulation, necrotic, and slough) scheme for chronic wound (CW) evaluation using medical image processing and statistical machine learning techniques. The red-green-blue () wound images grabbed by normal digital camera were first transformed into (hue, saturation, and intensity) color space and subsequently the “” component of color channels was selected as it provided higher contrast. Wound areas from 6 different types of CW were segmented from whole images using fuzzy divergence based thresholding by minimizing edge ambiguity. A set of color and textural features describing granulation, necrotic, and slough tissues in the segmented wound area were extracted using various mathematical techniques. Finally, statistical learning algorithms, namely, Bayesian classification and support vector machine (SVM), were trained and tested for wound tissue classification in different CW images. The performance of the wound area segmentation protocol was further validated by ground truth images labeled by clinical experts. It was observed that SVM with 3rd order polynomial kernel provided the highest accuracies, that is, 86.94%, 90.47%, and 75.53%, for classifying granulation, slough, and necrotic tissues, respectively. The proposed automated tissue classification technique achieved the highest overall accuracy, that is, 87.61%, with highest kappa statistic value (0.793).