Automated tissue classification framework for reproducible chronic wound assessment.

Rashmi Mukherjee,Chandan Chakraborty,Dev Kumar Das,Arun Achar,Analava Mitra,Dhiraj Dhane Manohar

doi:10.1155/2014/851582

Abstract

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 (RGB) wound images grabbed by normal digital camera were first transformed into HSI (hue, saturation, and intensity) color space and subsequently the “S” component of HSI 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).

Highlights

Chronic wounds (CWs) are a major threat to the public health and economy since they have detrimental effect on patient’s quality of life causing depression, social separation, and high costs for treatment
Wound tissues were classified into red granulation tissue, yellow slough tissue, and black necrotic tissue based on Bayesian and support vector machine (SVM) classifiers
The results showed SVM with 3rd degree polynomial provided highest classification accuracy for separating three types of wound tissue pixels based on color and texture features

Summary

Introduction

Chronic wounds (CWs) are a major threat to the public health and economy since they have detrimental effect on patient’s quality of life causing depression, social separation, and high costs for treatment. The healing process may be delayed if appropriate treatment is not provided based on accurate diagnosis. Venous ulcers, and pressure ulcers are considered as the 3 main types of CW, still, burn, malignant ulcer, and Pyoderma gangrenosum incidence rate is quite significant [2]. These CWs are often resistant to healing and require long term medical care

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Conclusion