Wound Image Analysis Classifier for Efficient Tracking of Wound Healing Status

Abstract

Wounds are evolved by increase in number of damage tissues. The traditional way of assessing the wound healing status is to periodic measure of the area covered by the wound. This technique is tedious to measure and periodic assessment is cumbersome. Basically healing status of the wound can be classified as contact methods and non contact methods. The purpose of this research work is to accurately assess the healing status of the wound .To accurately assess the wound, capturing of the wound images are the first task to be performed. There are various tools like the photographic wound assessment tool (PWAT) to acquire efficient wound images. Since the characteristics of different types of wounds (venous, pressure, diabetic, and arterial ulcers) vary markedly, determining the reliability and validity of using the PWAT to assess wound appearance for both chronic pressure ulcers and leg ulcers due to vascular insufficiency is important. Segmenting the area of the wound from the wound image using efficient segmentation techniques and preprocessing the segmented wound to reduce the noise using efficient filters and efficient denoising techniques. Efficient classifiers are needed to classify the wound images. One among the classifiers are the Wound Image Analysis Classifier (WIAC). Experimental evaluation has been made on comparing various classifiers like SVM, KNN, WIAC.