Wound Image Analysis Using Contour Evolution

The aim of the algorithm described in this paper is to segment wound images from the normal and classify them according to the types of the wound. The segmentation of wounds extravagates color representation, which has been followed by an algorithm of grayscale segmentation based on the stack mathematical approach. Accurate classification of wounds and analyzing wound healing process is a critical task for patient care and health cost reduction at hospital. The tissue uniformity and flatness leads to a simplified approach but requires multispectral imaging for enhanced wound delineation. Contour Evolution method which uses multispectral imaging replaces more complex tools such as, SVM supervised classification, as no training step is required. In Contour Evolution, classification can be done by clustering color information, with differential quantization algorithm, the color centroids of small squares taken from segmented part of the wound image in (C1,C2) plane. Where C1, C2 are two chrominance components. Wound healing is identified by measuring the size of the wound through various means like contact and noncontact methods of wound. The wound tissues proportion is also estimated by a qualitative visual assessment based on the red-yellow-black code. Moreover, involving all the spectral response of the tissue and not only RGB components provides a higher discrimination for separating healed epithelial tissue from granulation tissue.