Enhancement in the Identification of Slough Tissue in Chronic Wound Assessment

The prevalence of chronic ulcer wound is steadily increasing not only in predominantly rich nations but most markedly, in the world's middle-income countries. In addition to the dire consequences on the health and well-being of a patient, diabetes and its complications impact harshly on the finances of individuals and their families, and the economies of nations. The current diagnosis methods utilized by the diagnosticians are expert-oriented, vision-dependant, time-consuming, have interob-server variations and cause discomfort to the patient. Therefore, in an effort to improve capacity for diagnosis, a fully-automated wound tissue characterization system has been offered that would analyze the digital images of chronic wounds to identify the tissue types namely, granulation, slough, necrosis, and epithelial. In our previous research, the three tissue types (granulation, necrosis, and epithelial) were identified with higher accuracy in 301 images. In this paper, the slough identification has been enhanced by adding reference points and contrast enhancement to evaluate which method demonstrates better experimental results. Quantitative analysis of the results proves that preprocessing the images with Adaptable Histogram Equalization technique achieved the highest accuracy of 94.0% for the slough tissue.