Wound Area Segmentation Using 4D Probability Map and Superpixel Region Growing Chronic wounds poses a significant health burden. Gradually itu0027s becoming a major threat for world health and economy. In developed countries, 1xe2x80x932% of all population is affected by chronic wounds in their lifetime and the duration for chronic wound healing varies from several months to several years. The change in wound area over time is a highly predictive clue for wound treatment. The current clinical techniques for wound area measurement is based on visual inspection, which is potentially inaccurate and represents a tedious clinical workload for the clinician. Hence, a computerized wound measurement system is more quantitative and costeffective that enables more accurate and effective wound management. In this paper, the proposed wound area segmentation method based on 4D Probability map and superpixel region growing shows promising performance in terms of accuracy, stability and computational complexity. The proposed method has 79.2% accuracy, an improvement over previous method.

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 worldu0027s middleincome countries. In addition to the dire consequences on the health and wellbeing 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 expertoriented, visiondependant, timeconsuming, have interobserver variations and cause discomfort to the patient. Therefore, in an effort to improve capacity for diagnosis, a fullyautomated 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.