Development of a fast and accurate method for the segmentation of diabetic foot ulcer images

Abstract

Globally, Diabetic Foot Ulcers (DFUs) are among the major sources of morbidity and death among people diagnosed with diabetes. Diabetic foot ulcers are the leading diabetes-related complications that result in non-traumatic lower-limb amputations among these patients. Being a serious health concern, DFUs present a significant therapeutic challenge to specialists, particularly in countries with limited health resources and where the vast majority of patients are admitted to healthcare facilities when the ulcers have fully advanced. Clinical practices currently employed to assess and treat DFU are mostly based on the vigilance of both the patient and clinician. These practices have been proved to experience major limitations which include less accurate assessment methods, time-consuming diagnostic procedures, and relatively high treatment costs. Digital image processing is thus a potential solution to address issues of the inaccuracy of visual assessment as well as minimizing consecutive patient visits to the clinics. Image processing techniques for ulcer assessment have thus been a center of study in various works of literature. In the available works of literature, these methods include measuring the ulcer area as well as using a medical digital photography scheme. The most notable drawbacks of such approaches include system complexity, complex-exhaustive training phases, and high computational cost. Inspired by the weaknesses of the existing techniques, this study proposes a segmentation method that incorporates a hybrid diffusion-steered functional derived from the Total variation and the Perona-Malik diffusivities, which have been reported that they can effectively capture semantic features in images. Empirical results from the experiments that were carried out in the MATLAB environment show that the proposed method generates clearer segmented outputs with higher perceptual and objective qualities. More importantly, the proposed method offers lower computational times—an advantage that gives more insights into the possible application of the method in time-sensitive tasks.