Wound Assessment in Pedobarography Using Image Segmentation Techniques

Abstract

Pedobarography is elementary for kinetic gait analysis along with the analysis and exploration of multiple neurological and musculoskeletal diseases. One person among 11 adults suffer from Diabetes Mellitus. Also, Foot ulcers (FU) is a most harmful as well as associated chronic complications springing from diabetes mellitus (DM). Recently, there has been an evolving awareness that understanding the biomechanical factors beneath the diabetic ulcer in a better manner could result in improving the control activities over the disease, with considerable socio-economic effects. Diabetic Foot Ulcers (DFU) is a primary concern of this health issue, and if this is not addressed right can result in amputation. So in this research, the Image segmentation algorithms and Perimeter pixel comparison is carried out for wound classification depending on the simulation algorithm like Adaptive K-means, Clustering K means, Fuzzy C means, and Region growing approaches and among them, Fuzzy C means is found to achieve greatest accuracy of perimeter pixel values, which are 603, 462 and 356 pixel values in stages one, two and three. The time taken for execution among all the four simulation algorithms are observed and it can be revealed that Adaptive K means yields the least execution time for carrying out the simulation of foot ulcer. An evaluation on the self-assessment of wounds caused during diabetic foot ulcer employing image segmentation is developed. It is ultimately found that the objective of the image analysis pertaining to the ulcer in foot is the dynamic evaluation and definition of regions of high pressure in a diabetic patient’s foot depending on the estimations made on the perimeter pixel comparison and execution time.