Automated Peripheral Sensory Neuropathy Assessment of Diabetic Patients Using Optical Imaging and Binary Processing Techniques

A large proportion of individuals who live with type2 diabetes suffer from plantar sensory neuropathy (PSN). Regular testing and assessment for the condition is required to avoid ulceration or other damage to patients’ feet. Currently accepted practice involves a trained podiatrist testing patients’ feet manually with a hand-held nylon monofilament probe. The procedure is time consuming and labour intensive, requires training, is susceptible to error and is difficult to repeat.
This thesis presents the first investigation into a novel automated approach to automatically identify the pressure points on a given patient’s foot for the examination of sensory neuropathy via optical image processing via RGB and HSV colour space incorporating plantar anthropometry. The developed system effectively automates the traditional Semmes–Weinstein monofilament examination (SWME).
Further work presented demonstrates the development and demonstration of a generic automated lesion detection algorithms to recognise and avoid probe application on a plantar surface. A combination of local binary pattern and support vector machine methods in layered combination are used to avoid probe application where lesion and chosen pressure points overlap. The trained lesion detection and avoidance method was 100% effective on the lesions used.