Assessment of Segmentation Techniques for Chronic Wound Surface Area Detection

A skin ulcer is a clinical pathology of localized damage to skin and tissue instigated by venous insufficiency. Precise identification of wound surface area is one of the challenging tasks in the dermatological evaluation. The assessment is carried out by clinicians using traditional approach of scales or metrics through visual inspection. The manual assessment leads to intra-observer variability, subjective error and time complexity. This paper evaluates the performances of supervised and unsupervised segmentation techniques used for wound area detection. The unsupervised methods used for evaluation were namely K-means, Fuzzy C-means and Gaussian mixture model. On the other part, random forest was implemented for supervised classification. Several filtering methods were used to generate image feature set from wound images to train random forest. The Gaussian mixture model with classification expectation–maximization clustering method achieved the highest weighted sensitivity of 95.91% and weighted specificity of 96.7%. The comparative study shows the superiority of proposed method and its suitability in wound segmentation from normal skin.