Skin Lesion Border Detection Based on Best Statistical Model Using Optimal Colour Channel

This paper proposes an effective way to segment melanoma skin lesion in colour dermoscopic images, using an edge-based approach. The proposed method, different methods were combined to improve the segmentation performance. These methods are morphological operations, bilateral filter, spline, polynomial model and canny edge detector. Different methods were tested to select the best method that was produced the best outcome. These testing methods, bilateral filter provided the highest PSNR amongst other filters such as median filter, Gaussian and average filter. Two statistical models were implemented polynomial model and linear regression and selected the best performance as polynomial model. Four edge detectors were applied to detect the edge of skin lesion and select the best segmentation accuracy. Manual border selection was used as the benchmark to evaluation the accuracy of the automatic border. The proposed method was able to achieve a good average accuracy of 96.69% based on canny edge detector. Our dataset consists of (70) dermoscopic images that includes melanoma and nevus.