Automated skin lesion segmentation using K-Means clustering from digital dermoscopic images

Melanoma can prove fatal if not diagnosed at early stage. The accuracy of identification of skin cancer from dermoscopic images is directly proportional to the accuracy of the skin lesion segmentation. This work proposes a skin lesion segmentation method using clustering technique. The use of smoothing filter and area thresholding is competent enough to sufficiently reject the noisy pixels from the finally segmented image. The results of skin lesion segmentation obtained from the proposed algorithm has been compared with the annotated images. The results have been expressed in the form of overlapping score and correlation coefficient. The maximum values of overlapping score and correlation coefficient obtained from the algorithm are 96.75% and 97.66% respectively. The results are convincing and suggests that the proposed work can be used for some real time application.