As Melanoma is the deadliest form of skin cancer. Incidence rates of melanoma have been increasing, especially among non-Hispanic white males and females, but survival rates are high if detected early. Due to the costs for dermatologists to screen every patient, there is a need for an automated system to assess a patient’s risk of melanoma using images of their skin lesions captured using a standard digital camera. One challenge in implementing such a system is locating the skin lesion in the digital image. Therefore, a novel technique for detecting skin lesion segmentation algorithm is proposed. A set of representative texture distributions and spatial features are learned from an illumination-corrected photograph and feature vector table is calculated for each object. Next, regions in the image are classified as normal skin or lesion based on the occurrence of representative texture distributions. The proposed segmentation framework is tested by comparing lesion segmentation results to results using other state-of-art algorithms. The proposed framework has higher segmentation accuracy compared to all other tested algorithms. It is one of the efficient technique to get infinite probabilities to get very close result in a short time.
Malignant Melanoma, Skin Lesion, TDLS, Sensitivity, Specificity and Accuracy