Melanocytic and nevus lesion detection from diseased dermoscopic images using fuzzy and wavelet techniques

The use of computer-assisted decision system (CAD) for the diagnosis of skin cancer dermoscopy is aggravated by the potential gains of its excellent performance. It automates the skin lesion analysis and reduces the amount of repetitive and tedious tasks to be done by physicians. This research is mainly focused on the computer vision perspective to design a CAD system which will facilitate the physicians. An automated PR system includes four inter-related processes to analyze skin lesions by the clinicians: image preprocessing, segmentation, feature extraction, feature selection and classification. The dataset contains images and annotations provided by physicians. Segmentation is an imperative preprocessing step for CAD system of skin lesions. Feature extraction of segmented skin lesions is a pivotal step for implementing accurate decision support systems. Physicians are interested in examining a specific clinically significant region in a lesion. Such a region is expected to have more information in the form of texture that can be relevant for detection. In case of detection of melanoma, various local features, for example, pigment network and streaks, usually occur in peripheral region of the lesion. This led to the extraction of peripheral part for feature extraction instead of whole lesion processing. We propose novel techniques for lesion detection and classification on peripheral part of the lesion using m-mediod classifier along with the contrast of patterns. Classification results obtained from the proposed feature matrix were compared with some other texture descriptors, showing the superiority of our proposed descriptor.