Color channel based segmentation of skin lesion from clinical images for the detection of melanoma

Lesion localization is the first and most important step in the development of a melanoma detection system. Skin lesions show distinct color variations as they evolve from benign to malignant one. The use of different color spaces can provide the best discriminative information of lesions embedded in a particular color channel and used in efficient segmentation of a digital image. A simple and efficient method for the automatic segmentation of clinical images using colorspace analysis and improved binary thresholding algorithm is proposed. Clinical images taken from normal mobile cameras have the inherent problem of improperly illumined background compared to dermoscopic images. The proposed algorithm corrects these constraints with initial pre-processing steps of adjustments of clinical image. The algorithm has been evaluated using 175 images from different online public databases. The efficiency of the overall segmentation process is carried out by calculation of similarity matrices for segmented lesions from each color channel used. The framework is able to differentiate and extract the cancerous lesion from the background skin with around 94% accuracy with the preferred choice of color channel.