Segmentation of Melanoma and Other Pigmented Skin Lesions in Dermoscopic Images Using Fusion of Threshoding Methods based on Reinforcement Algorithm

Dermoscopy is one of the major imaging techniques used in diagnoses of Melanoma and other skin diseases. Because of difficulties and subjectivity of human interpretation, automatic and computerized analysis of dermoscopic images has opened an important research area. Automatic lesion detection is one of the main steps in analysis of these images. Finding an optimal threshold for segmenting the lesion is a severe task in image processing. Different methods for thresholding already exist. In this research a novel thresholding approach according to well-known thresholding methods and reinforcement algorithm for segmenting dermoscopic images is presented. The reinforced agent learns optimal weights for different thresholding methods and finally segments the dermoscopic image with optimal threshold. A reward function is designed for achieving the similarity ratio between the binary output image and original gray level image and calculating reward/punish signal which should be exerted to reinforced agent. We use three thresholding methods, Otsu, Kittler and Kapur, for combining in the reinforced agent and the detected lesions are compared with the ground-truth which is determined dermatologists and the border error is calculated. The results are also compared with other well-known automatic methods which indicate that the proposed method yields to more accuracy and less border error in detection of lesion in dermocopy images