A generalized fusion approach for segmenting dermoscopy images using Markov random field

Malignant melanoma is among the most rapidly increasing cancers in the world. Image border detection is often the first step to characterize skin lesion for the follow-up computer-aided diagnosis. Existing approaches lack robustness in the face of dermoscopy images varying in size, color, texture, and structure. In this paper, a generalized Markov random field (MRF) framework is proposed to fuse the results obtained from segmentation algorithms, by taking full advantages of characteristics of different methods and making them work synergistically to acquire more reliable results. The experimental results on the real dermoscopy image set demonstrate that the proposed fusion method is capable of improving the overall performance in terms of both accuracy and robustness.