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Segmentation of Dermoscopic Images by the Fusion of Type-2 Fuzziness Measure in Graph Cuts Image Binarization
This paper proposes a novel thresholding-based approach for the segmentation of pigmented skin lesion images regarding malignant melanoma diagnosis. This problem is challenging because of the high uncertainty and fuzziness encountered at the border between the lesion and the skin. The main contribution of this paper is the improvement of the segmentation accuracy of binarization algorithm that relies only on histogram information by integrating the spatial information. The proposed algorithm fuses Type-2 fuzziness measure in Graph Cuts (GC) energy minimization process, and by integrating the spatial information (pixel positions), embedded in GC energy minimization, the proposed method performs better than methods that relay only on the histogram information. Experimental results performed on dermoscopic images demonstrate the effectiveness of the algorithm in comparison with other algorithms.
Graph Cuts, Binarization, Historgam Thresholding, Type-2 Fuzzy Logic, Dermoscopics Images.
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