Weighted fuzzy clustering approach with adaptive spatial information and Kullback-Leibler divergence for skin lesion segmentation

Segmentation of dermoscopic images is an important phase of computer-aided diagnosis and classification of skin cancers. The varied shapes, sizes, and colors of the lesion, differences in the intensity of pixels within a lesion, presence of hair, and fuzziness at the lesion boundary make precise segmentation more difficult. To overcome all these problems, we propose a novel weighted fuzzy-based clustering method named WFC_AS_KL , which involves three terms in its objective function. The first term is a weighted combination of Fuzzy C-Mean and Fuzzy k-Plane Clustering, which is suitable to handle asymmetrical data distribution and changes in the color of dermoscopic images. To mitigate the effect of noise, weighted region-level and local-level spatial information is used as the second term. The third term is Kullback-Leibler information, which reduces the fuzziness of the fuzzy partition matrix. It also handles the cluster-center overlapping problem and unbalanced cluster size problem. An adaptive constraint is also used to control the original and spatial information of the image. We demonstrate the superior performance of the proposed WFC_AS_KL method on two publicly available medical image datasets against 13 fuzzy-based clustering methods in terms of sensitivity, specificity, precision, false positive rate, accuracy, dice similarity coefficient, and jaccard index. We also compared the performance of the proposed method against twelve traditional skin lesion segmentation methods and twelve existing deep learning methods. We also performed a non-parametric statistical test to demonstrate the statistically superior performance of the proposed WFC_AS_KL method in comparison to 13 fuzzy-based segmentation methods.