OCE-NGC: A neutrosophic graph cut algorithm using optimized clustering estimation algorithm for dermoscopic skin lesion segmentation

Abstract

Automated skin lesion segmentation is one of the most crucial stages in dermoscopic images based diagnosis. To guarantee efficient unsupervised clustering-based segmentation, a histogram-based clustering estimation (HBCE) algorithm can be used to obtain the initial number of clusters with their corresponding centroids. Accordingly, the present work introduced a novel skin lesion segmentation algorithm, called optimized clustering estimation for neutrosophic graph cut algorithm (OCE-NGC). Firstly, the genetic algorithm (GA) is used to optimize the HBCE procedure by finding its optimal threshold values which are functions of a factor, called β to be optimized. This optimization process guarantees the optimal determination of the initial number of clusters and their corresponding centroids for further use in the proposed clustering process. Thus, the skin lesion dermoscopic images are then mapped into the neutrosophic set (NS) domain which is computed by the neutrosophic c-means (NCM). The NCM groups the pixels in the dermoscopic images using the pre-determined optimal number of clusters obtained by the optimized HBCE. Finally, a cost function of the graph cut (GC) algorithm is defined in the NS domain for the segmentation process. The experimental results established the superiority of the proposed OCE-NGC approach in comparison with the traditional HBCE with NCM only, the traditional HBCE with the NGC, and the typical GC. In a public dataset, the proposed approach achieved 97.12% and 86.28% average accuracy and average Jaccard (JAC) values, respectively.