A hybrid dermoscopy images segmentation approach based on neutrosophic clustering and histogram estimation

Abstract

In this work, a novel skin lesion detection approach, called HBCENCM, is proposed using histogram-based clustering estimation (HBCE) algorithm to determine the required number of clusters in the neutrosophic c-means clustering (NCM) method. Initially, the dermoscopic images are mapped into the neutrosophic domain over three memberships, namely true, indeterminate, and false subsets. Then, an NCM algorithm is employed to group the pixels in the dermoscopy images, where the number of clusters in the dermoscopy images is determined using the HBCE algorithm. Lastly, the skin lesion is detected based on its intensity and morphological features. The public dataset (ISIC 2016) of 900 images for training and 379 images for testing are used in the present work. A comparative study of the original NCM clustering method is conducted on the same dataset. The results showed the superiority of the proposed approach to detect the lesion with 96.3% average accuracy compared to the average accuracy of 94.6% using the original NCM without HBCE algorithm.