Genetic algorithm-based initial contour optimization for skin lesion border detection

Abstract

Automated segmentation has an essential role in detecting several diseases, such as skin lesions. In segmentation, the active contour (AC) is an efficient method based on energy forces and constraints in an image to separate the region of interest (ROI) by defining a curvature or contour. It outlines an initial contour to fit the ROI, which changed iteratively by minimizing the energy function. If the contour is improperly initialized, the AC may trap in local minima. In this work, the initial contour of the AC without edge ‘Chan-Vese’ model is optimized using the genetic algorithm (GA) to find the optimal initial circular area percentage of the skin lesion image from the whole image area. This optimal optimized value drives the AC and enhances the performance of the traditional AC while detecting the skin lesion boundaries. Various evaluation metrics were measured to compare the performance of the proposed optimized IAC (initial active contour), graph-cut, and the k-means, in dermoscopic image segmentation. The results show the dominance of the proposed method indicating that the optimal initial circular contour of 30.86% from the original image area. The results proved 96.2% detection accuracy best results achieved using this optimal value.