Image segmentation based on genetic algorithm for region growth and region merging

Abstract

Medical image segmentation is the most important process to assist in the visualization of the structure of importance in medical images. Malignant melanoma is the most frequent type of skin cancer but it is treatable, if diagnosed at an early stage. Dermoscopy is a non-invasive, diagnostic tool having great possibility in the early diagnosis of malignant melanoma, but their interpretation is time consuming. In this work, a new image segmentation algorithm, for the early diagnosis of the skin cancer, is proposed where the dermoscopic images are segmented using a threshold. This threshold selection is based on the Genetic Algorithm (GA) for region growth, followed by region merging procedure. The obtained segmented image is then compared with the ground truth image using various parameters such as False Positive Error (FPE), False Negative Error (FNE) Coefficient of similarity, spatial overlap. Genetic algorithm is a class of probabilistic optimization algorithms, powerful in finding optimal feature vectors. Identification of approximate global optimal region in Genetic Algorithm is a quick process. To merge regions with similar characteristics, we have used grey level and texture. The segmentation done through Genetic Algorithm is efficient when compared to the image segmentation done by conventional algorithms.