Genetic algorithm-based initial contour optimization for skin lesion border detection
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Genetic algorithm-based initial contour optimization for skin lesion border detection Amira S. Ashour 1 & Reham Mohamed Nagieb 1 & Heba A. El-Khobby 1 & Mustafa M. Abd Elnaby 1 & Nilanjan Dey 2 Received: 28 May 2020 / Revised: 24 August 2020 / Accepted: 2 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Skin cancer . Dermoscopic images . Segmentation . Active contour . Genetic algorithm
1 Introduction Image segmentation segregates the pixels of interest for segmenting the ROI for further processes and analysis [9]. It provides information about the associated pixels of the ROI in an image.
* Amira S. Ashour [email protected]; amira.salah@f–eng.tanta.edu.eg
1
Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University Tanta, Tanta, Egypt
2
Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India
Multimedia Tools and Applications
Segmentation techniques can be classified based on the used dominant features: i) region-based segmentation, and ii) edge-based segmentation. Other categorization of image segmentation techniques is based on the similarity and discontinuity of the intensity levels includes active contours, region merging, region growing, thresholding, region splitting, and watershed segmentation. The active contour (AC) is deployed to separate the foreground from the background by defining models of AC from the ROI. However, during the segmentation process, various parameters are changeable in consistent with the applications leading to the necessity for using optimization techniques, such as particle swarm optimization (PSO) [25], genetic algorithm (GA) [32], firefly algorithm (FA) [29], and Bat algorithm (B
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