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.
Similar content being viewed by others
References
Aljanabi M, Özok Y, Rahebi J, Abdullah A (2018) Skin lesion segmentation method for dermoscopy images using artificial bee colony algorithm. Symmetry 10:347
Ashour AS, Hawas AR, Guo Y, Wahba MA (2018) a novel optimized neutrosophic k-means using genetic algorithm for skin lesion detection in dermoscopy images signal. Image Video Process 12:1311–1318
Beevi S, Nair MS, Bindu G (2016) Automatic segmentation of cell nuclei using krill herd optimization based multi-thresholding and localized active contour model. Biocybernetics Biomed Eng 36:584–596
Chabrier S, Rosenberger C, Emile B, Laurent H (2008) Optimization-based image segmentation by genetic algorithms. EURASIP J Image Video Process 2008:842029
Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10:266–277
Chan TF, Sandberg BY, Vese LA (2000) Active contours without edges for vector-valued images. J Vis Commun Image Represent 11:130–141
Damian FA, Moldovanu S, Dey N, Ashour AS, Moraru L (2020) Feature selection of non-dermoscopic skin lesion images for nevus and melanoma classification. Computation 8(2):41. https://doi.org/10.3390/computation8020041
Ding K, Xiao L, Weng G (2017) Active contours driven by region-scalable fitting and optimized Laplacian of Gaussian energy for image segmentation. Signal Process 134:224–233
Elayaraja P, Suganthi M (2014) Survey on medical image segmentation algorithms. Int J Adv Res Comput Commun Eng, vol 3
Getreuer P (2012) Chan-vese segmentation. Image Process Line 2:214–224
Ghosh P, Mitchell M (2006) Segmentation of medical images using a genetic algorithm. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp 1171–1178
Hemalatha S, Anouncia SM (2017) Unsupervised segmentation of remote sensing images using FD based texture analysis model and ISODATA. Int J Ambient Comput Intell 8:58–75
Isah RO, Usman AD, Tekanyi A (2017) Medical image segmentation through bat-active contour algorithm. Int J Intell Syst Appl 9:30–36
Khan MA, Khan MA, Ahmed F, Mittal M, Goyal LM, Hemanth DJ et al (2020a) Gastrointestinal diseases segmentation and classification based on duo-deep architectures. Pattern Recogn Lett 131:193–204
Khan MA, Sharif M, Akram T, Bukhari SAC, Nayak RS (2020b) Developed Newton-Raphson based deep features selection framework for skin lesion recognition. Pattern Recogn Lett 129:293–303
Kussener F 2011 Active contour: a parallel genetic algorithm approach. In:Proceedings of international conference on swarm intelligence (ICSI 2011)
Majid A, Khan MA, Yasmin M, Rehman A, Yousafzai A, Tariq U (2020) Classification of stomach infections: a paradigm of convolutional neural network along with classical features fusion and selection. Microsc Res Tech 83:562–576
Mandal D, Chatterjee A, Maitra M (2014) Robust medical image segmentation using particle swarm optimization aided level set based global fitting energy active contour approach. Eng Appl Artif Intell 35:199–214
Nagieb RM, Ashour AS, Guo Y, El-Khobby HA, Abd Elnaby MM (2018) Initialization of Active Contour for Dermoscopic Image Segmentation: A Comparative Study. In: 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp 370–374
Oliveira RB, Mercedes Filho E, Ma Z, Papa JP, Pereira AS, Tavares JMR (2016) Computational methods for the image segmentation of pigmented skin lesions: a review. Comput Methods Prog Biomed 131:127–141
Ramlau R, Ring W (2007) A Mumford–Shah level-set approach for the inversion and segmentation of X-ray tomography data. J Comput Phys 221:539–557
Rehman A, Khan MA, Mehmood Z, Saba T, Sardaraz M, Rashid M (2020) Microscopic melanoma detection and classification: a framework of pixel-based fusion and multilevel features reduction. Microsc Res Tech 83:410–423
Rousselle J-J, Vincent N, Verbeke N (2003) Genetic algorithm to set active contour In: Int Conference Comput Anal Images Patterns, pp. 345–352.
Saba T, Khan MA, Rehman A, Marie-Sainte SL (2019) Region extraction and classification of skin cancer: a heterogeneous framework of deep CNN features fusion and reduction. J Med Syst 43:289
Selvi V, Umarani R (2010) Comparative analysis of ant colony and particle swarm optimization techniques. Int J Comput Appl 5:1–6
Shahamatnia E, Ebadzadeh MM (2011) Application of particle swarm optimization and snake model hybrid on medical imaging In: 2011 IEEE Third International Workshop on Computational Intelligence in Medical Imaging, pp 1–8
Sudha MR, Sriraghav K, Jacob SG, Manisha S (2017) Approaches and applications of virtual reality and gesture recognition: a review. Int J Ambient Comput Intell 8:1–18
Wang XN, Feng YJ, Feng Z (2005) Ant colony optimization for image segmentation. Int Conference Machine Learn Cybernetics 9:5355–5360 IEEE
Yang X-S (2009) Firefly algorithms for multimodal optimization," in International symposium on stochastic algorithms, pp 169–178
Yang X-S, Hossein Gandomi A (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29:464–483
Yang X, Jiang X (2020) A hybrid active contour model based on new edge-stop functions for image segmentation. Int J Ambient Comput Intell 11:87–98
Zhang M (2016) Snake model based on improved genetic algorithm in fingerprint image segmentation. Int J Bioautomation 20:431–440
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ashour, A.S., Nagieb, R.M., El-Khobby, H.A. et al. Genetic algorithm-based initial contour optimization for skin lesion border detection. Multimed Tools Appl 80, 2583–2597 (2021). https://doi.org/10.1007/s11042-020-09792-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-09792-8