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Symmetry 2018, 10(2), 51;

Social Group Optimization Supported Segmentation and Evaluation of Skin Melanoma Images

Department of Information Technology, Techno India College of Technology, Kolkata 700156, West Bengal, India
Department of Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering, Chennai 600119, Tamilnadu, India
Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta 31527, Egypt
Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Porto, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal
Author to whom correspondence should be addressed.
Received: 8 January 2018 / Revised: 14 February 2018 / Accepted: 15 February 2018 / Published: 22 February 2018
(This article belongs to the Special Issue Advances in Medical Image Segmentation)
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The segmentation of medical images by computational methods has been claimed by the medical community, which has promoted the development of several algorithms regarding different tissues, organs and imaging modalities. Nowadays, skin melanoma is one of the most common serious malignancies in the human community. Consequently, automated and robust approaches have become an emerging need for accurate and fast clinical detection and diagnosis of skin cancer. Digital dermatoscopy is a clinically accepted device to register and to investigate suspicious regions in the skin. During the skin melanoma examination, mining the suspicious regions from dermoscopy images is generally demanded in order to make a clear diagnosis about skin diseases, mainly based on features of the region under analysis like border symmetry and regularity. Predominantly, the successful estimation of the skin cancer depends on the used computational techniques of image segmentation and analysis. In the current work, a social group optimization (SGO) supported automated tool was developed to examine skin melanoma in dermoscopy images. The proposed tool has two main steps, mainly the image pre-processing step using the Otsu/Kapur based thresholding technique and the image post-processing step using the level set/active contour based segmentation technique. The experimental work was conducted using three well-known dermoscopy image datasets. Similarity metrics were used to evaluate the clinical significance of the proposed tool such as Jaccard’s coefficient, Dice’s coefficient, false positive/negative rate, accuracy, sensitivity and specificity. The experimental findings suggest that the proposed tool achieved superior performance relatively to the ground truth images provided by a skin cancer physician. Generally, the proposed SGO based Kapur’s thresholding technique combined with the level set based segmentation technique is very effective for identifying melanoma dermoscopy digital images with high sensitivity, specificity and accuracy. View Full-Text
Keywords: skin melanoma; social group optimization (SGO); Otsu; Kapur; level set; active contour skin melanoma; social group optimization (SGO); Otsu; Kapur; level set; active contour

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Dey, N.; Rajinikanth, V.; Ashour, A.S.; Tavares, J.M.R.S. Social Group Optimization Supported Segmentation and Evaluation of Skin Melanoma Images. Symmetry 2018, 10, 51.

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