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Improved skin lesion edge detection method using Ant Colony Optimization
Author(s) -
Sengupta Sudhriti,
Mittal Neetu,
Modi Megha
Publication year - 2019
Publication title -
skin research and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.521
H-Index - 69
eISSN - 1600-0846
pISSN - 0909-752X
DOI - 10.1111/srt.12744
Subject(s) - prewitt operator , artificial intelligence , sobel operator , preprocessor , ant colony optimization algorithms , edge detection , computer vision , computer science , canny edge detector , lesion , pattern recognition (psychology) , enhanced data rates for gsm evolution , deriche edge detector , image processing , mathematics , image (mathematics) , medicine , pathology
Abstract Background Skin lesion edge detection is a significant step in developing an automatized diagnostic system. The efficient diagnostic system leads to correct identification and detection of skin lesion diseases. In this paper, ant colony optimization (ACO) technique is used to improve the edge contour of skin lesion images. Material and Method Firstly, a three‐stage preprocessing methodology involving color space conversion, contrast enhancement, and filtering is applied to improve the skin lesion image quality. The edge map is obtained by applying three types of conventional edge detection methods namely Canny, Sobel, and Prewitt. Thereafter, ACO is applied on these images to produce an improved edge contour. Result The improvement of the proposed methodology is quantitatively verified by analysis of the entropy of the final image obtained by conventional and proposed techniques. Conclusion From the result analysis, we can conclude that introduction of ACO has increased the efficiency of the conventional edge detection method in skin lesion images.

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