2019
DOI: 10.1111/srt.12744
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Improved skin lesion edge detection method using Ant Colony Optimization

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 qualit… Show more

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Cited by 19 publications
(12 citation statements)
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References 22 publications
(21 reference statements)
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“…As an image processing technology, edge detection is a hotspot, and it needs to be optimized in response to the complexity of medical images and the increasing amount of information contained [ 11 , 12 ]. Compared with the classic edge detection algorithm, the improved Prewitt algorithm proposed by Sengupta et al [ 13 ] greatly shortens the image processing time. On the basis of the gradient operator, Dash et al [ 14 ] used Gaussian filtering to smooth the image, and then, the Laplacian gradient edge detector was used to detect the edge of the image.…”
Section: Introductionmentioning
confidence: 99%
“…As an image processing technology, edge detection is a hotspot, and it needs to be optimized in response to the complexity of medical images and the increasing amount of information contained [ 11 , 12 ]. Compared with the classic edge detection algorithm, the improved Prewitt algorithm proposed by Sengupta et al [ 13 ] greatly shortens the image processing time. On the basis of the gradient operator, Dash et al [ 14 ] used Gaussian filtering to smooth the image, and then, the Laplacian gradient edge detector was used to detect the edge of the image.…”
Section: Introductionmentioning
confidence: 99%
“…Various segmentation results were presented to assess the efficiency and accuracy of the proposed method. In addition, the results are compared vs. existing methods including Prewitt, Canny, Sobel, CDEDA Method [9], CSEDM Method [10] and QFEDM Method [11]. The experiments are carried out on the MATLAB software 10.…”
Section: Resultsmentioning
confidence: 99%
“…Several image segmentation techniques, such as discontinuity, divide the image based on isolated points detection and abrupt change in grayscale [10,11]. Another technique is edge detection, a fundamental model that delivers information about the boundaries of different objects within the image [11]. Hence, selecting the proper technique is essential to reach the best segmentation results [12,13].…”
Section: Introductionmentioning
confidence: 99%
“…Feature selections are made by using a genetic algorithm and classify the lesion by using SVM. Sengupta, Mittal, and Modi () presented an improved lesion segmentation method. First, a three‐stage preprocessing approach, which comprised contrast enhancement, color space conversion, and filtering, is applied.…”
Section: Related Workmentioning
confidence: 99%