27 September 2016 An effective hair detection algorithm for dermoscopic melanoma images of skin lesions
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Abstract
Dermoscopic images are obtained using the method of skin surface microscopy. Pigmented skin lesions are evaluated in terms of texture features such as color and structure. Artifacts, such as hairs, bubbles, black frames, ruler-marks, etc., create obstacles that prevent accurate detection of skin lesions by both clinicians and computer-aided diagnosis. In this article, we propose a new algorithm for the automated detection of hairs, using an adaptive, Canny edge-detection method, followed by morphological filtering and an arithmetic addition operation. The algorithm was applied to 50 dermoscopic melanoma images. In order to ascertain this method’s relative detection accuracy, it was compared to the Razmjooy hair-detection method [1], using segmentation error (SE), true detection rate (TDR) and false positioning rate (FPR). The new method produced 6.57% SE, 96.28% TDR and 3.47% FPR, compared to 15.751% SE, 86.29% TDR and 11.74% FPR produced by the Razmjooy method [1]. Because of the 7.27-9.99% improvement in those parameters, we conclude that the new algorithm produces much better results for detecting thick, thin, dark and light hairs. The new method proposed here, shows an appreciable difference in the rate of detecting bubbles, as well.
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Damayanti Chakraborti, Damayanti Chakraborti, Ravneet Kaur, Ravneet Kaur, Scott Umbaugh, Scott Umbaugh, Robert LeAnder, Robert LeAnder, } "An effective hair detection algorithm for dermoscopic melanoma images of skin lesions", Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 997104 (27 September 2016); doi: 10.1117/12.2236565; https://doi.org/10.1117/12.2236565
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