From Event: SPIE Optical Engineering + Applications, 2016
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 , 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 . 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.
Damayanti Chakraborti, Ravneet Kaur, Scott Umbaugh, and Robert LeAnder, "An effective hair detection algorithm for dermoscopic melanoma images of skin lesions," Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 997104 (Presented at SPIE Optical Engineering + Applications: August 29, 2016; Published: 27 September 2016); https://doi.org/10.1117/12.2236565.
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