50 citations
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December 2011 in “Skin Research and Technology” In this study, the researchers reported that their novel algorithm for hair restoration in dermoscopy images achieved high accuracy and texture preservation, outperforming other techniques in diagnostic accuracy and texture quality measures.
2 citations
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November 2018 in “Modern Applied Science” This study proposed a method to automatically detect and replace hair in dermoscopy images, demonstrating high sensitivity and specificity in melanoma diagnostics.
This study presents a new approach to automatically remove hair artifacts from dermoscopic images, which reportedly performed well compared to existing methods like DullRazor using the PH2 datasets.
6 citations
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July 2022 in “Biomedical Signal Processing and Control” This study presents a new hair removal algorithm for dermatoscopic images of skin lesions that improves hair detection accuracy by 2–7% and hair repair accuracy by 2–5% on average, using advanced techniques like maximum variance fuzzy clustering, Criminisi priorities, and the ant colony algorithm.
1 citations
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January 2021 in “SISTEMASI” This study found that the multi-thresholding method was the most effective for segmenting hair during laser removal, as it clearly distinguished hair patterns with minimal noise.
