BACKGROUND/ PURPOSE: Dermoscopy is one of the major imaging modalities used in the diagnosis of pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, computerized image analysis techniques have become important tools in this research area. Hair removal from skin lesion images is one of the key problems for the precise segmentation and analysis of the skin lesions. In this study, we present a new scheme that automatically detects and removes hairs from dermoscopy images. METHODS: The proposed algorithm includes two steps: firstly, light and dark hairs and ruler marking are segmented through adaptive canny edge detector and refinement by morphological operators. Secondly, the hairs are repaired based on multi-resolution coherence transport inpainting. RESULTS: The algorithm was applied to 50 dermoscopy images. To estimate the accuracy of the proposed hair detection algorithm, quantitative analysis was performed using TDR, FPR, and DA metrics. Moreover, to evaluate the performance of the proposed hair repaired algorithm, three statistical metrics namely entropy, standard deviation, and co-occurrence matrix were used. CONCLUSION: The results demonstrate that the proposed algorithm is highly accurate and able to detect and repair the hair pixels with few errors. In addition, the segmentation veracity of the skin lesion is effectively improved after our proposed hair removal algorithm.
BACKGROUND/ PURPOSE: Dermoscopy is one of the major imaging modalities used in the diagnosis of pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, computerized image analysis techniques have become important tools in this research area. Hair removal from skin lesion images is one of the key problems for the precise segmentation and analysis of the skin lesions. In this study, we present a new scheme that automatically detects and removes hairs from dermoscopy images. METHODS: The proposed algorithm includes two steps: firstly, light and dark hairs and ruler marking are segmented through adaptive canny edge detector and refinement by morphological operators. Secondly, the hairs are repaired based on multi-resolution coherence transport inpainting. RESULTS: The algorithm was applied to 50 dermoscopy images. To estimate the accuracy of the proposed hair detection algorithm, quantitative analysis was performed using TDR, FPR, and DA metrics. Moreover, to evaluate the performance of the proposed hair repaired algorithm, three statistical metrics namely entropy, standard deviation, and co-occurrence matrix were used. CONCLUSION: The results demonstrate that the proposed algorithm is highly accurate and able to detect and repair the hair pixels with few errors. In addition, the segmentation veracity of the skin lesion is effectively improved after our proposed hair removal algorithm.