Artifact Removal from Skin Dermoscopy Imagesto Support Automated Melanoma Diagnosis

Mahmood, Dr.Ahlam Fadhil; Mahmood, Hamed Abdulaziz

doi:10.33899/rengj.2015.108992

Cited by 2 publications

(1 citation statement)

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“…Moreover, for edge sharpening, they have utilized a warping algorithm to move pixels from the neighborhood of the blurred edge closer to the edge while the overall luminosity and texture patterns of the skin lesions are preserved. In [15], two fundamental steps are proposed to automatically detect and remove hairs from dermoscopy images: generation of a binary image mask by isolating hairs and ruler marking. From the RGB dermoscopy image, a red channel is utilized to perform noise removal followed by a generation of the binary image mask via an adaptive canny edge detector.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Block-Based Algorithm for Hair Removal in Dermoscopic Images

Zaqout¹

2020

Computer Methods and Programs in Biomedical Signal and Image Processing

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Hair occlusion in dermoscopy images affects the diagnostic operation of the skin lesion. Segmentation and classification of skin lesions are two major steps of the diagnostic operation required by Dermatologists. We propose a new algorithm for hair removal in dermoscopy images that includes two main stages: hair detection and inpainting. In hair detection, a morphological bottom-hat operation is implemented on Y-channel image of YIQ color space followed by a binarization operation. In inpainting, the repaired Y-channel is partitioned into 256 nonoverlapped blocks and for each block, white pixels are replaced by locating the highest peak of using a histogram function and a morphological close operation. Our proposed algorithm reports a true positive rate (sensitivity) of 97.36%, a false positive rate (fall-out) of 4.25%, and a true negative rate (specificity) of 95.75%. The diagnostic accuracy achieved is recorded at a high level of 95.78%.

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Section: Related Workmentioning

confidence: 99%