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

Abstract

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%.

Skin lesion segmentation using an improved framework of encoder‐decoder based convolutional neural network

“…Another type is Gaussian filters that performs subtraction operation on images to reduce noise and to preserve edge information. 20 Finally, filtering operators as given in 21,22 proposed tophat and bottom-hat operators to detect hairlines and segment them based on a statistic threshold value. Then, occluded hair information in the images was repaired using a partial differential equation inpainting method.…”