title: Hair enhancement in dermoscopic images using dual-channel quaternion tubularness filters and MRF-based multilabel optimization.
abstract: Hair occlusion is one of the main challenges facing automatic lesion segmentation and feature extraction for skin cancer applications. We propose a novel method for simultaneously enhancing both light and dark hairs with variable widths, from dermoscopic images, without the prior knowledge of the hair color. We measure hair tubularness using a quaternion color curvature filter. We extract optimal hair features (tubularness, scale, and orientation) using Markov random field theory and multilabel optimization. We also develop a novel dual-channel matched filter to enhance hair pixels in the dermoscopic images while suppressing irrelevant skin pixels. We evaluate the hair enhancement capabilities of our method on hair-occluded images generated via our new hair simulation algorithm. Since hair enhancement is an intermediate step in a computer-aided diagnosis system for analyzing dermoscopic images, we validate our method and compare it to other methods by studying its effect on: 1) hair segmentation accuracy; 2) image inpainting quality; and 3) image classification accuracy. The validation results on 40 real clinical dermoscopic images and 94 synthetic data demonstrate that our approach outperforms competing hair enhancement methods.
authors.forename: Hengameh
authors.initials: H
authors.lastname: Mirzaalian
authors.forename: Tim K
authors.initials: TK
authors.lastname: Lee
authors.forename: Ghassan
authors.initials: G
authors.lastname: Hamarneh
mesh_terms: D046169:Dermoscopy / Q000379:methods*; D006197:Hair / Q000033:anatomy & histology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008390:Markov Chains; D008545:Melanoma / Q000175:diagnosis; D012878:Skin Neoplasms / Q000175:diagnosis