Realistic hair simulator for skin lesion images: A novel benchemarking tool

Abstract

Automated skin lesion analysis is one of the trending fields that has gained attention among the dermatologists and health care practitioners. Skin lesion restoration is an essential pre-processing step for lesion enhancements for accurate automated analysis and diagnosis by both dermatologists and computer-aided diagnosis tools. Hair occlusion is one of the most popular artifacts in dermatoscopic images. It can negatively impact the skin lesions diagnosis by both dermatologists and automated computer diagnostic tools. Digital hair removal is a non-invasive method for image enhancement for decrease the hair-occlusion artifact in previously captured images. Several hair removal methods were proposed for skin delineation and removal without standardized benchmarking techniques. Manual annotation is one of the main challenges that hinder the validation of these proposed methods on a large number of images or against benchmarking datasets for comparison purposes. In the presented work, we propose a photo-realistic hair simulator based on context-aware image synthesis using image-to-image translation techniques via conditional adversarial generative networks for generation of different hair occlusions in skin images, along with ground-truth mask for hair location. Hair-occluded image is synthesized using the latent structure of any input hair-free image by deep encoding the input image into a latent vector of features. The locations of required hair are highlighted using white pixels on the input image. Then, these deep encoded features are used to reconstruct the synthetic highly realistic hair-occluded image. Besides, we explored using three loss functions including L₁-norm, L₂-norm and structural similarity index (SSIM) to maximize the image synthesis visual quality. For the evaluation of the generated samples, the t-SNE feature mapping and Bland-Altman test are used as visualization tools for the experimental results. The results show the superior performance of our proposed method compared to previous methods for hair synthesis with plausible colours and preserving the integrity of the lesion texture. The proposed method can be used to generate benchmarking datasets for comparing the performance of digital hair removal methods. The code is available online at: https://github.com/attiamohammed/realhair.

Keywords: Computer-aided diagnosis; Dermatology; Generative adversarial networks; Melanoma; Synthetic.