Multi-scale analysis of skin hyper-pigmentation evolution

Abstract

In this paper, we use statistical inference and muti-spectral images to quantify the evolution of skin hyper-pigmentation lesions under treatment. We show that statistical inference allows getting change maps of the disease which can be useful for dermatologists to analyze the disease evolution. Indeed, a local change map is obtained by computing the deviation between two multi-spectral images in a region of interest (ROI). Then, we normalize the obtained map and develop a statistical inference framework to quantify the changes. Finally, we propose a criterion that integrates change maps in order to quantify the treatment efficacy on a patient.

What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics

“…As a result there have been substantial efforts to develop novel noninvasive optical imaging techniques as a way to detect and analyze skin lesions [222], [100], with recent advances involving smart hand-held devices [36], [263]. In addition to skin cancer, image-based methods can apply to other common skin diseases like chronic inflammatory psoriasis [149], [57], pressure ulcers [141] and hyper-pigmentation evolution [194].…”