Severity of Cellulite Classification Based on Tissue Thermal Imagining

Abstract

In this article we present a novel approach to cellulite classification that can be personlised based on non-contact thermal imaging using IR thermography. By analysing the superficial temperature distribution of the body it is possible to diagnose the stages of cellulite development. The study investigates thermal images of posterior of thighs of female volunteers and identifies cellulite areas in an automatic way using image processing. The Growing Bubble Algorithm has been used for thermal picture conversion into valid input vector for a neural network based classifier scheme. Using machine learning process of training the input database was prepared as the stage of cellulite classifier according to the state of the art Nurnberger-Muller diagnosis scheme. Our work demonstrates that it is possible to diagnose the cellulite with over 70% accuracy using a cost-effective, simple and unsophisticated classifier which operates on low-definition pictures. In essence, our work shows that IR-thermography, when coupled with computer aided image analysis and processing, can be a very convenient and effective tool to enable personalized diagnosis and preventive medicine to improve the quality of life of women suffering from cellulite problems.