Non-invasive jaundice detection using machine vision

Abstract

The study investigated a non-invasive and instant method of jaundice detection using machine vision technique. Color images of sclera region of the eyes of healthy subjects and patients suffering from jaundice were acquired. Image processing algorithms were developed by using CIELab color model. The principal component analysis (PCA)-based discrimination analysis was applied over the color data obtained from patient's sclera region, which showed a variance of 89%. The results of PCA biplot indicated correlations among jaundice patients and color attributes. Based upon these results, neuro-fuzzy-based software was developed for the prediction of jaundice as well as the calculation of degree of its severity. The experimental results show satisfactory performance as compared to the conventional chemical methods. The proposed technique is totally non-invasive and low cost.