ABSTRACT
This chapter discusses a case study that involved a noninvasive and instant disease detection technique based upon machine vision through the scanning of the eyes of the subjects. The detected diseases involved conjunctivitis (eye flu) and jaundice. In the proposed technique, color images of the sclera region of the subjects’ eyes were acquired by using an imaging setup specifically designed for the purpose. The image acquisition setup consists of three separate charge-coupled device (3CCD) digital color camera kept in an aphotic enclosure. The facial region of the patient was lit under controlled illumination, and the images of the targeted region, namely, sclera region of the eyes, were acquired and processed using the algorithms developed in a MATLAB® environment to get the respective color attributes in RGB and La*b* color models. Principal component analysis (PCA)-based discrimination was applied over the color data of the subjects that showed high variance. The results of PCA indicated correlations among patients and the color attributes. The neuro-fuzzy-based software was developed for the prediction of jaundice and conjunctivitis along with the degree of severity and types, respectively. The experimental results showed good performance for the proposed method as compared to the conventional chemical methods with an accuracy level of over 90%.
