Diagnosing Affected Organs Using Automated Iridology System

Iridology is the study of the iris of the eye for medical purposes. It is a preventive medicine since it can warn a person's tendency towards an apparent disease. Cleansing and healing of the body can be verified from changes in the iris. This study aims to design and develop an iris recognition system for automating iridology. The project involves 3 main steps: applying image processing techniques on eye image for data acquisition, collecting the database which is necessary for the iridology analysis and recognizing the affected organ in the body through iris analysis by using neural networks techniques. The image processing techniques are utilized for extracting eye
images. A chart of right and left eyes has been acquired through the Internet and approved by an iridologist: Then, the extracted iris image is compared to the chart to determine the affected organ. Neural network with Back propagation is used to match the iris images with affected organ. A total of 159 images retrieved from internet was preprocessed and fed into NN engine. The Backprobagation network succeeded and getting best results because it attained to 96.2 % correction percentage.