Conference Paper

Health examination based on iris images

DOI: 10.1109/ICMLC.2010.5580885 Conference: International Conference on Machine Learning and Cybernetics, ICMLC 2010, Qingdao, China, July 11-14, 2010, Proceedings
Source: DBLP


This study combined iridology with image processing technique to conducts iris disease examination. Iridology is not to determine disease, but to reflect degeneration of organic functions, toxin precipitation and various unhealthy situations caused by mental or other factors. Iris test technique applies simple and non-invasive healthy examination method, helps the people prevent disease, and regularly follows up self-health conditions to achieve real-time prevention and treatment. The system consists of four modules: eye image capture, image preprocessing, texture feature extraction and symptomatic analysis. Following the input of eye image, the required iris part is acquired from eye images by using image preprocessing module. The texture feature extraction module utilizes 2-D Gabor filter to extract texture feature. The symptomatic analysis module uses fuzzy theory to evaluate severity of organ symptoms.

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    • "Standard tools were prepared to avoid shifting the area of pancreas. Cheng-liang Lai and Chien-lun Chiu[3], developed a system for health examination based on iris images. In this system, Iris image was normalized into a rectangular image by converting polar coordinates to Cartesian coordinate. "
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    ABSTRACT: The iris of human eye is globally identified as the better solution for biometric systems with its unique feature and complex pattern. In other side the iris has the significance to reflect the changes in human body with the varying health condition. This study of the iris for medical purposes is called iridology. A primary theory of Iridology is that the iris is constructed in layers that represent the four stages of tissue activity, namely acute changes, sub-acute changes, chronic changes and degenerative changes. Iridology is a novel, cost-effective and non-invasive approach of medical analysis because there are no touching, no damage to human body. The Iridologists have to measure color of iris, its density, open and closed lesion, iris signs and the corresponding location of body organ in iris image as stated in iridology chart. This paper is proposing a real time approach to analyze human iris specifically for Pulmonary Diseases due to problems in lung organ, using Image Processing techniques such as Circular Hough Transform, Fuzzy C-Means clustering and Gray level analysis. The efficiency of the proposed system is analyzed for its correctness with iridologist report.
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    ABSTRACT: Iridology is a science which correlates the apparitions of iris to tissue weaknesses in the body. It merely reveals weaknesses, inflammation, or toxicity in organs or tissues. It also indicates weakness long before the symptoms appear. In this paper, support vector machine-based iris recognition system utilizing iridology has been used to determine diabetes. Features from eye image database of 40 people having healthy eye (normal) and having affected eye (diabetes) have been extracted by 2-D wavelet tree. The overall accuracy is obtained to be 87.50 % which reasonably demonstrates the effectiveness of the system.
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