Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/32537
Title: Iris-based medical analysis by geometric deformation features
Authors: Ma, L
Zhang, D 
Li, N
Cai, Y
Zuo, W
Wang, K
Keywords: Collarette-based feature
Disease recognition
Iris geometric deformation feature
Pupil-based feature
Issue Date: 2013
Source: IEEE Journal of biomedical and health informatics, 2013, v. 17, no. 1, p. 223-231 How to cite?
Journal: IEEE Journal of Biomedical and Health Informatics 
Abstract: Iris analysis studies the relationship between human health and changes in the anatomy of the iris. Apart from the fact that iris recognition focuses on modeling the overall structure of the iris, iris diagnosis emphasizes the detecting and analyzing of local variations in the characteristics of irises. This paper focuses on studying the geometrical structure changes in irises that are caused by gastrointestinal diseases, and on measuring the observable deformations in the geometrical structures of irises that are related to roundness, diameter, and other geometric forms of the pupil and the collarette. Pupil- and collarette-based features are defined and extracted. A series of experiments are implemented on our experimental pathological iris database, including manual clustering of both normal and pathological iris images, manual classification by nonspecialists, manual classification by individuals with a medical background, classification ability verification for the proposed features, and disease recognition by applying the proposed features. The results prove the effectiveness and clinical diagnostic significance of the proposed features and a reliable recognition performance for automatic disease diagnosis. Our research results offer a novel systematic perspective for iridology studies and promote the progress of both theoretical and practical work in iris diagnosis.
URI: http://hdl.handle.net/10397/32537
ISSN: 2168-2194
DOI: 10.1109/TITB.2012.2222655
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