Please use this identifier to cite or link to this item: https://doi.org/10.1109/IEMBS.2011.6091249
Title: Automatic detection of cortical and PSC cataracts using texture and intensity analysis on retro-illumination lens images
Authors: Chow, Y.C.
Gao, X.
Li, H.
Lim, J.H.
Sun, Y. 
Wong, T.Y. 
Issue Date: 2011
Source: Chow, Y.C.,Gao, X.,Li, H.,Lim, J.H.,Sun, Y.,Wong, T.Y. (2011). Automatic detection of cortical and PSC cataracts using texture and intensity analysis on retro-illumination lens images. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS : 5044-5047. ScholarBank@NUS Repository. https://doi.org/10.1109/IEMBS.2011.6091249
Abstract: Cataract remains a leading cause for blindness worldwide. Cataract diagnosis via human grading is subjective and time-consuming. Several methods of automatic grading are currently available, but each of them suffers from some drawbacks. In this paper, a new approach for automatic detection based on texture and intensity analysis is proposed to address the problems of existing methods and improve the performance from three aspects, namely ROI detection, lens mask generation and opacity detection. In the detection method, image clipping and texture analysis are applied to overcome the over-detection problem for clear lens images and global thresholding is exploited to solve the under-detection problem for severe cataract images. The proposed method is tested on 725 retro-illumination lens images randomly selected from a database of a community study. Experiments show improved performance compared with the state-of-the-art method. © 2011 IEEE.
Source Title: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
URI: http://scholarbank.nus.edu.sg/handle/10635/51119
ISBN: 9781424441211
ISSN: 1557170X
DOI: 10.1109/IEMBS.2011.6091249
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