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Proceedings Paper

Image based grading of nuclear cataract by SVM regression
Author(s): Huiqi Li; Joo Hwee Lim; Jiang Liu; Tien Yin Wong; Ava Tan; Jie Jin Wang; Paul Mitchell
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Paper Abstract

Cataract is one of the leading causes of blindness worldwide. A computer-aided approach to assess nuclear cataract automatically and objectively is proposed in this paper. An enhanced Active Shape Model (ASM) is investigated to extract robust lens contour from slit-lamp images. The mean intensity in the lens area, the color information on the central posterior subcapsular reflex, and the profile on the visual axis are selected as the features for grading. A Support Vector Machine (SVM) scheme is proposed to grade nuclear cataract automatically. The proposed approach has been tested using the lens images from Singapore National Eye Centre. The mean error between the automatic grading and grader's decimal grading is 0.38. Statistical analysis shows that 97.8% of the automatic grades are within one grade difference to human grader's integer grades. Experimental results indicate that the proposed automatic grading approach is promising in facilitating nuclear cataract diagnosis.

Paper Details

Date Published: 17 March 2008
PDF: 8 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 691536 (17 March 2008); doi: 10.1117/12.769975
Show Author Affiliations
Huiqi Li, Agency for Science, Technology and Research (Singapore)
Joo Hwee Lim, Agency for Science, Technology and Research (Singapore)
Jiang Liu, Agency for Science, Technology and Research (Singapore)
Tien Yin Wong, National Univ. of Singapore (Singapore)
Ava Tan, The Univ. of Sydney (Australia)
Jie Jin Wang, The Univ. of Sydney (Australia)
Paul Mitchell, The Univ. of Sydney (Australia)


Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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