DocumentCode :
3515011
Title :
Refractive error detection via group sparse representation
Author :
Li, Qin ; Wang, Jinghua ; You, Jane ; Zhang, Bob ; Karray, Fakhri
Author_Institution :
Dept. of Comput., Hong Kong Polytech. Univ., Hong Kong, China
fYear :
2010
fDate :
21-23 June 2010
Firstpage :
1
Lastpage :
5
Abstract :
Nowadays large populations worldwide are suffering from eye diseases such as astigmatism, myopia, and hyperopia which are caused by ophthalmologically refractive errors. This paper presents an effective approach to computer aided diagnosis of such eye diseases due to ophthalmologically refractive errors. The proposed system consists of two major steps: (1) image segmentation and geometrical feature extraction; (2) group sparse representation based classification. Although image segmentation seems relatively easy and straight forward, it is a challenge task to achieve high accuracy of segmentation for images at poor quality caused by distortion during image digitization. To avoid misclassifications by incomplete information, we propose group sparse representation-based classification scheme to classify low-dimensional data which are partially corrupted. The experimental results demonstrate the feasibility of the new classification scheme with good performance for potential medical applications.
Keywords :
eye; feature extraction; image segmentation; medical image processing; sparse matrices; vision defects; astigmatism; computer aided diagnosis; eye disease; geometrical feature extraction; group sparse representation; hyperopia; image digitization; image segmentation; images quality; myopia; ophthalmological refractive error detection; Animals; Feature extraction; Image edge detection; Image segmentation; Pixel; Shape; Support vector machine classification; Eye disease; feature extraction; group sparse classification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Autonomous and Intelligent Systems (AIS), 2010 International Conference on
Conference_Location :
Povoa de Varzim
Print_ISBN :
978-1-4244-7104-1
Type :
conf
DOI :
10.1109/AIS.2010.5547046
Filename :
5547046
Link To Document :
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