Detection and classification of bright lesions in color fundus images
Zhang Xiaohui
Chutatape, A.
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore;
This paper appears in: Image Processing, 2004. ICIP '04. 2004 International Conference on
Publication Date: 24-27 Oct. 2004
Volume: 1,
On page(s): 139- 142 Vol. 1
ISSN: 1522-4880
ISBN: 0-7803-8554-3
INSPEC Accession Number: 8402546
Digital Object Identifier: 10.1109/ICIP.2004.1418709
Current Version Published: 2005-04-18
Abstract
Bright lesions, including exudates and cotton wool spots, are the main symptoms in diabetic retinopathy. Early detection and classification of such evidence is essential for an effective treatment. A three-stage approach is applied to detect and classify bright lesions. After a local contrast enhancement preprocessing stage, two-step improved fuzzy C-means is applied in Luv color space to segment candidate bright-lesion areas. The results are shown to be effective in dealing with the inhomogeneous illumination of the fundus images while reducing the influence of noise. Finally, a hierarchical support vector machine (SVM) classification structure is successfully applied to classify bright non-lesion areas, exudates and cotton wool spots.
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
