Conference or Workshop Item #18416

(2018) A dictionary learning based method for detection of diabetic retinopathy in color fundus images. In: 10th Iranian Conference on Machine Vision and Image Processing, MVIP 2017, 22 November 2017through 23 November 2017, Isfahan.

Full text not available from this repository.

Abstract

Diabetic retinopathy (DR) is a chronic eye disease characterized by degenerative changes to the retina's blood vessels. In this paper, we present a dictionary learning (DL)-based method for automatic detection of DR in digital fundus images. The detection method is according to best atomic representation of fundus images based on learned dictionaries by K-SVD algorithm. However, the learned dictionaries by K-SVD should be able to discriminate the normal and diabetic classes, i.e. discriminative atoms should be designed. For this purpose, the best discriminative atoms are obtained for atomic representation of images in each class. The classification rule is based on the best sparse representation, i.e. the test image is belonged to the class with minimum number of best specific atoms. Our discriminative DL-based method was tested on 30 color fundus images which accuracies of 70 and 90 were obtained for normal and diabetic images, respectively. © 2017 IEEE.

Item Type: Conference or Workshop Item (Paper)
Keywords: Diabetic retinopathy Dictionary learning Digital fundus images K-SVD Atoms Blood vessels Computer vision Automatic Detection Classification rules Degenerative changes Learned dictionaries Sparse representation Eye protection
Subjects: WN Radiology. Diagnostic Imaging > WN 180-240 Diagnostic Imaging
WW Ophthalmology
Divisions: Medical Image and Signal Processing Research Center
Page Range: pp. 119-122
Journal Index: Scopus
Volume: 2017-N
Publisher: IEEE Computer Society
Identification Number: https://doi.org/10.1109/IranianMVIP.2017.8342333
ISBN: 21666776 (ISSN); 9781538644041 (ISBN)
Depositing User: Zahra Otroj
URI: http://eprints.mui.ac.ir/id/eprint/18416

Actions (login required)

View Item View Item