Title
Adaptive weighted locality-constrained sparse coding for glaucoma diagnosis.
Abstract
Glaucoma is a sight-threading disease which can lead to irreversible blindness. Currently, extracting the vertical cup-to-disc ratio (CDR) from 2D retinal fundus images is promising for automatic glaucoma diagnosis. In this paper, we present a novel sparse coding approach for glaucoma diagnosis called adaptive weighted locality-constrained sparse coding (AWLCSC). Different from the existing reconstruction-based glaucoma diagnosis approaches, the weighted matrix in AWLCSC is constructed by adaptively fusing multiple distance measurement information between the reference images and the testing image, making our approach more robust and effective to glaucoma diagnosis. In our approach, the disc image is firstly extracted and reconstructed according to the proposed AWLCSC technique. Then, with the usage of the obtained reconstruction coefficients and a series of reference disc images with known CDRs, the CDR of the testing disc image can be automated estimation for glaucoma diagnosis. The performance of the proposed AWLCSC is evaluated on two publicly available DRISHTI-GS1 and RIM-ONE r2 databases. The experimental results indicate that the proposed approach outperforms the state-of-the-art approaches.
Year
DOI
Venue
2019
10.1007/s11517-019-02011-z
Medical & Biological Engineering & Computing
Keywords
Field
DocType
Glaucoma, Cup-to-disc ratio, Multiple distance measurements, Sparse coding
Distance measurement,Computer vision,Glaucoma,Locality,Neural coding,Cup-to-disc ratio,Fundus (eye),Artificial intelligence,Blindness,Mathematics
Journal
Volume
Issue
ISSN
57
9
0140-0118
Citations 
PageRank 
References 
1
0.38
26
Authors
4
Name
Order
Citations
PageRank
Wei Zhou152.16
Yugen Yi29215.25
Jining Bao321.75
Wenle Wang411.73