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Adaptive weighted locality-constrained sparse coding for glaucoma diagnosis

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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.

The flowchart of the proposed approach for glaucoma diagnosis.

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Funding

This work was supported by the National Natural Science Foundation of China (Nos. 61602221, 61762050, 61603415, 61471101, and 41661083), the Science and Technology Research Project of Jiangxi Provincial Department of Education (No. GJJ160333), the Natural Science Foundation of Jiangxi Province (No. 20171BAB212009), and the Project of Doctoral Foundation of Shenyang Aerospace University (No. 19YB01), Scientific Research Fund Project of Liaoning Provincial Department of Education (No. JYT19040).

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Authors and Affiliations

  1. College of Computer Science, Shenyang Aerospace University, Shenyang, Liaoning, China

    Wei Zhou

  2. School of Software, Jiangxi Normal University, Nanchang, Jiangxi, China

    Yugen Yi & Wenle Wang

  3. College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China

    Jining Bao

  4. DITEN Department, University of Genoa, Genoa, Italy

    Jining Bao

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  1. Wei Zhou
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  2. Yugen Yi
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  3. Jining Bao
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  4. Wenle Wang
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Correspondence to Wei Zhou or Yugen Yi.

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Zhou, W., Yi, Y., Bao, J. et al. Adaptive weighted locality-constrained sparse coding for glaucoma diagnosis. Med Biol Eng Comput 57, 2055–2067 (2019). https://doi.org/10.1007/s11517-019-02011-z

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