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Automated glaucoma screening method based on image segmentation and feature extraction

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Abstract

Glaucoma is a chronic disease that threatens eye health and can cause permanent blindness. Since there is no cure for glaucoma, early screening and detection are crucial for the prevention of glaucoma. Therefore, a novel method for automatic glaucoma screening that combines clinical measurement features with image-based features is proposed in this paper. To accurately extract clinical measurement features, an improved UNet++ neural network is proposed to segment the optic disc and optic cup based on region of interest (ROI) simultaneously. Some important clinical measurement features, such as optic cup to disc ratio, are extracted from the segmentation results. Then, the increasing field of view (IFOV) feature model is proposed to fully extract texture features, statistical features, and other hidden image-based features. Next, we select the best feature combination from all the features and use the adaptive synthetic sampling approach to alleviate the uneven distribution of training data. Finally, a gradient boosting decision tree (GBDT) classifier for glaucoma screening is trained. Experimental results based on the ORIGA dataset show that the proposed algorithm achieves excellent glaucoma screening performance with sensitivity of 0.894, accuracy of 0.843, and AUC of 0.901, which is superior to other existing methods.

Graphical abstract

Framework of the proposed glaucoma classification method

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Funding

This work was supported by the National Natural Science Foundation of China under Grant 61502537, the Natural Science Foundation of Hunan Province of China under Grant 2018JJ3681, the Independent Exploration and Innovation Project for Graduate Students of Central South University under Grant 2020zzts567, and the Open Project Fund of the Key Laboratory of Digital Signal and Image Processing of Guangdong Province under Grant 2018GDDSIPL-01.

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Authors and Affiliations

  1. School of Automation, Central South University, Changsha, 410083, China

    Fan Guo, Weiqing Li & Jin Tang

  2. School of Computer Science and Engineering, Central South University, Changsha, 410083, China

    Beiji Zou

  3. Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Shantou University, Shantou, 515063, China

    Zhun Fan

Authors
  1. Fan Guo
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  2. Weiqing Li
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  3. Jin Tang
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  4. Beiji Zou
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  5. Zhun Fan
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Correspondence to Jin Tang.

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Appendix

Appendix

The description of the features after ranking and selected in Fig. 15 can be seen in Table 9

Table 9 The description of the selected features
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Guo, F., Li, W., Tang, J. et al. Automated glaucoma screening method based on image segmentation and feature extraction. Med Biol Eng Comput 58, 2567–2586 (2020). https://doi.org/10.1007/s11517-020-02237-2

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