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An Approach for Glaucoma Detection Based on the Features Representation in Radon Domain

  • Beiji Zou
  • Qilin Chen
  • Rongchang Zhao
  • Pingbo Ouyang
  • Chengzhang Zhu
  • Xuanchu Duan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)

Abstract

Glaucoma is a chronic and irreversible eye disease that leads to the structural changes of the Optic Nerve Head (ONH). In clinical practice, ONH assessment is one of the most significant measurements for glaucoma detection. However, the structural changes of ONH reveals complex mixture of visual patterns that are challenging to be represented. In this paper, a novel features representation approach in Radon domain is proposed to capture these complex patterns. In our method, fundus images are projected into Radon domain with Radon Transform (RT) in which the spatial radial variations of ONH are converted to a discrete signal for constraint optimization, feature enhancement and dimensionality reduction. Subsequently, the Discrete Wavelet Transform (DWT) is adopted to obtain subtle differences and quantize them. The experiments show that our approach achieves excellent detection results on RIMONE-r2 dataset with the accuracy and Area Under the Curve (AUC) of receiver operating characteristic curve at 86.154% and 0.906 respectively, much better than other algorithms. The results demonstrate that the proposed method can be used as an effective tool for glaucoma detection in the mass screening of fundus images.

Keywords

Computer-aided diagnosis Glaucoma detection Radon transform 

Notes

Acknowledgement

This work is supported by the National Natural Science of Foundation of China (No. 61573380) and Hunan Natural Science Foundation of China (No. 2017JJ3411).

References

  1. 1.
    Chen, X., Xu, Y., Yan, S., Wong, D.W.K., Wong, T.Y., Liu, J.: Automatic feature learning for glaucoma detection based on deep learning. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 669–677. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_80CrossRefGoogle Scholar
  2. 2.
    Fu, H., Cheng, J., Xu, Y., Wong, D.W.K., Jiang, L., Cao, X.: Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Trans. Med. Imaging PP(99), 1 (2018)Google Scholar
  3. 3.
    Cheng, J., Zhang, Z., Tao, D., Wong, D., Jiang, L., Baskaran, M.: Similarity regularized sparse group lasso for cup to disc ratio computation. Biomed. Opt. Express 8(8), 3763 (2017)CrossRefGoogle Scholar
  4. 4.
    Harizman, N., Oliveira, C., Chiang, A., Tello, C., Marmor, M., Ritch, R.: The ISNT rule and differentiation of normal from glaucomatous eyes. Arch. Ophthalmol. 124(11), 1579–1583 (2006)CrossRefGoogle Scholar
  5. 5.
    Teng, C.C., De Moraes, C.G., Prata, T.S., Tello, C., Ritch, R., Liebmann, J.M.: Beta-Zone parapapillary atrophy and the velocity of glaucoma progression. Ophthalmology 117(5), 909–915 (2010)CrossRefGoogle Scholar
  6. 6.
    Bock, R., Nyul, M.L.G., Hornegger, J., Michelson, G.: Glaucoma risk index: automated glaucoma detection from color fundus images. Med. Image Anal. 14(3), 471–481 (2010)CrossRefGoogle Scholar
  7. 7.
    Cheng, J., Yin, F., Wong, D.W.K., Tao, D., Jiang, L.: Sparse dissimilarity-constrained coding for glaucoma screening. IEEE Trans. Biomed. Eng. 62(5), 1395–1403 (2015)CrossRefGoogle Scholar
  8. 8.
    Maheshwari, S., Pachori, R.B., Acharya, U.R.: Automated diagnosis of glaucoma using empirical wavelet transform and correntropy features extracted from fundus images. IEEE J. Biomed. Health Inf. 21(3), 803–813 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Beiji Zou
    • 1
  • Qilin Chen
    • 1
  • Rongchang Zhao
    • 1
  • Pingbo Ouyang
    • 1
    • 2
  • Chengzhang Zhu
    • 1
  • Xuanchu Duan
    • 2
  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaChina
  2. 2.The Second Xiangya Hospital of Central South UniversityChangshaChina

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