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Automatic optic disk detection in retinal images using hybrid vessel phase portrait analysis

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Abstract

We propose vessel vector-based phase portrait analysis (VVPPA) and a hybrid between VVPPA and a clustering method proposed earlier for automatic optic disk (OD) detection called the vessel transform (VT). The algorithms are based primarily on the location and direction of retinal blood vessels and work equally well on fine and poor quality images. To localize the OD, the direction vectors derived from the vessel network are constructed, and points of convergence of the resulting vector field are examined by phase portrait analysis. The hybrid method (HM) uses a set of rules acquired from the decision model to alternate the use of VVPPA and VT. To identify the OD contour, the scale space (SS) approach is integrated with VVPPA, HM, and the circular approximation (SSVVPPAC and SSHMC). We test the proposed combination against state-of-the-art OD detection methods. The results show that the proposed algorithms outperform the benchmark methods, especially on poor quality images. Specifically, the HM gets the highest accuracy of 98% for localization of the OD regardless of the image quality. Testing the segmentation routines SSVVPPAC and SSHMC against the conventional methods shows that SSHMC performs better than the existing methods, achieving the highest PPV of 71.81% and the highest sensitivity of 70.67% for poor quality images. Furthermore, the HM can supplement practically any segmentation model as long as it offers multiple OD candidates. In order to prove this claim, we test the efficiency of the HM in detecting retinal abnormalities in a real clinical setting. The images have been obtained by portable lens connected to a smart phone. In detecting the abnormalities related to diabetic retinopathy (DR), the algorithm provided 94.67 and 98.13% for true negatives and true positives, respectively.

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Acknowledgements

The authors would like to gratefully acknowledge the financial support from Thailand Research Fund (TRF), grant number RSA5780034, the Center of Excellence in Biomedical Engineering of Thammasat University, and the Thai Royal Government Scholarship, the Ministry of Science and Technology, National Research University. We would also like to thank the Department of Ophthalmology, Faculty of Medicine of Thammasat University for their help in collecting the data and conducting the clinical experiments.

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  1. Sirindhorn International Institute of Technology, Thammasat University, 131 Moo 5, Tiwanont Road, Bangkadi, Muang, Pathum Thani, 12000, Thailand

    Nittaya Muangnak, Pakinee Aimmanee & Stanislav Makhanov

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  1. Nittaya Muangnak
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  2. Pakinee Aimmanee
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Correspondence to Pakinee Aimmanee.

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Muangnak, N., Aimmanee, P. & Makhanov, S. Automatic optic disk detection in retinal images using hybrid vessel phase portrait analysis. Med Biol Eng Comput 56, 583–598 (2018). https://doi.org/10.1007/s11517-017-1705-z

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