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Vessel-based hybrid optic disk segmentation applied to mobile phone camera retinal images

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Abstract

Precise detection of the optic disk (OD) is an important task in the diagnosis of diabetic retinopathy. To manage the massive diabetic population, there is a significant demand for efficient and remote retinal imaging techniques. In this regard, the use of handheld mobile cameras attached to a smartphone is a promising approach. However, smartphone retinal images are often of low quality, compared to those obtained on standard equipment. They also have a narrow field of view and an incomplete/unbalanced vessel structure. Hence, we propose a new, fully automatic hybrid method for OD localization (HLM). It is designed for and verified on mobile camera/smartphone retinal images. The HLM analyzes the vessel structure and finds the OD locations by using the exclusion method when an image has a complete vessel system, and a newly proposed line detection method, otherwise. For OD segmentation, an active contour model followed by the circle fitting approach is integrated into the HLM. The proposed method was tested on three mobile camera datasets and four datasets obtained by standard equipment. For mobile camera datasets, the HLM achieves an average accuracy of 98% for OD localization. The segmentation routine obtains an average precision of 92.64% and an average recall of 82.38%. Testing against the recent state-of-the-art methods on the standard datasets shows comparable performance.

Graphical abstract

The proposed framework for OD localization and segmentation designed for and verified on mobile camera retinal datasets and standard datasets. (EM - "Exclusion Method", LDM - "Line Detection Method", OD - "Optic Disk" and PPV - "Positive Predictive Value")

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Acknowledgements

We would like to acknowledge Thammasat Eye Center (TEC) for providing the mobile camera datasets and for conductive discussions regarding the clinical experiments.

Funding

This work received financial support from the Thai Government Research Fund (contract numbers 33/2560 and 24/2561), the National Research Council of Thailand (NRCT) (grant number NRCT5-RSA63010-05), the JSPS Core-to-Core Program (grant number JPJSCCA20170004), and the Center of Excellence in Biomedical Engineering of Thammasat University.

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Authors and Affiliations

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

    Tin Tin Khaing, Pakinee Aimmanee & Stanislav Makhanov

  2. Division of Fundamental Engineering, Graduate School of Science and Engineering, Chiba University, 1-33 Yayoicho, Inage Ward, Chiba, 263-8522, Japan

    Tin Tin Khaing

  3. Center for Frontier Medical Engineering, Chiba University, 1-33 Yayoicho, Inage Ward, Chiba, 263-8522, Japan

    Hideaki Haneishi

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  1. Tin Tin Khaing
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  2. Pakinee Aimmanee
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Correspondence to Pakinee Aimmanee.

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Khaing, T.T., Aimmanee, P., Makhanov, S. et al. Vessel-based hybrid optic disk segmentation applied to mobile phone camera retinal images. Med Biol Eng Comput 60, 421–437 (2022). https://doi.org/10.1007/s11517-021-02484-x

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