Optical coherence tomography cannot easily be used for visual identification of the ganglion cell layer (GCL) for diagnosing retinal diseases owing to the extremely low image contrast between adjacent layers. To solve this problem, the authors used a limit-clipping optimisation method along with the image entropy to enhance the image contrast of targeted layers. As a result, the GCL was successfully extracted using an intelligent tracking system without impacting the segmentation of other retinal layers and image morphology. The segmentation results were evaluated through comparisons with manual segmentation results provided by clinical experts. The results of this study should help realise simple and efficient discrimination of important retinal layers for the early diagnosis of glaucoma.
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Acknowledgments: This research was supported in part by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Centre) support program (IITP-2019-2016-0-00464) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation). This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2017R1A2B2003808). The authors thank Mr. Y.-M. Cha for
ASJC Scopus subject areas
- Electrical and Electronic Engineering