Retinal automatic segmentation method based on prior information and optimized boundary tracking algorithm

Dongmei Fu; Hejun Tong; Ling Luo; Fulin Gao

doi:10.1117/12.2244915

29 August 2016 Retinal automatic segmentation method based on prior information and optimized boundary tracking algorithm

Dongmei Fu, Hejun Tong, Ling Luo, Fulin Gao

Author Affiliations +

Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100331C (2016) https://doi.org/10.1117/12.2244915
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China

Abstract

Optical coherence tomography (OCT) is a new imaging technology which is widely used in the field of ophthalmology, and retinal tissue layers segmentation plays an important role in the diagnosis of retinal diseases. This paper proposed an OCT macular retinal segmentation method based on the prior information of retinal structure and the optimized boundary tracking algorithm and realized the automatic segmentation of nine retinal layers. After image preprocessing, according to the multi-scale morphological operations and retinal structure characteristics, the optimal initial points were acquired in the parafovea domain. According to the new definition of boundary description feature, this paper optimized the traditional boundary tracking algorithm, and segmented the retinal boundaries. This paper analyzed 100 retinal OCT images, which come from 50 healthy participants from 18 to 29 years old, then compared our segmentation results with graph-based segmentation results and manual segmentations labeled by two experts. Experimental results showed that our method can accurately and effectively segment nine retinal layers (mean square error of boundary position is 1.18 ± 0.40 pixels), and is close to the results of manual segmentation (1.06±0.22 pixels), better than the literature segmentation results (3.02±1.03 pixels).

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Dongmei Fu, Hejun Tong, Ling Luo, and Fulin Gao "Retinal automatic segmentation method based on prior information and optimized boundary tracking algorithm", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100331C (29 August 2016); https://doi.org/10.1117/12.2244915

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