29 March 2016 Automatic segmentation of canine retinal OCT using adaptive gradient enhancement and region growing
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In recent years, several studies have shown that the canine retina model offers important insight for our understanding of human retinal diseases. Several therapies developed to treat blindness in such models have already moved onto human clinical trials, with more currently under development [1]. Optical coherence tomography (OCT) offers a high resolution imaging modality for performing in-vivo analysis of the retinal layers. However, existing algorithms for automatically segmenting and analyzing such data have been mostly focused on the human retina. As a result, canine retinal images are often still being analyzed using manual segmentations, which is a slow and laborious task. In this work, we propose a method for automatically segmenting 5 boundaries in canine retinal OCT. The algorithm employs the position relationships between different boundaries to adaptively enhance the gradient map. A region growing algorithm is then used on the enhanced gradient maps to find the five boundaries separately. The automatic segmentation was compared against manual segmentations showing an average absolute error of 5.82 ± 4.02 microns.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yufan He, Yufan He, Yankui Sun, Yankui Sun, Min Chen, Min Chen, Yuanjie Zheng, Yuanjie Zheng, Hui Liu, Hui Liu, Cecilia Leon, Cecilia Leon, William Beltran, William Beltran, James C. Gee, James C. Gee, } "Automatic segmentation of canine retinal OCT using adaptive gradient enhancement and region growing", Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 97881Q (29 March 2016); doi: 10.1117/12.2217186; https://doi.org/10.1117/12.2217186

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