21 July 2017 A deep convolutional feature based learning layer-specific edges method for segmenting OCT image
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Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 1042029 (2017) https://doi.org/10.1117/12.2282508
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China
Abstract
Optical coherence tomography (OCT) is a high resolution and non-invasive imaging modality that has become one of the most prevalent techniques for ophthalmic diagnostic. However, manual segmentation is often a time-consuming and subjective process. In this work, we present a new method for retinal layer segmentation in retinal optical coherence tomography images, which uses a deep convolutional feature to train a structured random forest classifier. The experimental results show that our method achieves good results with the mean distance error of 1.45 pixels whereas that of the state-of-the-art was 1.68 pixels, and achieve a F-score of 0.86 which is also better than 0.83 that is obtained by the state-of-the-art method.
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Tianyu Fu, Tianyu Fu, Xiaoming Liu, Xiaoming Liu, Dong Liu, Dong Liu, Zhou Yang, Zhou Yang, } "A deep convolutional feature based learning layer-specific edges method for segmenting OCT image", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 1042029 (21 July 2017); doi: 10.1117/12.2282508; https://doi.org/10.1117/12.2282508
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