TY - GEN
T1 - Intra-retinal layers segmentation of macular OCT images based on the graph optimal approach
AU - Gao, Zhijun
AU - Bu, Wei
AU - Wu, Xiangqian
AU - Zheng, Yalin
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/2/13
Y1 - 2017/2/13
N2 - Based on the graph optimal technology, a novel automated intra-retinal layers segmentation method is proposed in this paper. Eleven boundaries of ten retinal layers in optical coherence tomography (OCT) images are exactly, fast and reliably quantified. Instead of considering the intensity or gradient features of the single-pixel, the proposed method focuses on the whole edge-based image cues. The image is represented as a complete weighted graph with connected components as nodes. Each node is ranked based on the gradient and spatial distance information of the connected components, and the affinity matrices. Segmentation is efficiently implemented in a three-stage project to extract eleven boundaries. The segmentation algorithm is evaluated on 64 OCT images from two different databases, and is compared with the manual tracings of two independent observers. It demonstrates encouraging results in term of the mean unsigned boundaries errors and the mean signed boundaries errors.
AB - Based on the graph optimal technology, a novel automated intra-retinal layers segmentation method is proposed in this paper. Eleven boundaries of ten retinal layers in optical coherence tomography (OCT) images are exactly, fast and reliably quantified. Instead of considering the intensity or gradient features of the single-pixel, the proposed method focuses on the whole edge-based image cues. The image is represented as a complete weighted graph with connected components as nodes. Each node is ranked based on the gradient and spatial distance information of the connected components, and the affinity matrices. Segmentation is efficiently implemented in a three-stage project to extract eleven boundaries. The segmentation algorithm is evaluated on 64 OCT images from two different databases, and is compared with the manual tracings of two independent observers. It demonstrates encouraging results in term of the mean unsigned boundaries errors and the mean signed boundaries errors.
KW - affinity matrice
KW - graph optimal
KW - intra-retinal layers segmentation
KW - optical coherence tomography(OCT)
UR - https://www.scopus.com/pages/publications/85016065625
U2 - 10.1109/CISP-BMEI.2016.7852928
DO - 10.1109/CISP-BMEI.2016.7852928
M3 - 会议稿件
AN - SCOPUS:85016065625
T3 - Proceedings - 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2016
SP - 1359
EP - 1364
BT - Proceedings - 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2016
Y2 - 15 October 2016 through 17 October 2016
ER -