Abstract
Age-related macular degeneration (AMD) is a common cause of vision loss among the elderly in developed countries. Geographic atrophy (GA) appears in advanced stages of non-exudative AMD. In this paper, we present a hybrid GA segmentation model for spectral-domain optical coherence tomography (SD-OCT) images. The method first segments the layered structure of the SD-OCT scan data and produces the projection images. Then we construct the histogram of the resulting image into a probability distribution function, and use this function to fit a Gaussian mixed model (GMM) by Moth-flame optimization (MFO) algorithm. To incorporate the globe spatial information to over come the impact of noise, a robust affinity diffusion method is proposed to construct the affinity map. Finally, bias field correction process is employed to remove the intensity inhomogeneity. Two data sets, respectively consisting on 55 SD-OCT scans from twelve eyes in eight patients with GA and 56 SD-OCT scans from 56 eyes in 56 patients with GA, are utilized to quantitatively evaluate the segmentation algorithm. Experimental results demonstrate that the proposed algorithm can achieve high segmentation accuracy.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grants 61401209 & 61671242, in part by the Natural Science Foundation of Jiangsu Province, China under Grant BK20140790, in part by the Fundamental Research Funds for the Central Universities under Grant 30916011324, and in part by China Postdoctoral Science Foundation under Grants 2014T70525 & 2013M531364.
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Huang, Y., Ji, Z., Chen, Q., Niu, S. (2017). Geographic Atrophy Segmentation for SD-OCT Images by MFO Algorithm and Affinity Diffusion. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_42
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DOI: https://doi.org/10.1007/978-3-319-67777-4_42
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