Supervised pixel classification for segmenting geographic atrophy in fundus autofluorescene images
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Gérard G. Medioni | Srinivas R. Sadda | Matthias Hernandez | Zhihong Hu | G. Medioni | S. Sadda | Z. Hu | Matthias Hernandez
[1] Glenn J Jaffe,et al. Semiautomated image processing method for identification and quantification of geographic atrophy in age-related macular degeneration. , 2011, Investigative ophthalmology & visual science.
[2] Ron Kikinis,et al. Statistical validation of image segmentation quality based on a spatial overlap index. , 2004, Academic radiology.
[3] C. J. Blair,et al. Geographic atrophy of the retinal pigment epithelium. A manifestation of senile macular degeneration. , 1975, Archives of ophthalmology.
[4] A. Bird,et al. FUNDUS AUTOFLUORESCENCE IMAGING: Review and Perspectives , 2008, Retina.
[5] Ameet Talwalkar,et al. Foundations of Machine Learning , 2012, Adaptive computation and machine learning.
[6] M. Killingsworth,et al. Evolution of geographic atrophy of the retinal pigment epithelium , 1988, Eye.
[7] Zhihong Hu,et al. Segmentation of the geographic atrophy in spectral-domain optical coherence tomography and fundus autofluorescence images. , 2013, Investigative ophthalmology & visual science.
[8] Mona Kathryn Garvin,et al. Automated Segmentation of 3-D Spectral OCT Retinal Blood Vessels by Neural Canal Opening False Positive Suppression , 2010, MICCAI.
[9] Ronald Klein,et al. Changes in visual acuity in a population over a 15-year period: the Beaver Dam Eye Study. , 2006, American journal of ophthalmology.
[10] Milan Sonka,et al. Three-Dimensional Analysis of Retinal Layer Texture: Identification of Fluid-Filled Regions in SD-OCT of the Macula , 2010, IEEE Transactions on Medical Imaging.