Deep Retinal Image Understanding

[1]  Kotagiri Ramamohanarao,et al.  An effective retinal blood vessel segmentation method using multi-scale line detection , 2013, Pattern Recognit..

[2]  Mariano Alcañiz Raya,et al.  Automatic Detection of Optic Disc Based on PCA and Mathematical Morphology , 2013, IEEE Transactions on Medical Imaging.

[3]  Matthew B. Blaschko,et al.  Learning Fully-Connected CRFs for Blood Vessel Segmentation in Retinal Images , 2014, MICCAI.

[4]  Jitendra Malik,et al.  Region-Based Convolutional Networks for Accurate Object Detection and Segmentation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Aliaa A. A. Youssif,et al.  Optic Disc Detection From Normalized Digital Fundus Images by Means of a Vessels' Direction Matched Filter , 2008, IEEE Transactions on Medical Imaging.

[6]  Jordi Pont-Tuset,et al.  Supervised Evaluation of Image Segmentation and Object Proposal Techniques , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Mariano Rincón,et al.  Identification of the optic nerve head with genetic algorithms , 2008, Artif. Intell. Medicine.

[8]  Tien Yin Wong,et al.  Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening , 2013, IEEE Transactions on Medical Imaging.

[9]  Saining Xie,et al.  Holistically-Nested Edge Detection , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[10]  Jitendra Malik,et al.  Hypercolumns for object segmentation and fine-grained localization , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  A.D. Hoover,et al.  Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response , 2000, IEEE Transactions on Medical Imaging.

[12]  Vincent Lepetit,et al.  Projection onto the Manifold of Elongated Structures for Accurate Extraction , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[13]  Roberto Marcondes Cesar Junior,et al.  Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification , 2005, IEEE Transactions on Medical Imaging.

[14]  Lei Zhang,et al.  Retinal vessel extraction by matched filter with first-order derivative of Gaussian , 2010, Comput. Biol. Medicine.

[15]  Bunyarit Uyyanonvara,et al.  Blood vessel segmentation methodologies in retinal images - A survey , 2012, Comput. Methods Programs Biomed..

[16]  Joachim M. Buhmann,et al.  Boosting Convolutional Filters with Entropy Sampling for Optic Cup and Disc Image Segmentation from Fundus Images , 2015, MLMI.

[17]  C. Lawrence Zitnick,et al.  Structured Forests for Fast Edge Detection , 2013, 2013 IEEE International Conference on Computer Vision.

[18]  Li Cheng,et al.  Learning to Boost Filamentary Structure Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[19]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[20]  Max A. Viergever,et al.  Ridge-based vessel segmentation in color images of the retina , 2004, IEEE Transactions on Medical Imaging.

[21]  Thomas Walter,et al.  Segmentation of Color Fundus Images of the Human Retina: Detection of the Optic Disc and the Vascular Tree Using Morphological Techniques , 2001, ISMDA.

[22]  Vincent Lepetit,et al.  Supervised Feature Learning for Curvilinear Structure Segmentation , 2013, MICCAI.

[23]  Victor S. Lempitsky,et al.  N4-Fields: Neural Network Nearest Neighbor Fields for Image Transforms , 2014, ArXiv.

[24]  Francisco Fumero,et al.  RIM-ONE: An open retinal image database for optic nerve evaluation , 2011, 2011 24th International Symposium on Computer-Based Medical Systems (CBMS).

[25]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Elisa Ricci,et al.  Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification , 2007, IEEE Transactions on Medical Imaging.

[27]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.