Literature DB >> 24579126

Optic disc and cup segmentation from color fundus photograph using graph cut with priors.

Yuanjie Zheng1, Dwight Stambolian2, Joan O'Brien2, James C Gee1.   

Abstract

For automatic segmentation of optic disc and cup from color fundus photograph, we describe a fairly general energy function that can naturally fit into a global optimization framework with graph cut. Distinguished from most previous work, our energy function includes priors on the shape & location of disc & cup, the rim thickness and the geometric interaction of "disc contains cup". These priors together with the effective optimization of graph cut enable our algorithm to generate reliable and robust solutions. Our approach is able to outperform several state-of-the-art segmentation methods, as shown by a set of experimental comparisons with manual delineations and a series of results of correlations with the assessments of a merchant-provided software from Optical Coherence Tomography (OCT) regarding several cup and disc parameters.

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Year:  2013        PMID: 24579126      PMCID: PMC4165089          DOI: 10.1007/978-3-642-40763-5_10

Source DB:  PubMed          Journal:  Med Image Comput Comput Assist Interv


  9 in total

1.  Comparison of optic nerve imaging methods to distinguish normal eyes from those with glaucoma.

Authors:  Michael J Greaney; Douglas C Hoffman; David F Garway-Heath; Mamdouh Nakla; Anne L Coleman; Joseph Caprioli
Journal:  Invest Ophthalmol Vis Sci       Date:  2002-01       Impact factor: 4.799

2.  Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching.

Authors:  M Lalonde; M Beaulieu; L Gagnon
Journal:  IEEE Trans Med Imaging       Date:  2001-11       Impact factor: 10.048

3.  Towards robust and effective shape modeling: sparse shape composition.

Authors:  Shaoting Zhang; Yiqiang Zhan; Maneesh Dewan; Junzhou Huang; Dimitris N Metaxas; Xiang Sean Zhou
Journal:  Med Image Anal       Date:  2011-09-05       Impact factor: 8.545

4.  Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques.

Authors:  Arturo Aquino; Manuel Emilio Gegundez-Arias; Diego Marin
Journal:  IEEE Trans Med Imaging       Date:  2010-06-17       Impact factor: 10.048

5.  Deformable segmentation via sparse representation and dictionary learning.

Authors:  Shaoting Zhang; Yiqiang Zhan; Dimitris N Metaxas
Journal:  Med Image Anal       Date:  2012-08-23       Impact factor: 8.545

6.  An efficient optimization framework for multi-region segmentation based on Lagrangian duality.

Authors:  Johannes Ulén; Petter Strandmark; Fredrik Kahl
Journal:  IEEE Trans Med Imaging       Date:  2012-09-10       Impact factor: 10.048

7.  Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment.

Authors:  Gopal Datt Joshi; Jayanthi Sivaswamy; S R Krishnadas
Journal:  IEEE Trans Med Imaging       Date:  2011-05-02       Impact factor: 10.048

8.  Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features.

Authors:  Michael D Abràmoff; Wallace L M Alward; Emily C Greenlee; Lesya Shuba; Chan Y Kim; John H Fingert; Young H Kwon
Journal:  Invest Ophthalmol Vis Sci       Date:  2007-04       Impact factor: 4.799

9.  Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level sets.

Authors:  H Yu; E S Barriga; C Agurto; S Echegaray; M S Pattichis; W Bauman; P Soliz
Journal:  IEEE Trans Inf Technol Biomed       Date:  2012-05-10
  9 in total
  7 in total

1.  Multimodal Segmentation of Optic Disc and Cup From SD-OCT and Color Fundus Photographs Using a Machine-Learning Graph-Based Approach.

Authors:  Mohammad Saleh Miri; Michael D Abràmoff; Kyungmoo Lee; Meindert Niemeijer; Jui-Kai Wang; Young H Kwon; Mona K Garvin
Journal:  IEEE Trans Med Imaging       Date:  2015-03-13       Impact factor: 10.048

2.  A machine-learning graph-based approach for 3D segmentation of Bruch's membrane opening from glaucomatous SD-OCT volumes.

Authors:  Mohammad Saleh Miri; Michael D Abràmoff; Young H Kwon; Milan Sonka; Mona K Garvin
Journal:  Med Image Anal       Date:  2017-05-06       Impact factor: 8.545

3.  Joint optic disc and cup segmentation based on densely connected depthwise separable convolution deep network.

Authors:  Bingyan Liu; Daru Pan; Hui Song
Journal:  BMC Med Imaging       Date:  2021-01-28       Impact factor: 1.930

4.  Contrast based circular approximation for accurate and robust optic disc segmentation in retinal images.

Authors:  Jose Sigut; Omar Nunez; Francisco Fumero; Marta Gonzalez; Rafael Arnay
Journal:  PeerJ       Date:  2017-09-07       Impact factor: 2.984

5.  Statistical atlas-based descriptor for an early detection of optic disc abnormalities.

Authors:  Fantin Girard; Conrad Kavalec; Farida Cheriet
Journal:  J Med Imaging (Bellingham)       Date:  2018-03-06

6.  A Retrospective Comparison of Deep Learning to Manual Annotations for Optic Disc and Optic Cup Segmentation in Fundus Photographs.

Authors:  Huazhu Fu; Fei Li; Yanwu Xu; Jingan Liao; Jian Xiong; Jianbing Shen; Jiang Liu; Xiulan Zhang
Journal:  Transl Vis Sci Technol       Date:  2020-06-24       Impact factor: 3.283

7.  A Precise Method to Evaluate 360 Degree Measures of Optic Cup and Disc Morphology in an African American Cohort and Its Genetic Applications.

Authors:  Victoria Addis; Min Chen; Richard Zorger; Rebecca Salowe; Ebenezer Daniel; Roy Lee; Maxwell Pistilli; Jinpeng Gao; Maureen G Maguire; Lilian Chan; Harini V Gudiseva; Selam Zenebe-Gete; Sayaka Merriam; Eli J Smith; Revell Martin; Candace Parker Ostroff; James C Gee; Qi N Cui; Eydie Miller-Ellis; Joan M O'Brien; Prithvi S Sankar
Journal:  Genes (Basel)       Date:  2021-12-09       Impact factor: 4.096

  7 in total

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