Literature DB >> 20546966

Automated segmentation of optic disc region on retinal fundus photographs: Comparison of contour modeling and pixel classification methods.

Chisako Muramatsu1, Toshiaki Nakagawa, Akira Sawada, Yuji Hatanaka, Takeshi Hara, Tetsuya Yamamoto, Hiroshi Fujita.   

Abstract

The automatic determination of the optic disc area in retinal fundus images can be useful for calculation of the cup-to-disc (CD) ratio in the glaucoma screening. We compared three different methods that employed active contour model (ACM), fuzzy c-mean (FCM) clustering, and artificial neural network (ANN) for the segmentation of the optic disc regions. The results of these methods were evaluated using new databases that included the images captured by different camera systems. The average measures of overlap between the disc regions determined by an ophthalmologist and by using the ACM (0.88 and 0.87 for two test datasets) and ANN (0.88 and 0.89) methods were slightly higher than that by using FCM (0.86 and 0.86) method. These results on the unknown datasets were comparable with those of the resubstitution test; this indicates the generalizability of these methods. The differences in the vertical diameters, which are often used for CD ratio calculation, determined by the proposed methods and based on the ophthalmologist's outlines were even smaller than those in the case of the measure of overlap. The proposed methods can be useful for automatic determination of CD ratios. 2011 Elsevier Ireland Ltd. All rights reserved.

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Year:  2010        PMID: 20546966     DOI: 10.1016/j.cmpb.2010.04.006

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  8 in total

1.  Validating retinal fundus image analysis algorithms: issues and a proposal.

Authors:  Emanuele Trucco; Alfredo Ruggeri; Thomas Karnowski; Luca Giancardo; Edward Chaum; Jean Pierre Hubschman; Bashir Al-Diri; Carol Y Cheung; Damon Wong; Michael Abràmoff; Gilbert Lim; Dinesh Kumar; Philippe Burlina; Neil M Bressler; Herbert F Jelinek; Fabrice Meriaudeau; Gwénolé Quellec; Tom Macgillivray; Bal Dhillon
Journal:  Invest Ophthalmol Vis Sci       Date:  2013-05-01       Impact factor: 4.799

2.  Adaptive optics imaging of healthy and abnormal regions of retinal nerve fiber bundles of patients with glaucoma.

Authors:  Monica F Chen; Toco Y P Chui; Paula Alhadeff; Richard B Rosen; Robert Ritch; Alfredo Dubra; Donald C Hood
Journal:  Invest Ophthalmol Vis Sci       Date:  2015-01-08       Impact factor: 4.799

Review 3.  Retinal imaging as a source of biomarkers for diagnosis, characterization and prognosis of chronic illness or long-term conditions.

Authors:  T J MacGillivray; E Trucco; J R Cameron; B Dhillon; J G Houston; E J R van Beek
Journal:  Br J Radiol       Date:  2014-06-17       Impact factor: 3.039

4.  An empirical study of preprocessing techniques with convolutional neural networks for accurate detection of chronic ocular diseases using fundus images.

Authors:  Veena Mayya; Sowmya Kamath S; Uma Kulkarni; Divyalakshmi Kaiyoor Surya; U Rajendra Acharya
Journal:  Appl Intell (Dordr)       Date:  2022-04-30       Impact factor: 5.019

5.  Mean curvature and texture constrained composite weighted random walk algorithm for optic disc segmentation towards glaucoma screening.

Authors:  Rashmi Panda; N B Puhan; Ganapati Panda
Journal:  Healthc Technol Lett       Date:  2018-01-05

6.  Automatic Glaucoma Detection Method Applying a Statistical Approach to Fundus Images.

Authors:  Anindita Septiarini; Dyna M Khairina; Awang H Kridalaksana; Hamdani Hamdani
Journal:  Healthc Inform Res       Date:  2018-01-31

7.  Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice.

Authors:  Anna S Mursch-Edlmayr; Wai Siene Ng; Alberto Diniz-Filho; David C Sousa; Louis Arnold; Matthew B Schlenker; Karla Duenas-Angeles; Pearse A Keane; Jonathan G Crowston; Hari Jayaram
Journal:  Transl Vis Sci Technol       Date:  2020-10-15       Impact factor: 3.283

8.  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

  8 in total

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