Literature DB >> 19616920

Algorithms for digital image processing in diabetic retinopathy.

R J Winder1, P J Morrow, I N McRitchie, J R Bailie, P M Hart.   

Abstract

This work examined recent literature on digital image processing in the field of diabetic retinopathy. Algorithms were categorized into 5 steps (preprocessing; localization and segmentation of the optic disk; segmentation of the retinal vasculature; localization of the macula and fovea; localization and segmentation of retinopathy). The variety of outcome measures, use of a gold standard or ground truth, data sample sizes and the use of image databases is discussed. It is intended that our classification of algorithms into a small number of categories, definition of terms and discussion of evolving techniques will provide guidance to algorithm designers for diabetic retinopathy.

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Year:  2009        PMID: 19616920     DOI: 10.1016/j.compmedimag.2009.06.003

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  22 in total

1.  Mapping the 3D Connectivity of the Rat Inner Retinal Vascular Network Using OCT Angiography.

Authors:  Conor Leahy; Harsha Radhakrishnan; Geoffrey Weiner; Jeffrey L Goldberg; Vivek J Srinivasan
Journal:  Invest Ophthalmol Vis Sci       Date:  2015-09       Impact factor: 4.799

2.  Computer-aided diabetic retinopathy detection using trace transforms on digital fundus images.

Authors:  Karthikeyan Ganesan; Roshan Joy Martis; U Rajendra Acharya; Chua Kuang Chua; Lim Choo Min; E Y K Ng; Augustinus Laude
Journal:  Med Biol Eng Comput       Date:  2014-06-24       Impact factor: 2.602

3.  Accurate and reliable segmentation of the optic disc in digital fundus images.

Authors:  Andrea Giachetti; Lucia Ballerini; Emanuele Trucco
Journal:  J Med Imaging (Bellingham)       Date:  2014-07-14

4.  QUANTITATIVE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY FEATURES FOR OBJECTIVE CLASSIFICATION AND STAGING OF DIABETIC RETINOPATHY.

Authors:  Minhaj Alam; Yue Zhang; Jennifer I Lim; Robison V P Chan; Min Yang; Xincheng Yao
Journal:  Retina       Date:  2018-10-31       Impact factor: 4.256

5.  An improved retinal vessel segmentation method based on high level features for pathological images.

Authors:  Razieh Ganjee; Reza Azmi; Behrouz Gholizadeh
Journal:  J Med Syst       Date:  2014-07-19       Impact factor: 4.460

6.  Automatic optic disk detection in retinal images using hybrid vessel phase portrait analysis.

Authors:  Nittaya Muangnak; Pakinee Aimmanee; Stanislav Makhanov
Journal:  Med Biol Eng Comput       Date:  2017-08-24       Impact factor: 2.602

7.  A multiscale decomposition approach to detect abnormal vasculature in the optic disc.

Authors:  Carla Agurto; Honggang Yu; Victor Murray; Marios S Pattichis; Sheila Nemeth; Simon Barriga; Peter Soliz
Journal:  Comput Med Imaging Graph       Date:  2015-01-20       Impact factor: 4.790

8.  Multimodal retinal vessel segmentation from spectral-domain optical coherence tomography and fundus photography.

Authors:  Zhihong Hu; Meindert Niemeijer; Michael D Abràmoff; Mona K Garvin
Journal:  IEEE Trans Med Imaging       Date:  2012-06-29       Impact factor: 10.048

9.  Landmark matching based retinal image alignment by enforcing sparsity in correspondence matrix.

Authors:  Yuanjie Zheng; Ebenezer Daniel; Allan A Hunter; Rui Xiao; Jianbin Gao; Hongsheng Li; Maureen G Maguire; David H Brainard; James C Gee
Journal:  Med Image Anal       Date:  2013-10-26       Impact factor: 8.545

10.  Automated Identification of Referable Retinal Pathology in Teleophthalmology Setting.

Authors:  Qitong Gao; Joshua Amason; Scott Cousins; Miroslav Pajic; Majda Hadziahmetovic
Journal:  Transl Vis Sci Technol       Date:  2021-05-03       Impact factor: 3.283

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