Literature DB >> 24290931

Computer-aided diagnosis of diabetic retinopathy: a review.

Muthu Rama Krishnan Mookiah1, U Rajendra Acharya, Chua Kuang Chua, Choo Min Lim, E Y K Ng, Augustinus Laude.   

Abstract

Diabetes mellitus may cause alterations in the retinal microvasculature leading to diabetic retinopathy. Unchecked, advanced diabetic retinopathy may lead to blindness. It can be tedious and time consuming to decipher subtle morphological changes in optic disk, microaneurysms, hemorrhage, blood vessels, macula, and exudates through manual inspection of fundus images. A computer aided diagnosis system can significantly reduce the burden on the ophthalmologists and may alleviate the inter and intra observer variability. This review discusses the available methods of various retinal feature extractions and automated analysis.
© 2013 Elsevier Ltd. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Computer-aided diagnosis; Fundus imaging; Image processing; Pattern classification; Retina; Retinopathy

Mesh：

Year:  2013        PMID： 24290931     DOI： 10.1016/j.compbiomed.2013.10.007

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN： 0010-4825            Impact factor:   4.589


  57 in total

1.  Automated pathologies detection in retina digital images based on complex continuous wavelet transform phase angles.

Authors:  Salim Lahmiri; Christian S Gargour; Marcel Gabrea
Journal:  Healthc Technol Lett       Date:  2014-11-06

2.  An exudate detection method for diagnosis risk of diabetic macular edema in retinal images using feature-based and supervised classification.

Authors:  D Marin; M E Gegundez-Arias; B Ponte; F Alvarez; J Garrido; C Ortega; M J Vasallo; J M Bravo
Journal:  Med Biol Eng Comput       Date:  2018-01-10       Impact factor: 2.602

Review 3.  Optic disc detection in retinal fundus images using gravitational law-based edge detection.

Authors:  Mohammad Alshayeji; Suood Abdulaziz Al-Roomi; Sa'ed Abed
Journal:  Med Biol Eng Comput       Date:  2016-09-16       Impact factor: 2.602

4.  Classification of diabetes maculopathy images using data-adaptive neuro-fuzzy inference classifier.

Authors:  Sulaimon Ibrahim; Pradeep Chowriappa; Sumeet Dua; U Rajendra Acharya; Kevin Noronha; Sulatha Bhandary; Hatwib Mugasa
Journal:  Med Biol Eng Comput       Date:  2015-06-25       Impact factor: 2.602

5.  mHealth App for iOS to Help in Diagnostic Decision in Ophthalmology to Primary Care Physicians.

Authors:  Marta Manovel López; Miguel Maldonado López; Isabel de la Torre Díez; José Carlos Pastor Jimeno; Miguel López-Coronado
Journal:  J Med Syst       Date:  2017-03-31       Impact factor: 4.460

6.  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

7.  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

8.  Tracking and diameter estimation of retinal vessels using Gaussian process and Radon transform.

Authors:  Masoud Elhami Asl; Navid Alemi Koohbanani; Alejandro F Frangi; Ali Gooya
Journal:  J Med Imaging (Bellingham)       Date:  2017-09-12

9.  Automated segmentation of hyperreflective foci in spectral domain optical coherence tomography with diabetic retinopathy.

Authors:  Idowu Paul Okuwobi; Wen Fan; Chenchen Yu; Songtao Yuan; Qinghuai Liu; Yuhan Zhang; Bekalo Loza; Qiang Chen
Journal:  J Med Imaging (Bellingham)       Date:  2018-02-06

10.  Automated detection of mild and multi-class diabetic eye diseases using deep learning.

Authors:  Rubina Sarki; Khandakar Ahmed; Hua Wang; Yanchun Zhang
Journal:  Health Inf Sci Syst       Date:  2020-10-08
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