Literature DB >> 28730125

Hard exudates referral system in eye fundus utilizing speeded up robust features.

Syed Ali Gohar Naqvi1, Hafiz Muhammad Faisal Zafar1, Ihsanul Haq1.   

Abstract

In the paper a referral system to assist the medical experts in the screening/referral of diabetic retinopathy is suggested. The system has been developed by a sequential use of different existing mathematical techniques. These techniques involve speeded up robust features (SURF), K-means clustering and visual dictionaries (VD). Three databases are mixed to test the working of the system when the sources are dissimilar. When experiments were performed an area under the curve (AUC) of 0.9343 was attained. The results acquired from the system are promising.

Entities:  

Keywords:  eye; fundus; referral system; speeded up robust features; visual dictionaries

Year:  2017        PMID: 28730125      PMCID: PMC5514284          DOI: 10.18240/ijo.2017.07.24

Source DB:  PubMed          Journal:  Int J Ophthalmol        ISSN: 2222-3959            Impact factor:   1.779


  12 in total

1.  A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features.

Authors:  Diego Marin; Arturo Aquino; Manuel Emilio Gegundez-Arias; José Manuel Bravo
Journal:  IEEE Trans Med Imaging       Date:  2010-08-09       Impact factor: 10.048

2.  General retinal vessel segmentation using regularization-based multiconcavity modeling.

Authors:  Benson S Y Lam; Yongsheng Gao; Alan Wee-Chung Liew
Journal:  IEEE Trans Med Imaging       Date:  2010-03-18       Impact factor: 10.048

3.  Retinal blood vessel segmentation using line operators and support vector classification.

Authors:  Elisa Ricci; Renzo Perfetti
Journal:  IEEE Trans Med Imaging       Date:  2007-10       Impact factor: 10.048

4.  An active contour model for segmenting and measuring retinal vessels.

Authors:  Bashir Al-Diri; Andrew Hunter; David Steel
Journal:  IEEE Trans Med Imaging       Date:  2009-03-24       Impact factor: 10.048

5.  Comparison of logistic regression and neural network classifiers in the detection of hard exudates in retinal images.

Authors:  Maria Garcia; Carmen Valverde; Maria I Lopez; Jesus Poza; Roberto Hornero
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2013

6.  A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images.

Authors:  Daniel Welfer; Jacob Scharcanski; Diane Ruschel Marinho
Journal:  Comput Med Imaging Graph       Date:  2009-12-01       Impact factor: 4.790

7.  Evaluation of automated fundus photograph analysis algorithms for detecting microaneurysms, haemorrhages and exudates, and of a computer-assisted diagnostic system for grading diabetic retinopathy.

Authors:  B Dupas; T Walter; A Erginay; R Ordonez; N Deb-Joardar; P Gain; J-C Klein; P Massin
Journal:  Diabetes Metab       Date:  2010-03-10       Impact factor: 6.041

8.  Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods.

Authors:  Akara Sopharak; Bunyarit Uyyanonvara; Sarah Barman; Thomas H Williamson
Journal:  Comput Med Imaging Graph       Date:  2008-10-18       Impact factor: 4.790

9.  Referral system for hard exudates in eye fundus.

Authors:  Syed Ali Gohar Naqvi; Muhammad Faisal Zafar; Ihsan ul Haq
Journal:  Comput Biol Med       Date:  2015-07-14       Impact factor: 4.589

10.  Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering.

Authors:  Akara Sopharak; Bunyarit Uyyanonvara; Sarah Barman
Journal:  Sensors (Basel)       Date:  2009-03-24       Impact factor: 3.576

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