Literature DB >> 19954928

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

Daniel Welfer1, Jacob Scharcanski, Diane Ruschel Marinho.   

Abstract

The detection of exudates is a prerequisite for detecting and grading severe retinal lesions, like the diabetic macular edema. In this work, we present a new method based on mathematical morphology for detecting exudates in color eye fundus images. A preliminary evaluation of the proposed method performance on a known public database, namely DIARETDB1, indicates that it can achieve an average sensitivity of 70.48%, and an average specificity of 98.84%. Comparing to other recent automatic methods available in the literature, our proposed approach potentially can obtain better exudate detection results in terms of sensitivity and specificity. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

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Year:  2009        PMID: 19954928     DOI: 10.1016/j.compmedimag.2009.10.001

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


  8 in total

1.  A Multi-Anatomical Retinal Structure Segmentation System for Automatic Eye Screening Using Morphological Adaptive Fuzzy Thresholding.

Authors:  Jasem Almotiri; Khaled Elleithy; Abdelrahman Elleithy
Journal:  IEEE J Transl Eng Health Med       Date:  2018-05-17       Impact factor: 3.316

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

Authors:  Syed Ali Gohar Naqvi; Hafiz Muhammad Faisal Zafar; Ihsanul Haq
Journal:  Int J Ophthalmol       Date:  2017-07-18       Impact factor: 1.779

3.  Detection of exudates in fundus photographs with imbalanced learning using conditional generative adversarial network.

Authors:  Rui Zheng; Lei Liu; Shulin Zhang; Chun Zheng; Filiz Bunyak; Ronald Xu; Bin Li; Mingzhai Sun
Journal:  Biomed Opt Express       Date:  2018-09-14       Impact factor: 3.732

Review 4.  A review on automatic analysis techniques for color fundus photographs.

Authors:  Renátó Besenczi; János Tóth; András Hajdu
Journal:  Comput Struct Biotechnol J       Date:  2016-10-06       Impact factor: 7.271

5.  Semi-automated quantification of hard exudates in colour fundus photographs diagnosed with diabetic retinopathy.

Authors:  Abhilash Goud Marupally; Kiran Kumar Vupparaboina; Hari Kumar Peguda; Ashutosh Richhariya; Soumya Jana; Jay Chhablani
Journal:  BMC Ophthalmol       Date:  2017-09-20       Impact factor: 2.209

6.  Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa.

Authors:  Muhammad Arsalan; Na Rae Baek; Muhammad Owais; Tahir Mahmood; Kang Ryoung Park
Journal:  Sensors (Basel)       Date:  2020-06-18       Impact factor: 3.576

7.  Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms.

Authors:  Parham Khojasteh; Behzad Aliahmad; Dinesh K Kumar
Journal:  BMC Ophthalmol       Date:  2018-11-06       Impact factor: 2.209

8.  Advancing bag-of-visual-words representations for lesion classification in retinal images.

Authors:  Ramon Pires; Herbert F Jelinek; Jacques Wainer; Eduardo Valle; Anderson Rocha
Journal:  PLoS One       Date:  2014-06-02       Impact factor: 3.240

  8 in total

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