Literature DB >> 23686810

Automated analysis of diabetic retinopathy images: principles, recent developments, and emerging trends.

Baoxin Li1, Helen K Li.   

Abstract

Diabetic retinopathy (DR) is a vision-threatening complication of diabetes. Timely diagnosis and intervention are essential for treatment that reduces the risk of vision loss. A good color retinal (fundus) photograph can be used as a surrogate for face-to-face evaluation of DR. The use of computers to assist or fully automate DR evaluation from retinal images has been studied for many years. Early work showed promising results for algorithms in detecting and classifying DR pathology. Newer techniques include those that adapt machine learning technology to DR image analysis. Challenges remain, however, that must be overcome before fully automatic DR detection and analysis systems become practical clinical tools.

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Year:  2013        PMID: 23686810     DOI: 10.1007/s11892-013-0393-9

Source DB:  PubMed          Journal:  Curr Diab Rep        ISSN: 1534-4827            Impact factor:   4.810


  55 in total

1.  Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response.

Authors:  A Hoover; V Kouznetsova; M Goldbaum
Journal:  IEEE Trans Med Imaging       Date:  2000-03       Impact factor: 10.048

2.  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

3.  Active learning for an efficient training strategy of computer-aided diagnosis systems: application to diabetic retinopathy screening.

Authors:  C I Sánchez; M Niemeijer; M D Abràmoff; B van Ginneken
Journal:  Med Image Comput Comput Assist Interv       Date:  2010

4.  Automatic detection of microaneurysms in color fundus images.

Authors:  Thomas Walter; Pascale Massin; Ali Erginay; Richard Ordonez; Clotilde Jeulin; Jean-Claude Klein
Journal:  Med Image Anal       Date:  2007-05-26       Impact factor: 8.545

5.  A multiple-instance learning framework for diabetic retinopathy screening.

Authors:  Gwénolé Quellec; Mathieu Lamard; Michael D Abràmoff; Etienne Decencière; Bruno Lay; Ali Erginay; Béatrice Cochener; Guy Cazuguel
Journal:  Med Image Anal       Date:  2012-07-06       Impact factor: 8.545

Review 6.  Progress towards automated diabetic ocular screening: a review of image analysis and intelligent systems for diabetic retinopathy.

Authors:  T Teng; M Lefley; D Claremont
Journal:  Med Biol Eng Comput       Date:  2002-01       Impact factor: 2.602

7.  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

8.  Screening for diabetic retinopathy. The wide-angle retinal camera.

Authors:  J A Pugh; J M Jacobson; W A Van Heuven; J A Watters; M R Tuley; D R Lairson; R J Lorimor; A S Kapadia; R Velez
Journal:  Diabetes Care       Date:  1993-06       Impact factor: 19.112

9.  Detection of anatomic structures in human retinal imagery.

Authors:  Kenneth W Tobin; Edward Chaum; V Priya Govindasamy; Thomas P Karnowski
Journal:  IEEE Trans Med Imaging       Date:  2007-12       Impact factor: 10.048

10.  Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes: response to Abramoff et al.

Authors:  John A Olson; Peter F Sharp; Alan Fleming; Sam Philip
Journal:  Diabetes Care       Date:  2008-08       Impact factor: 19.112

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  6 in total

1.  Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features.

Authors:  Qaisar Abbas; Irene Fondon; Auxiliadora Sarmiento; Soledad Jiménez; Pedro Alemany
Journal:  Med Biol Eng Comput       Date:  2017-03-28       Impact factor: 2.602

2.  MIRank-KNN: multiple-instance retrieval of clinically relevant diabetic retinopathy images.

Authors:  Parag Shridhar Chandakkar; Ragav Venkatesan; Baoxin Li
Journal:  J Med Imaging (Bellingham)       Date:  2017-09-01

Review 3.  Automated retinal image analysis for diabetic retinopathy in telemedicine.

Authors:  Dawn A Sim; Pearse A Keane; Adnan Tufail; Catherine A Egan; Lloyd Paul Aiello; Paolo S Silva
Journal:  Curr Diab Rep       Date:  2015-03       Impact factor: 5.430

Review 4.  Machine Learning and Data Mining Methods in Diabetes Research.

Authors:  Ioannis Kavakiotis; Olga Tsave; Athanasios Salifoglou; Nicos Maglaveras; Ioannis Vlahavas; Ioanna Chouvarda
Journal:  Comput Struct Biotechnol J       Date:  2017-01-08       Impact factor: 7.271

Review 5.  Automated detection of diabetic retinopathy in retinal images.

Authors:  Carmen Valverde; Maria Garcia; Roberto Hornero; Maria I Lopez-Galvez
Journal:  Indian J Ophthalmol       Date:  2016-01       Impact factor: 1.848

Review 6.  The Evolution of Diabetic Retinopathy Screening Programmes: A Chronology of Retinal Photography from 35 mm Slides to Artificial Intelligence.

Authors:  Josef Huemer; Siegfried K Wagner; Dawn A Sim
Journal:  Clin Ophthalmol       Date:  2020-07-20
  6 in total

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