Literature DB >> 19163471

Elliptical local vessel density: a fast and robust quality metric for retinal images.

L Giancardo1, M D Abramoff, E Chaum, T P Karnowski, F Meriaudeau, K W Tobin.   

Abstract

A great effort of the research community is geared towards the creation of an automatic screening system able to promptly detect diabetic retinopathy with the use of fundus cameras. In addition, there are some documented approaches for automatically judging the image quality. We propose a new set of features independent of field of view or resolution to describe the morphology of the patient's vessels. Our initial results suggest that these features can be used to estimate the image quality in a time one order of magnitude shorter than previous techniques.

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Year:  2008        PMID： 19163471     DOI： 10.1109/IEMBS.2008.4649968

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN： 1557-170X


  8 in total

Review 1.  Automated quality assessment of retinal fundus photos.

Authors:  Jan Paulus; Jörg Meier; Rüdiger Bock; Joachim Hornegger; Georg Michelson
Journal:  Int J Comput Assist Radiol Surg       Date:  2010-05-19       Impact factor: 2.924

2.  Validating retinal fundus image analysis algorithms: issues and a proposal.

Authors:  Emanuele Trucco; Alfredo Ruggeri; Thomas Karnowski; Luca Giancardo; Edward Chaum; Jean Pierre Hubschman; Bashir Al-Diri; Carol Y Cheung; Damon Wong; Michael Abràmoff; Gilbert Lim; Dinesh Kumar; Philippe Burlina; Neil M Bressler; Herbert F Jelinek; Fabrice Meriaudeau; Gwénolé Quellec; Tom Macgillivray; Bal Dhillon
Journal:  Invest Ophthalmol Vis Sci       Date:  2013-05-01       Impact factor: 4.799

3.  Automated Quality Assessment of Colour Fundus Images for Diabetic Retinopathy Screening in Telemedicine.

Authors:  Sajib Kumar Saha; Basura Fernando; Jorge Cuadros; Di Xiao; Yogesan Kanagasingam
Journal:  J Digit Imaging       Date:  2018-12       Impact factor: 4.056

4.  A health insurance portability and accountability act-compliant ocular telehealth network for the remote diagnosis and management of diabetic retinopathy.

Authors:  Yaqin Li; Thomas P Karnowski; Kenneth W Tobin; Luca Giancardo; Scott Morris; Sylvia E Sparrow; Seema Garg; Karen Fox; Edward Chaum
Journal:  Telemed J E Health       Date:  2011-08-05       Impact factor: 3.536

5.  Deep Learning for Image Quality Assessment of Fundus Images in Retinopathy of Prematurity.

Authors:  Aaron S Coyner; Ryan Swan; James M Brown; Jayashree Kalpathy-Cramer; Sang Jin Kim; J Peter Campbell; Karyn E Jonas; Susan Ostmo; R V Paul Chan; Michael F Chiang
Journal:  AMIA Annu Symp Proc       Date:  2018-12-05

6.  Fractal dimension of retinal vasculature as an image quality metric for automated fundus image analysis systems.

Authors:  Xingzheng Lyu; Purvish Jajal; Muhammad Zeeshan Tahir; Sanyuan Zhang
Journal:  Sci Rep       Date:  2022-07-13       Impact factor: 4.996

7.  Towards Stroke Biomarkers on Fundus Retinal Imaging: A Comparison Between Vasculature Embeddings and General Purpose Convolutional Neural Networks.

Authors:  Ivan Coronado; Rania Abdelkhaleq; Juntao Yan; Sergio Salazar Marioni; Amanda Jagolino-Cole; Roomasa Channa; Samiksha Pachade; Sunil A Sheth; Luca Giancardo
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2021-11

8.  Feature-Based Retinal Image Registration Using D-Saddle Feature.

Authors:  Roziana Ramli; Mohd Yamani Idna Idris; Khairunnisa Hasikin; Noor Khairiah A Karim; Ainuddin Wahid Abdul Wahab; Ismail Ahmedy; Fatimah Ahmedy; Nahrizul Adib Kadri; Hamzah Arof
Journal:  J Healthc Eng       Date:  2017-10-24       Impact factor: 2.682
  8 in total

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