Literature DB >> 17947087

The automatic detection of the optic disc location in retinal images using optic disc location regression.

Michael D Abràmoff1, Meindert Niemeijer.   

Abstract

The automatic detection of the position of the optic disc is an important step in the automatic analysis of retinal images. A method to detect the approximate position of the optic disc using kNN regression is presented. The method starts by building a regression model of the optic disc position. Using a prior vessel segmentation all vessel pixels are searched for those which are inside the optic disc according to the regression model. The regression output is blurred to handle noise. The point which is closest to the middle of the optic disc is chosen. The method was tested on 1000 screening images and was able to find the correct position in 99.9% of all cases.

Mesh:

Year:  2006        PMID: 17947087      PMCID: PMC2739589          DOI: 10.1109/IEMBS.2006.259622

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


  5 in total

1.  Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels.

Authors:  Adam Hoover; Michael Goldbaum
Journal:  IEEE Trans Med Imaging       Date:  2003-08       Impact factor: 10.048

2.  Ridge-based vessel segmentation in color images of the retina.

Authors:  Joes Staal; Michael D Abràmoff; Meindert Niemeijer; Max A Viergever; Bram van Ginneken
Journal:  IEEE Trans Med Imaging       Date:  2004-04       Impact factor: 10.048

3.  Detection of optic disc in retinal images by means of a geometrical model of vessel structure.

Authors:  M Foracchia; E Grisan; A Ruggeri
Journal:  IEEE Trans Med Imaging       Date:  2004-10       Impact factor: 10.048

4.  Automatic detection of red lesions in digital color fundus photographs.

Authors:  Meindert Niemeijer; Bram van Ginneken; Joes Staal; Maria S A Suttorp-Schulten; Michael D Abràmoff
Journal:  IEEE Trans Med Imaging       Date:  2005-05       Impact factor: 10.048

5.  Web-based screening for diabetic retinopathy in a primary care population: the EyeCheck project.

Authors:  Michael D Abramoff; Maria S A Suttorp-Schulten
Journal:  Telemed J E Health       Date:  2005-12       Impact factor: 3.536

  5 in total
  9 in total

1.  Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application.

Authors:  Valentina Bellemo; Gilbert Lim; Tyler Hyungtaek Rim; Gavin S W Tan; Carol Y Cheung; SriniVas Sadda; Ming-Guang He; Adnan Tufail; Mong Li Lee; Wynne Hsu; Daniel Shu Wei Ting
Journal:  Curr Diab Rep       Date:  2019-07-31       Impact factor: 4.810

2.  Automatic differentiation of color fundus images containing drusen or exudates using a contextual spatial pyramid approach.

Authors:  Mark J J P van Grinsven; Thomas Theelen; Leonard Witkamp; Job van der Heijden; Johannes P H van de Ven; Carel B Hoyng; Bram van Ginneken; Clara I Sánchez
Journal:  Biomed Opt Express       Date:  2016-02-02       Impact factor: 3.732

3.  Automated early detection of diabetic retinopathy.

Authors:  Michael D Abràmoff; Joseph M Reinhardt; Stephen R Russell; James C Folk; Vinit B Mahajan; Meindert Niemeijer; Gwénolé Quellec
Journal:  Ophthalmology       Date:  2010-06       Impact factor: 12.079

4.  Optic disc detection in color fundus images using ant colony optimization.

Authors:  Carla Pereira; Luís Gonçalves; Manuel Ferreira
Journal:  Med Biol Eng Comput       Date:  2012-11-19       Impact factor: 2.602

5.  Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features.

Authors:  Michael D Abràmoff; Wallace L M Alward; Emily C Greenlee; Lesya Shuba; Chan Y Kim; John H Fingert; Young H Kwon
Journal:  Invest Ophthalmol Vis Sci       Date:  2007-04       Impact factor: 4.799

6.  Vessel boundary delineation on fundus images using graph-based approach.

Authors:  Xiayu Xu; Meindert Niemeijer; Qi Song; Milan Sonka; Mona K Garvin; Joseph M Reinhardt; Michael D Abràmoff
Journal:  IEEE Trans Med Imaging       Date:  2011-01-06       Impact factor: 10.048

7.  Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis.

Authors:  Meindert Niemeijer; Bram van Ginneken; Stephen R Russell; Maria S A Suttorp-Schulten; Michael D Abràmoff
Journal:  Invest Ophthalmol Vis Sci       Date:  2007-05       Impact factor: 4.799

Review 8.  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

9.  Detecting optic disc on asians by multiscale gaussian filtering.

Authors:  Bob Zhang; Jane You; Fakhri Karray
Journal:  Int J Biomed Imaging       Date:  2012-06-26
  9 in total

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