Literature DB >> 20550999

Fast localization of the optic disc using projection of image features.

Ahmed E Mahfouz1, Ahmed S Fahmy.   

Abstract

Optic Disc (OD) localization is an important pre-processing step that significantly simplifies subsequent segmentation of the OD and other retinal structures. Current OD localization techniques suffer from impractically-high computation times (few minutes per image). In this work, we present a fast technique that requires less than a second to localize the OD. The technique is based upon obtaining two projections of certain image features that encode the x- and y- coordinates of the OD. The resulting 1-D projections are then searched to determine the location of the OD. This avoids searching the 2-D image space and, thus, enhances the speed of the OD localization process. Image features such as retinal vessels orientation and the OD brightness are used in the current method. Four publicly-available databases, including STARE and DRIVE, are used to evaluate the proposed technique. The OD was successfully located in 330 images out of 340 images (97%) with an average computation time of 0.65 s.

Mesh:

Year:  2010        PMID: 20550999     DOI: 10.1109/TIP.2010.2052280

Source DB:  PubMed          Journal:  IEEE Trans Image Process        ISSN: 1057-7149            Impact factor:   10.856


  6 in total

1.  A new and effective method for human retina optic disc segmentation with fuzzy clustering method based on active contour model.

Authors:  Ahmad S Abdullah; Javad Rahebi; Yasa Ekşioğlu Özok; Mohanad Aljanabi
Journal:  Med Biol Eng Comput       Date:  2019-08-24       Impact factor: 2.602

2.  PCA-based localization approach for segmentation of optic disc.

Authors:  Varun P Gopi; M S Anjali; S Issac Niwas
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-09-30       Impact factor: 2.924

3.  Automatic optic disk detection in retinal images using hybrid vessel phase portrait analysis.

Authors:  Nittaya Muangnak; Pakinee Aimmanee; Stanislav Makhanov
Journal:  Med Biol Eng Comput       Date:  2017-08-24       Impact factor: 2.602

Review 4.  Automated analysis of diabetic retinopathy images: principles, recent developments, and emerging trends.

Authors:  Baoxin Li; Helen K Li
Journal:  Curr Diab Rep       Date:  2013-08       Impact factor: 4.810

5.  Regional Image Features Model for Automatic Classification between Normal and Glaucoma in Fundus and Scanning Laser Ophthalmoscopy (SLO) Images.

Authors:  Muhammad Salman Haleem; Liangxiu Han; Jano van Hemert; Alan Fleming; Louis R Pasquale; Paolo S Silva; Brian J Song; Lloyd Paul Aiello
Journal:  J Med Syst       Date:  2016-04-16       Impact factor: 4.460

6.  Localization and segmentation of optic disc in retinal images using circular Hough transform and grow-cut algorithm.

Authors:  Muhammad Abdullah; Muhammad Moazam Fraz; Sarah A Barman
Journal:  PeerJ       Date:  2016-05-10       Impact factor: 2.984

  6 in total

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