Literature DB >> 17243590

Segmentation of the optic disc, macula and vascular arch in fundus photographs.

Meindert Niemeijer1, Michael D Abràmoff, Bram van Ginneken.   

Abstract

An automatic system is presented to find the location of the major anatomical structures in color fundus photographs; the optic disc, the macula, and the vascular arch. These structures are found by fitting a single point-distribution-model to the image, that contains points on each structure. The method can handle optic disc and macula centered images of both the left and the right eye. The system uses a cost function, which is based on a combination of both global and local cues, to find the correct position of the model points. The global terms in the cost function are based on the orientation and width of the vascular pattern in the image. The local term is derived from the image structure around the points of the model. To optimize the fit of the point-distribution-model to an image, a sophisticated combination of optimization processes is proposed which combines optimization in the parameter space of the model and in the image space, where points are moved directly. Experimental results are presented demonstrating that our specific choices for the cost function components and optimization scheme are needed to obtain good results. The system was developed and trained on a set of 500 screening images, and tested on a completely independent set of 500 screening images. In addition to this the system was also tested on a separate set of 100 pathological images. In the screening set it was able to find the vascular arch in 93.2%, the macula in 94.4%, the optic disc location in 98.4% and whether it is dealing with a left or right eye in 100% of all tested cases. For the pathological images test set, this was 77.0%, 92.0%, 94.0%, and 100% respectively.

Mesh:

Year:  2007        PMID: 17243590     DOI: 10.1109/TMI.2006.885336

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  17 in total

1.  Diagnosis of diabetic retinopathy: automatic extraction of optic disc and exudates from retinal images using marker-controlled watershed transformation.

Authors:  Ahmed Wasif Reza; C Eswaran; Kaharudin Dimyati
Journal:  J Med Syst       Date:  2010-01-29       Impact factor: 4.460

2.  A decision support system for automatic screening of non-proliferative diabetic retinopathy.

Authors:  Ahmed Wasif Reza; C Eswaran
Journal:  J Med Syst       Date:  2009-07-04       Impact factor: 4.460

3.  Automatic tracing of optic disc and exudates from color fundus images using fixed and variable thresholds.

Authors:  Ahmed Wasif Reza; C Eswaran; Subhas Hati
Journal:  J Med Syst       Date:  2009-02       Impact factor: 4.460

4.  Fast detection of the optic disc and fovea in color fundus photographs.

Authors:  Meindert Niemeijer; Michael D Abràmoff; Bram van Ginneken
Journal:  Med Image Anal       Date:  2009-09-04       Impact factor: 8.545

5.  Computer-aided diagnosis of proliferative diabetic retinopathy via modeling of the major temporal arcade in retinal fundus images.

Authors:  Faraz Oloumi; Rangaraj M Rangayyan; Anna L Ells
Journal:  J Digit Imaging       Date:  2013-12       Impact factor: 4.056

6.  An improved retinal vessel segmentation method based on high level features for pathological images.

Authors:  Razieh Ganjee; Reza Azmi; Behrouz Gholizadeh
Journal:  J Med Syst       Date:  2014-07-19       Impact factor: 4.460

7.  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

8.  A statistical segmentation method for measuring age-related macular degeneration in retinal fundus images.

Authors:  Cemal Köse; Uğur Sevik; Okyay Gençalioğlu; Cevat Ikibaş; Temel Kayikiçioğlu
Journal:  J Med Syst       Date:  2010-02       Impact factor: 4.460

9.  Analysis of Fundus Fluorescein Angiogram Based on the Hessian Matrix of Directional Curvelet Sub-bands and Distance Regularized Level Set Evolution.

Authors:  Asieh Soltanipour; Saeed Sadri; Hossein Rabbani; Mohammad Reza Akhlaghi
Journal:  J Med Signals Sens       Date:  2015 Jul-Sep

10.  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

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