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

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.

[1]  Gary Bradski,et al.  Learning-Based Computer Vision with Intels Open Source Computer Vision Library , 2005 .

[2]  J. Olson,et al.  Automated assessment of diabetic retinal image quality based on clarity and field definition. , 2006, Investigative ophthalmology & visual science.

[3]  Peter F. Sharp,et al.  Evaluation of a System for Automatic Detection of Diabetic Retinopathy From Color Fundus Photographs in a Large Population of Patients With Diabetes , 2008, Diabetes Care.

[4]  Frédéric Zana,et al.  Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation , 2001, IEEE Trans. Image Process..

[5]  V. F. F. Leavers.  Shape Detection in Computer Vision Using the Hough Transform , 2011 .

[6]  P. Zimmet,et al.  The Rising Global Burden of Diabetes and its Complications: Estimates and Projections to the Year 2010 , 1997, Diabetic medicine : a journal of the British Diabetic Association.

[7]  Ian J. Deary,et al.  Retinal image analysis: concepts, applications and potential , 2006 .

[8]  Yiming Wang,et al.  Automatic retinal image quality assessment and enhancement , 1999, Medical Imaging.

[9]  M. Abràmoff,et al.  Web-based screening for diabetic retinopathy in a primary care population: the EyeCheck project. , 2005, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[10]  Alexei A. Efros,et al.  Discovering object categories in image collections , 2005 .

[11]  Langis Gagnon,et al.  Automatic visual quality assessment in optical fundus images , 2001 .

[12]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  Alexei A. Efros,et al.  Discovering objects and their location in images , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[14]  Bram van Ginneken,et al.  Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening , 2006, Medical Image Anal..

[15]  R. Halír.  Numerically Stable Direct Least Squares Fitting of Ellipses , 1998 .

[16]  Paul L. Rosin.  Ellipse fitting by accumulating five-point fits , 1993, Pattern Recognit. Lett..

[17]  Kenneth W. Tobin,et al.  Characterization of the optic disc in retinal imagery using a probabilistic approach , 2006, SPIE Medical Imaging.