Establishing the macular grading grid by means of fovea centre detection using anatomical-based and visual-based features

This paper presents a methodology for establishing the macular grading grid in digital retinal images by means of fovea centre detection. To this effect, visual and anatomical feature-based criteria are combined with the aim of exploiting the benefits of both techniques. First, acceptable fovea centre estimation is obtained by using a priori known anatomical features with respect to the optic disc and the vascular tree. Second, a type of morphological processing is employed in an attempt to improve the obtained fovea centre estimation when the fovea is detectable in the image; otherwise, it is declared indistinguishable and the first result is retained. The methodology was tested on the MESSIDOR and DIARETDB1 databases making use of a distance criterion between the obtained and the real fovea centre. Fovea centres in the brackets between the categories Excellent and Fair (fovea centres primarily accepted as valid in the literature) made up for 98.24% and 94.38% of the cases in the MESSIDOR and DIARETDB1, respectively.

[1]  András Hajdu,et al.  Combining algorithms for automatic detection of optic disc and macula in fundus images , 2012, Comput. Vis. Image Underst..

[2]  G. Bresnick,et al.  A screening approach to the surveillance of patients with diabetes for the presence of vision-threatening retinopathy. , 2000, Ophthalmology.

[3]  Bunyarit Uyyanonvara,et al.  Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods , 2008, Comput. Medical Imaging Graph..

[4]  Bram van Ginneken,et al.  Fast detection of the optic disc and fovea in color fundus photographs , 2009, Medical Image Anal..

[5]  S. Balasubramanian,et al.  Automatic Detection of Anatomical Structures in Digital Fundus Retinal Images , 2007, MVA.

[6]  Huiqi Li,et al.  Automatic location of optic disk in retinal images , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[7]  J. Olson,et al.  Automatic detection of retinal anatomy to assist diabetic retinopathy screening , 2007, Physics in medicine and biology.

[8]  F L Ferris,et al.  Photocoagulation for diabetic retinopathy. Early Treatment Diabetic Retinopathy Study Research Group. , 1991, JAMA.

[9]  Jasjit S. Suri,et al.  Handbook of Biomedical Image Analysis , 2005 .

[10]  Hans Walther Larsen The ocular fundus: A color atlas , 1976 .

[11]  Jayanthi Sivaswamy,et al.  Appearance-based object detection in colour retinal images , 2008, 2008 15th IEEE International Conference on Image Processing.

[12]  James T Schwiegerling,et al.  Field guide to visual and ophthalmic optics , 2004 .

[13]  Kenneth W. Tobin,et al.  Detection of Anatomic Structures in Human Retinal Imagery , 2007, IEEE Transactions on Medical Imaging.

[14]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[15]  Joni-Kristian Kämäräinen,et al.  The DIARETDB1 Diabetic Retinopathy Database and Evaluation Protocol , 2007, BMVC.

[16]  S. Wild,et al.  Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. , 2004, Diabetes care.

[17]  H. Taylor,et al.  Costs of mobile screening for diabetic retinopathy: a practical framework for rural populations. , 2001, The Australian journal of rural health.

[18]  Asoke K. Nandi,et al.  Automated localisation of optic disk and fovea in retinal fundus images , 2008, 2008 16th European Signal Processing Conference.

[19]  Jacob Scharcanski,et al.  Fovea center detection based on the retina anatomy and mathematical morphology , 2011, Comput. Methods Programs Biomed..

[20]  Bram van Ginneken,et al.  Segmentation of the Optic Disc, Macula and Vascular Arch in Fundus Photographs , 2007, IEEE Transactions on Medical Imaging.

[21]  José Manuel Bravo,et al.  A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features , 2011, IEEE Transactions on Medical Imaging.

[22]  Charles V. Stewart,et al.  Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy , 2006, IEEE Transactions on Biomedical Engineering.

[23]  Manuel Emilio Gegúndez-Arias,et al.  Detecting the Optic Disc Boundary in Digital Fundus Images Using Morphological, Edge Detection, and Feature Extraction Techniques , 2010, IEEE Transactions on Medical Imaging.

[24]  Langis Gagnon,et al.  Procedure to detect anatomical structures in optical fundus images , 2001, SPIE Medical Imaging.

[25]  H. Taylor,et al.  World blindness: a 21st century perspective , 2001, The British journal of ophthalmology.

[26]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[27]  José Manuel Bravo,et al.  Locating the fovea center position in digital fundus images using thresholding and feature extraction techniques , 2013, Comput. Medical Imaging Graph..

[28]  Huiqi Li,et al.  Automated feature extraction in color retinal images by a model based approach , 2004, IEEE Transactions on Biomedical Engineering.

[29]  Andrea Giachetti,et al.  The use of radial symmetry to localize retinal landmarks , 2013, Comput. Medical Imaging Graph..

[30]  C. Sinthanayothin,et al.  Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images , 1999, The British journal of ophthalmology.