AUTOMATIC EXTRACTION OF THE OPTIC DISC BOUNDARY FOR DETECTING RETINAL DISEASES

In this paper, we propose an algorithm based on active shape model for the extraction of Optic Disc boundary. The determination of Optic Disc boundary is fundamental to the automation of retinal eye disease diagnosis because the Optic Disc Center is typically used as a reference point to locate other retinal structures, and any structural change in Optic Disc, whether textural or geometrical, can be used to determine the occurrence of retinal diseases such as Glaucoma. The algorithm is based on determining a model for the Optic Disc boundary by learning patterns of variability from a training set of annotated Optic Discs. The model can be deformed so as to reflect the boundary of Optic Disc in any feasible shape. The algorithm provides some initial steps towards automation of the diagnostic process for retinal eye disease in order that more patients can be screened with consistent diagnoses. The overall accuracy of the algorithm was 92% on a set of 110 images.

[1]  J. Thiran,et al.  Identification of the optic disk boundary in retinal images using active contours , 1999 .

[2]  Cemal Köse,et al.  Statistical Techniques for Detection of Optic Disc and Macula and Parameters Measurement in Retinal Fundus Images , 2011 .

[3]  Jo a o Manuel R. S. Tavares,et al.  Methods to automatically build Point Distribution Models for objects like hand palms and faces represented in images , 2008 .

[4]  R. Reilly,et al.  Extraction of the optic disk boundary in digital fundus images , 1999, Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. N.

[5]  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.

[6]  Jayanthi Sivaswamy,et al.  Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment , 2011, IEEE Transactions on Medical Imaging.

[7]  Juan Xu,et al.  Optic disk feature extraction via modified deformable model technique for glaucoma analysis , 2007, Pattern Recognit..

[8]  Lindsay I. Smith,et al.  A tutorial on Principal Components Analysis , 2002 .

[9]  M. B. Stegmann,et al.  A Brief Introduction to Statistical Shape Analysis , 2002 .

[10]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[11]  Junaed Sattar.  Snakes , Shapes and Gradient Vector Flow , 2022 .

[12]  Jos B. T. M. Roerdink,et al.  The Watershed Transform: Definitions, Algorithms and Parallelization Strategies , 2000, Fundam. Informaticae.

[13]  J. Jonas,et al.  Ophthalmoscopic evaluation of the optic nerve head. , 1999, Survey of ophthalmology.

[14]  Jonathon Shlens,et al.  A Tutorial on Principal Component Analysis , 2014, ArXiv.

[15]  Thomas Walter,et al.  Segmentation of Color Fundus Images of the Human Retina: Detection of the Optic Disc and the Vascular Tree Using Morphological Techniques , 2001, ISMDA.

[16]  Huiqi Li,et al.  Boundary detection of optic disk by a modified ASM method , 2003, Pattern Recognit..

[17]  U. Rajendra Acharya,et al.  Automated Diagnosis of Glaucoma Using Digital Fundus Images , 2009, Journal of Medical Systems.

[18]  J M J Roodhooft,et al.  Leading causes of blindness worldwide. , 2002, Bulletin de la Societe belge d'ophtalmologie.

[19]  Timothy F. Cootes,et al.  Statistical models of appearance for computer vision , 1999 .

[20]  Mariano Rincón,et al.  Identification of the optic nerve head with genetic algorithms , 2008, Artif. Intell. Medicine.

[21]  Demetri Terzopoulos,et al.  Deformable models in medical image analysis: a survey , 1996, Medical Image Anal..