Detecting optic disc on asians by multiscale gaussian filtering

Authors:
Bob Zhang;Jane You;Fakhri Karray
Affiliations:
Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada;Biometric Research Center, and Department of Computing, Hong Kong Polytechnic University, Hong Kong;Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada
Venue:
Journal of Biomedical Imaging - Special issue on Advances in Computer-Aided Detection and Diagnosis
Year:
2012

Citing 4
Cited 0

Segmentation of Color Fundus Images of the Human Retina: Detection of the Optic Disc and the Vascular Tree Using Morphological Techniques

ISMDA '01 Proceedings of the Second International Symposium on Medical Data Analysis
Comparison of Colour Spaces for Optic Disc Localisation in Retinal Images

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
A Model-Based Approach for Automated Feature Extraction in Fundus Images

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Optic disc detection by multi-scale gaussian filtering with scale production and a vessels' directional matched filter

ICMB'10 Proceedings of the Second international conference on Medical Biometrics

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Abstract

The optic disc (OD) is an important anatomical feature in retinal images, and its detection is vital for developing automated screening programs. Currently, there is no algorithm designed to automatically detect the OD in fundus images captured from Asians which are larger and have thicker vessels compared to Caucasians. In this paper, we propose such a method to complement current algorithms using two steps: OD vessel candidate detection and OD vessel candidate matching. The first step is achieved with multiscale Gaussian filtering, scale production, and double thresholding to initially extract the vessels' directional map of various thicknesses. The map is then thinned before another threshold is applied to remove pixels with low intensities. This result forms the OD vessel candidates. In the second step, a Vessels' DirectionalMatched Filter (VDMF) of various dimensions is applied to the candidates to be matched, and the pixel with the smallest difference designated the OD center. We tested the proposed method on a new database consisting of 402 images from a diabetic retinopathy (DR) screening programme consisting of Asians. The OD center was successfully detected with an accuracy of 99.25% (399/402).