High speed detection of retinal blood vessels in fundus image using phase congruency |
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Authors: | M Ashraful Amin Hong Yan |
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Affiliation: | (1) School of Engineering and Computer Science, Independent University Bangladesh, Dhaka, Bangladesh;(2) Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong;(3) School of Electrical and Information Engineering, University of Sydney, Sydney, NSW, 2006, Australia |
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Abstract: | Detection of blood vessels in retinal fundus image is the preliminary step to diagnose several retinal diseases. There exist
several methods to automatically detect blood vessels from retinal image with the aid of different computational methods.
However, all these methods require lengthy processing time. The method proposed here acquires binary vessels from a RGB retinal
fundus image in almost real time. Initially, the phase congruency of a retinal image is generated, which is a soft-classification
of blood vessels. Phase congruency is a dimensionless quantity that is invariant to changes in image brightness or contrast;
hence, it provides an absolute measure of the significance of feature points. This experiment acquires phase congruency of
an image using Log-Gabor wavelets. To acquire a binary segmentation, thresholds are applied on the phase congruency image.
The process of determining the best threshold value is based on area under the relative operating characteristic (ROC) curve.
The proposed method is able to detect blood vessels in a retinal fundus image within 10 s on a PC with (accuracy, area under
ROC curve) = (0.91, 0.92), and (0.92, 0.94) for the STARE and the DRIVE databases, respectively. |
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