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Published Articles >> Table of Contents >> Abstract
XVI Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'03)
p. 262
Blood Vessels Segmentation in Non-Mydriatic Images Using Wavelets and Statistical Classi.ers
Jorge J. G. Leandro, USP
Joao V. B. Soares, USP
Roberto M. Cesar Jr., USP
Herbert F. Jelinek, Charles Sturt University
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SIBGRA.2003.1241018
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| Abstract |
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This work describes a new framework for automatic analysis of optic fundus non-mydriatic images, focusing on the segmentation of the blood vessels by using pixel classification based on pattern recognition techniques. Each pixel is represented by a feature vector composed of color information and measurements at different scales taken from the continuous-wavelet (Morlet) transform as well as from mean and order filtering applied to the green channel. The major benefit resulting from the wavelet application to the optic fundus images is its multiscale analysing capability in tuning to specific frequencies, thus allowing noise filtering and blood vessel enhancement in a single step. Supervised classifiers are then applied to label each pixel as either a vessel or a non-vessel. Two different strategies to select the traning set have been devised: (1) the blood vessels of a sample image are completely drawn by hand, leading to a labeled image (i.e. vessels × non-vessel pixels) which is used to train the classifier, to be applied to other images; (2) the vessels located in a given small portion of the target image are drawn by hand and the remaining fundus image segmented by a classifier trained using the hand-drawn portion to define the training set. The latter strategy is particularly suitable for the implementation of a semi-automated software to be used by health workers in order to avoid the need of setting imaging parameters such as thresholds. Both strategies have been extensively assessed and several successful experimental results using real-case images have been obtained.
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 Additional Information
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Citation:
Jorge J. G. Leandro, Joao V. B. Soares, Roberto M. Cesar Jr., Herbert F. Jelinek,
"Blood Vessels Segmentation in Non-Mydriatic Images Using Wavelets and Statistical Classi.ers,"
sibgrapi,
p. 262,
XVI Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'03),
2003
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