Optimal filter approach for the detection of vessel bifurcations in color fundus images
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13 March 2013 Optimal filter approach for the detection of vessel bifurcations in color fundus images
Qiao Hu, Mona K. Garvin, Mark A. Christopher, Xiayu Xu, T. E. Scheetz, Michael D. Abramoff
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Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 866920 (2013) https://doi.org/10.1117/12.2007088
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
Bifurcations of retinal vessels in fundus images are important structures clinically and their detection is also an important component in image processing algorithms such as registration, segmentation and change detection. In this paper, we develop a method for direct bifurcation detection based on the optimal filter framework. This approach first generates a set of filters to represent all cases of bifurcations, and then uses them to generate a feature space for a classifier to distinguish bifurcations and non-bifurcations. This approach is different from previous methods as it uses a minimal number of assumptions, essentially only requiring training images and expert annotations of bifurcations. The method is trained on 60 fundus images and tested on 20 fundus images, resulting in an AUC of 0.883, which compares well to a human expert.
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Qiao Hu, Mona K. Garvin, Mark A. Christopher, Xiayu Xu, T. E. Scheetz, and Michael D. Abramoff "Optimal filter approach for the detection of vessel bifurcations in color fundus images", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866920 (13 March 2013); https://doi.org/10.1117/12.2007088
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KEYWORDS
Image segmentation
Feature selection
Image filtering
Optimal filtering
Detection and tracking algorithms
Image processing
Image processing algorithms and systems
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