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23 February 2012 A robust and accurate approach to automatic blood vessel detection and segmentation from angiography x-ray images using multistage random forests
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Abstract
In this paper we propose a novel approach based on multi-stage random forests to address problems faced by traditional vessel segmentation algorithms on account of image artifacts such as stitches organ shadows etc.. Our approach consists of collecting a very large number of training data consisting of positive and negative examples of valid seed points. The method makes use of a 14x14 window around a putative seed point. For this window three types of feature vectors are computed viz. vesselness, eigenvalue and a novel effective margin feature. A random forest RF is trained for each of the feature vectors. At run time the three RFs are applied in succession to a putative seed point generated by a naiive vessel detection algorithm based on vesselness. Our approach will prune this set of putative seed points to correctly identify true seed points thereby avoiding false positives. We demonstrate the effectiveness of our algorithm on a large dataset of angio images.
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Vipin Gupta, Amit Kale, and Hari Sundar "A robust and accurate approach to automatic blood vessel detection and segmentation from angiography x-ray images using multistage random forests", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83152F (23 February 2012); https://doi.org/10.1117/12.910649
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