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| A Universal Vessel Segmentation Method Based on Multi-Scale Filtering and Statistical Mixture Model |
| Lu Pei Wang Lei Li Zhicheng Zhou Shoujun* |
| Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, China |
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Abstract Accurate extraction and localization of blood vesselsare the keys to the intervention operation of cardiac and cerebral vessels. Multi-scale filtering strengthens the vessels while weaken the background voxels, but the vessels are still not marked out. Statistical based segmentation method classifies the vessels through model fitting for the histogram curve, but it needs to adjust its model to fit a certain image histogram. To overcome these problems, a universal vessel segmentation method with a fixed model has been proposed in this paper. Firstly, the original image was preprocessed with multi-scale vessel enhancement algorithm. Secondly, a mixture model formed by three probabilistic distributions (one normal distribution and two exponentials) was built to fit the enhanced data. Expectation maximization algorithm has been used for parameters estimation. Finally, the vessels were segmented by maximum a posteriori classification. To test the effectiveness of the proposed method, experiments have been done on a series of phantoms, magnetic resonance angiography (MRA) data and computed tomography angiography (CTA) data. As a result, the segmentation errors of the phantoms are less than 0.3%. Meanwhile, the proposed method performed well on multi-modality images with strong robustness.
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Received: 12 January 2016
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