Abstract
Optimized anisotropic diffusion is commonly used in medical imaging for the purpose of reducing background noise and tissues and enhancing the vessel structures of interest. In this work, a hybrid diffusion tensor is developed, which integrates Frangi’s vesselness measure with a continuous switch, suitable for filtering both tubular and planar image structures. Besides, a new 3D diffusion discretization scheme is proposed, in which we apply Gaussian kernel decomposition for computing image derivatives. This scheme is rotational invariant and shows good isotropic filtering properties on both synthetic and real Computed Tomography Angiography (CTA) data. In addition, segmentation approach is performed over filtered images obtained by using different schemes. Our method is proved to give better segmentation result and more thin branches can be detected. In conclusion, the proposed method should garner wider clinical applicability in Computed Tomography Coronary Angiography (CTCA) images preprocessing.
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Acknowledgment
The study is supported by the National Natural Science Foundation of China under Grants 61471297, the China Postdoctoral Science Foundation under Grant 2017M623245 and the Fundamental Research Funds for the Central Universities under Grants 3102018zy031. We are very grateful to the National Heart Centre Singapore for the DICOM datasets.
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The authors declare that they have no conflict of interest.
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Cui, H. (2018). Fast and Robust 3D Numerical Method for Coronary Artery Vesselness Diffusion from CTA Images. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11166. Springer, Cham. https://doi.org/10.1007/978-3-030-00764-5_45
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DOI: https://doi.org/10.1007/978-3-030-00764-5_45
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