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A review on segmentation and modeling of cerebral vasculature for surgical planning


Ajam, Azimah and Abd. Aziz, Azrina and Asirvadam, Vijanth Sagayan and Muda, Ahmad Sobri and Faye, Ibrahima and Gardezi, S. Jamal Safdar (2017) A review on segmentation and modeling of cerebral vasculature for surgical planning. IEEE Access, 5. 15222 - 15240. ISSN 2169-3536


Visualization of cerebral blood vessels is vital for stroke diagnosis and surgical planning. A suitable modality for the visualization of blood vessels is very important for the analysis of abnormalities of the cerebrovascular system, as it is the most complex blood circulation system in the human body and vulnerable to bleeding, infection, blood clot, stenosis, and many other forms of damage. Images produced by current imaging modalities are not promising because of noise, artifacts, and the complex structure of cerebral blood vessels. Therefore, there is a requirement for the accurate reconstruction of blood vessels to assist the clinician in making an accurate diagnosis and surgical planning. This paper presents an overall review of modeling techniques that can be classified into the three categories, i.e., image-based modeling, mathematical modeling, and hybrid modeling. Image-based modeling deals directly with medical images and which involves preprocessing, segmentation, feature extraction, and classification. Mathematical modeling exploits existing mathematical laws and equations, an example being an arterial bifurcation, which is assumed to follow a fractal and cube law, and a system of ordinary differential equations are solved to obtain pressure and velocity estimates in a branching network. Whereas, Hybrid modeling incorporates both image-based and mathematical modeling to attempt to produce a more detailed and realistic arterial structure. From the literature review and the analysis of the results, it can be summarized that hybrid models provide a faster and more robust technique, which can significantly help in diagnosis and surgical planning, such as for finding the shortest path for a stenting procedure.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Medicine and Health Science
DOI Number: https://doi.org/10.1109/ACCESS.2017.2718590
Publisher: Institute of Electrical and Electronics Engineers
Keywords: Cerebrovascular imaging; Cerebral vessel modeling; Image-based modeling; Mathematical modeling; Hybrid modeling; Segmentation
Depositing User: Ms. Nida Hidayati Ghazali
Date Deposited: 29 Apr 2019 05:54
Last Modified: 29 Apr 2019 05:54
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/ACCESS.2017.2718590
URI: http://psasir.upm.edu.my/id/eprint/60722
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