A Review on Segmentation and Modeling of Cerebral Vasculature for Surgical Planning

Ajam, A. and Aziz, A.A. and Asirvadam, V.S. and Muda, A.S. and Faye, I. and Safdar Gardezi, S.J. (2017) A Review on Segmentation and Modeling of Cerebral Vasculature for Surgical Planning. IEEE Access, 5 . pp. 15222-15240.

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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. © 2013 IEEE.

Item Type:Article
Impact Factor:cited By 0
Departments / MOR / COE:Division > Academic > Faculty of Engineering > Electrical & Electronic Engineering
ID Code:19463
Deposited By: Ahmad Suhairi
Deposited On:20 Apr 2018 05:58
Last Modified:20 Apr 2018 05:58

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