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

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 b...

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Main Authors: Ajam, A., Aziz, A.A., Asirvadam, V.S., Muda, A.S., Faye, I., Safdar Gardezi, S.J.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.19463 /
Published: Institute of Electrical and Electronics Engineers Inc. 2017
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85023751151&doi=10.1109%2fACCESS.2017.2718590&partnerID=40&md5=c3e549d068f9bf1329420a0b28b533fd
http://eprints.utp.edu.my/19463/
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spelling utp-eprints.194632018-04-20T05:58:54Z A Review on Segmentation and Modeling of Cerebral Vasculature for Surgical Planning Ajam, A. Aziz, A.A. Asirvadam, V.S. Muda, A.S. Faye, I. Safdar Gardezi, S.J. 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. Institute of Electrical and Electronics Engineers Inc. 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85023751151&doi=10.1109%2fACCESS.2017.2718590&partnerID=40&md5=c3e549d068f9bf1329420a0b28b533fd 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. http://eprints.utp.edu.my/19463/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description 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.
format Article
author Ajam, A.
Aziz, A.A.
Asirvadam, V.S.
Muda, A.S.
Faye, I.
Safdar Gardezi, S.J.
spellingShingle Ajam, A.
Aziz, A.A.
Asirvadam, V.S.
Muda, A.S.
Faye, I.
Safdar Gardezi, S.J.
A Review on Segmentation and Modeling of Cerebral Vasculature for Surgical Planning
author_sort Ajam, A.
title A Review on Segmentation and Modeling of Cerebral Vasculature for Surgical Planning
title_short A Review on Segmentation and Modeling of Cerebral Vasculature for Surgical Planning
title_full A Review on Segmentation and Modeling of Cerebral Vasculature for Surgical Planning
title_fullStr A Review on Segmentation and Modeling of Cerebral Vasculature for Surgical Planning
title_full_unstemmed A Review on Segmentation and Modeling of Cerebral Vasculature for Surgical Planning
title_sort review on segmentation and modeling of cerebral vasculature for surgical planning
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85023751151&doi=10.1109%2fACCESS.2017.2718590&partnerID=40&md5=c3e549d068f9bf1329420a0b28b533fd
http://eprints.utp.edu.my/19463/
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score 11.62408

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