Fast parallel volume visualization on cuda technology

In the medical diagnosis and treatment planning, radiologists and surgeons rely heavily on the slices produced by medical imaging scanners. Unfortunately, most of these scanners can only produce two dimensional images because the machines that can produce three dimensional are very expensive. The...

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Main Author:	Adekunle Micheal, Adeshina
Format:	Thesis
Language:	English English English
Published:	2013
Subjects:	TA Engineering (General) Civil engineering (General)
Online Access:	http://eprints.uthm.edu.my/2187/1/24p%20ADESHINA%20ADEKUNLE%20MICHAEL.pdf http://eprints.uthm.edu.my/2187/2/ADESHINA%20ADEKUNLE%20MICHAEL%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/2187/3/ADESHINA%20ADEKUNLE%20MICHAEL%20WATERMARK.pdf
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spelling	my-uthm-ep.21872021-10-31T03:53:20Z Fast parallel volume visualization on cuda technology 2013-11 Adekunle Micheal, Adeshina TA Engineering (General). Civil engineering (General) TA1501-1820 Applied optics. Photonics In the medical diagnosis and treatment planning, radiologists and surgeons rely heavily on the slices produced by medical imaging scanners. Unfortunately, most of these scanners can only produce two dimensional images because the machines that can produce three dimensional are very expensive. The two dimensional images from these devices are difficult to interpret because they only show cross-sectional views of the human structure. Consequently, such circumstances require highly qualified doctors to use their expertise in the interpretation of the possible location, size or shape of the abnormalities especially for large datasets of enormous amount of slices. Previously, the concept of reconstructing two dimensional images to three dimensional was introduced. However, such reconstruction model requires high performance computation, may either be time-consuming or costly. Furthermore, detecting the internal features of human anatomical structure, such as the imaging of the blood vessels, is still an open topic in the computer-aided diagnosis of disorders and pathologies. This study proposed, designed and implemented a visualization framework named SurLens with high performance computing using Compute Unified Device Architecture (CUDA), augmenting the widely proven ray casting technique in terms of superior qualities of images but with slow speed. Considering the rapid development of technology in the medical community, our framework is implemented on Microsoft .NET environment for easy interoperability with other emerging revolutionary tools. The Visualization System was evaluated with brain datasets from the department of Surgery, University of North Carolina, United States, containing 109 datasets of MRA, T1-FLASH, T2-Weighted, DTI and T1-MPRAGE. Significantly, at a reasonably cheaper cost, SurLens Visualization System achieves immediate reconstruction and obvious mappings of the internal features of the human brain, reliable enough for instantaneously locate possible blockages in the brain blood vessels without any prior segmentation of the datasets. 2013-11 Thesis http://eprints.uthm.edu.my/2187/ http://eprints.uthm.edu.my/2187/1/24p%20ADESHINA%20ADEKUNLE%20MICHAEL.pdf text en public http://eprints.uthm.edu.my/2187/2/ADESHINA%20ADEKUNLE%20MICHAEL%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/2187/3/ADESHINA%20ADEKUNLE%20MICHAEL%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Malaysia Fakulti Sains Komputer dan Teknologi Maklumat
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TA Engineering (General) Civil engineering (General) TA Engineering (General) Civil engineering (General)
spellingShingle	TA Engineering (General) Civil engineering (General) TA Engineering (General) Civil engineering (General) Adekunle Micheal, Adeshina Fast parallel volume visualization on cuda technology
description	In the medical diagnosis and treatment planning, radiologists and surgeons rely heavily on the slices produced by medical imaging scanners. Unfortunately, most of these scanners can only produce two dimensional images because the machines that can produce three dimensional are very expensive. The two dimensional images from these devices are difficult to interpret because they only show cross-sectional views of the human structure. Consequently, such circumstances require highly qualified doctors to use their expertise in the interpretation of the possible location, size or shape of the abnormalities especially for large datasets of enormous amount of slices. Previously, the concept of reconstructing two dimensional images to three dimensional was introduced. However, such reconstruction model requires high performance computation, may either be time-consuming or costly. Furthermore, detecting the internal features of human anatomical structure, such as the imaging of the blood vessels, is still an open topic in the computer-aided diagnosis of disorders and pathologies. This study proposed, designed and implemented a visualization framework named SurLens with high performance computing using Compute Unified Device Architecture (CUDA), augmenting the widely proven ray casting technique in terms of superior qualities of images but with slow speed. Considering the rapid development of technology in the medical community, our framework is implemented on Microsoft .NET environment for easy interoperability with other emerging revolutionary tools. The Visualization System was evaluated with brain datasets from the department of Surgery, University of North Carolina, United States, containing 109 datasets of MRA, T1-FLASH, T2-Weighted, DTI and T1-MPRAGE. Significantly, at a reasonably cheaper cost, SurLens Visualization System achieves immediate reconstruction and obvious mappings of the internal features of the human brain, reliable enough for instantaneously locate possible blockages in the brain blood vessels without any prior segmentation of the datasets.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Adekunle Micheal, Adeshina
author_facet	Adekunle Micheal, Adeshina
author_sort	Adekunle Micheal, Adeshina
title	Fast parallel volume visualization on cuda technology
title_short	Fast parallel volume visualization on cuda technology
title_full	Fast parallel volume visualization on cuda technology
title_fullStr	Fast parallel volume visualization on cuda technology
title_full_unstemmed	Fast parallel volume visualization on cuda technology
title_sort	fast parallel volume visualization on cuda technology
granting_institution	Universiti Tun Hussein Malaysia
granting_department	Fakulti Sains Komputer dan Teknologi Maklumat
publishDate	2013
url	http://eprints.uthm.edu.my/2187/1/24p%20ADESHINA%20ADEKUNLE%20MICHAEL.pdf http://eprints.uthm.edu.my/2187/2/ADESHINA%20ADEKUNLE%20MICHAEL%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/2187/3/ADESHINA%20ADEKUNLE%20MICHAEL%20WATERMARK.pdf
_version_	1747830920847556608

Fast parallel volume visualization on cuda technology

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