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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| Format: | Thesis |
| Language: | English English English |
| Published: |
2013
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| Subjects: | |
| 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 http://eprints.uthm.edu.my/2187/ |
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| Institution: | Universiti Tun Hussein Onn Malaysia |
| Language: | English English English |
| Summary: | 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. |
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