This thesis investigates the feasibility of using fluid flow as a deformable model for
segmenting vessels in 2D and 3D medical images. Exploiting fluid flow in vessel
segmentation is biologically plausible since vessels naturally provide the medium for
blood transportation. Fluid flow can be used as a basis for powerful vessel
segmentation because streaming fluid regions can merge and split providing
topological adaptivity. In addition, the fluid can also flow through small gaps formed
by imaging artifacts building connections between disconnected areas. In our study,
due to their simplicity, parallelism, and low computational cost compared to other
fluid simulation methods, linearized shallow water equations (LSWE) are used. The
method developed herein is validated using synthetic data sets, two clinical datasets,
and publicly available simulated datasets which contain Magnetic Resonance
Angiography (MRA) images, Magnetic Resonance Venography (MRV) images and
retinal angiography images. Depending on image size, one to two order of magnitude
speed ups are obtained with developed parallel implementation using Nvidia
Compute Unified Device Architecture (CUDA) compared to single-core and multicore
CPU implementation.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12613197/index.pdf |
| Date | 01 May 2011 |
| Creators | Nar, Fatih |
| Contributors | Gokcay, Didem |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | Ph.D. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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