Vessel segmentation using shallow water equations

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Tezin Türü: Doktora

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Enformatik Enstitüsü, Sağlık Bilişimi Anabilim Dalı, Türkiye

Tezin Onay Tarihi: 2011

Öğrenci: FATİH NAR

Danışman: DİDEM GÖKÇAY

Özet:

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.