Diploma and Master Theses (authored and supervised):

D. Major:
"Markov Random Field Based Structure Localisation of Vertebrae for 3D-Segmentation of the Spine in CT Volume Data";
Supervisor: E. Gröller, K. Bühler; Institut für Computergraphik und Algorithmen, 2010; final examination: 2010-05-11.

English abstract:
Medical Image Processing is a growing field in medicine and plays an important role in medical decision making. Computer-based segmentation of anatomies in data made by imaging modalities supports clinicians and speeds up their diagnosis making compared to doing it manually. Computed Tomography (CT) is an imaging modality for slice-wise three dimensional reconstruction of the human body in the form of volumetric data which is especially applicable for imaging of bony structures and so for the vertebral column. Most bony structures, such as vertebrae, are characterised by complex shape and texture appearances which turns its segmentation into a difficult task. Model-based segmentation approaches are promising techniques to cope with variations in form and texture of the anatomy of interest. This is done by incorporating information about shape and texture appearance gained from an imaging modality in a model. The model can then be applied to segment the object of interest in target data, however most of the model-based approaches need a model intialisation for a fast and reliable segmentation of the object of interest. This thesis was motivated by novel works on fast anatomical structure localisation with Markov Random Fields (MRFs) and focuses on the sparse structure localisation of single vertebrae in CT scans for a subsequent model initialisation of more sophisticated segmentation algorithms. A MRF based model of appearance, which employs local information in regions around anatomical landmarks and geometrical information through connections between adjacent landmarks, is built on volumetric CT datasets of lumbar vertebrae. The MRF based model is built on a 6 landmark configuration in vertebra volumetric data and is additionally matched with target data. This is done by finding a best fit MRF matching by the Max-sum algorithm among feature points found by a decision tree based feature detection algorithm called probabilistic boosting tree (PBT). Anatomical landmark regions are described by vector spin-images and shape index histograms. Adjacency information is extracted by Delaunay tetrahedralisation where distances and gradient-related angles describe connections between adjacent regions. The results on single lumbar vertebra CT scans show that the MRF approach is applicable on volumetric CT datasets with an accuracy enough for supporting more sophisticated segmentation algorithms such as Active Appearance Models (AAMs).

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.