Improvement of MRI brain segmentation - Fully multispectral approach from the 'New Segmentation' method of Statistical Parametric Mapping

Angel Diego Cunado Alonso

AbstractThe PET scanners show the metabolic activity of the studied biological tissues and they are very important in the clinical diagnosis of brain diseases. They generate low resolution images that can be improved with the estimated GM volume of the brain. The MRI scanners provide high resolution and can be optimized for the segmentation of anatomical structures. Therefore, the goal of this project is the improvement of a state-of-the-art automatic method that segments MRI brain volumes into GM, WM and CSF tissues.
The 'New Segmentation' method implemented in SPM8 allows multispectral input data, but it assumes non-correlated modalities. Therefore, this thesis modi fies this method and its Matlab implementation in order to include correlation between modalities in the generative model, and hence use all the potential of multispectral approaches.
The modifi ed method was compared to other uni-modal and multi-modal methods in the segmentation of two di fferent datasets. The results showed that the multi-modal approaches were better that the uni-modal. In addition, the obtained Dice scores of the modi fied method were slightly higher than the ones of the original method. It was also visually checked the segmented volumes from original and modifi ed method, and it showed that the latter is able to segment better the voxels that lie in the interface among several tissues.
TypeMaster's thesis [Academic thesis]
Year2011
PublisherTechnical University of Denmark, DTU Informatics, E-mail: reception@imm.dtu.dk
AddressAsmussens Alle, Building 305, DK-2800 Kgs. Lyngby, Denmark
SeriesIMM-M.Sc.-2011-58
NoteSupervised by Professor Rasmus Larsen, rl@imm.dtu.dk, DTU Informatics
Electronic version(s)[pdf]
Publication linkhttp://www.imm.dtu.dk/English.aspx
BibTeX data [bibtex]
IMM Group(s)Image Analysis & Computer Graphics