@MASTERSTHESIS\{IMM2011-06090,
    author       = "A. C. Alonso",
    title        = "Improvement of {MRI} brain segmentation - Fully multispectral approach from the 'New Segmentation' method of Statistical Parametric Mapping",
    year         = "2011",
    school       = "Technical University of Denmark, {DTU} Informatics, {E-}mail: reception@imm.dtu.dk",
    address      = "Asmussens Alle, Building 305, {DK-}2800 Kgs. Lyngby, Denmark",
    type         = "",
    note         = "Supervised by Professor Rasmus Larsen, rl@imm.dtu.dk, {DTU} Informatics",
    url          = "http://www.imm.dtu.dk/English.aspx",
    abstract     = "The {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 different 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."
}