Semi-automatic approach for brain tissue segmentation using MRI

Reference:

Nicolau Gonçalves and Ricardo Vigário. Semi-automatic approach for brain tissue segmentation using MRI. In 1st INCF Congress of Neuroinformatics: Databasing and Modeling the Brain (Neuroinformatics 2008), page 106, Stockholm, Sweden, September 2008. Poster.

Abstract:

Magnetic resonance imaging is a widely used non-invasive diagnostic tool, which requires expert evaluation to assess the severity of brain lesions. In this paper, a method is presented for semi-automatic detection of those lesions. The goal is to provide a reliable tool for lesion detection, by improving tissue contrast and visualisation. The final objective is to reduce the intensive need for specialists, and allowing a rather systematic follow-up of the lesion evolution, or its treatment. Our approach is based on a combination of machine learning techniques. First, independent component analysis is used to allow for tissue segmentation. Afterwards, self-organising maps are applied, in order to create labels for the subsequent tissue classification. Then, two classification methods are experimented and compared: support vector machines and discriminative clustering, with special emphasis on the latter. The results indicate that the use of discriminative clustering allows for a good tissue classification, with the ability to even detect small isolated lesions, usually not detected in region-growth techniques.

Keywords:

brain MRI, discriminative clustering, tissue segmentation, unsupervised classification, MS lesion

Suggested BibTeX entry:

@inproceedings{Goncalves2008INCF,
    address = {Stockholm, Sweden},
    author = {Gonçalves, Nicolau and Vigário, Ricardo},
    booktitle = {1st INCF Congress of Neuroinformatics: Databasing and Modeling the Brain (Neuroinformatics 2008)},
    language = {eng},
    month = {September},
    note = {Poster},
    pages = {106},
    title = {Semi-automatic approach for brain tissue segmentation using {MRI}},
    year = {2008},
}