Title :
Using Machine Learning Techniques to Explore 1H-MRS Data of Brain Tumors
Author :
Gonzalez-Navarro, Felix F. ; Belanche-Muoz, L.A.
Author_Institution :
Dept. de Llenguatges i Sistemes Inf., Univ. Politec. de Catalunya, Barcelona, Spain
Abstract :
Machine learning is a powerful paradigm to analyze proton magnetic resonance spectroscopy 1H-MRS spectral data for the classification of brain tumor pathologies. An important characteristic of this task is the high dimensionality of the involved data sets. In this work we apply filter feature selection methods on three types of 1H-MRS spectral data: long echo time, short echo time and an ad hoc combination of both. The experimental findings show that feature selection permits to drastically reduce the dimension, offering at the same time very attractive solutions both in terms of prediction accuracy and the ability to interpret the involved spectral frequencies. A linear dimensionality reduction technique that preserves the class discrimination capabilities is additionally used for visualization of the selected frequencies.
Keywords :
biomedical MRI; brain; data visualisation; echo; feature extraction; image classification; learning (artificial intelligence); medical image processing; tumours; 1H-MRS spectral data; ad hoc combination; brain tumor pathology classification; class discrimination capability; filter feature selection method; frequency visualization; linear dimensionality reduction technique; long echo time; machine learning; proton magnetic resonance spectroscopy; short echo time; Filters; Frequency; Machine learning; Magnetic analysis; Magnetic resonance; Magnetic separation; Neoplasms; Pathology; Protons; Spectroscopy; Classification; Feature Selection; Machine Learning; Proton Magnetic Resonance Spectroscopy; Visualization;
Conference_Titel :
Artificial Intelligence, 2009. MICAI 2009. Eighth Mexican International Conference on
Conference_Location :
Guanajuato
Print_ISBN :
978-0-7695-3933-1
DOI :
10.1109/MICAI.2009.26