Classification of in vivo magnetic resonance spectra

Björn H. Menze, Michael Wormit, Peter Bachert, Matthias Lichy, Heinz Peter Schlemmer, Fred A. Hamprecht

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

We present the results of a systematic and quantitative comparison of methods from pattern recognition for the analysis of clinical magnetic resonance spectra. The medical question being addressed is the detection of brain tumor. In this application we find regularized linear methods to be superior to more flexible methods such as support vector machines, neural networks or random forests. The best preprocessing method for our spectral data is a smoothing and subsampling approach.

Original languageEnglish
Title of host publicationClassification - The Ubiquitous Challenge
Subtitle of host publicationProceedings of the 28th Annual Conference of the Gesellschaft fur Klassifikation e.V., GfKl 2004
PublisherKluwer Academic Publishers
Pages362-369
Number of pages8
ISBN (Print)3540256776, 9783540256779
DOIs
StatePublished - 2005
Externally publishedYes
Event28th Annual Conference of the German Classification Society (Gesellschaft fur Klassifikation) on Classification: The Ubiquitous Challenge, GfKl 2004 - Dortmund, Germany
Duration: 9 Mar 200411 Mar 2004

Publication series

NameStudies in Classification, Data Analysis, and Knowledge Organization
ISSN (Print)1431-8814

Conference

Conference28th Annual Conference of the German Classification Society (Gesellschaft fur Klassifikation) on Classification: The Ubiquitous Challenge, GfKl 2004
Country/TerritoryGermany
CityDortmund
Period9/03/0411/03/04

Fingerprint

Dive into the research topics of 'Classification of in vivo magnetic resonance spectra'. Together they form a unique fingerprint.

Cite this