Exploratory characterization of outliers in a multi-centre 1H-MRS brain tumour dataset

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/10944

dc.contributor	Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
dc.contributor	Universitat Politècnica de Catalunya. SOCO - Soft Computing
dc.contributor.author	Vellido Alcacena, Alfredo
dc.contributor.author	Julià Sapé, Margarida
dc.contributor.author	Romero Merino, Enrique
dc.contributor.author	Arús, Carles
dc.date	2008-09
dc.identifier.citation	Vellido, A. [et al.]. Exploratory characterization of outliers in a multi-centre 1H-MRS brain tumour dataset. "Lecture notes in computer science", Setembre 2008, vol. 5178, p. 189-196.
dc.identifier.citation	0302-9743
dc.identifier.citation	10.1007/978-3-540-85565-1_24
dc.identifier.uri	http://hdl.handle.net/2117/10944
dc.language.iso	eng
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
dc.subject	Brain -- Tumors -- Diagnosis
dc.subject	Medical informatics
dc.subject	Medical decision support systems
dc.subject	Proton magnetic resonance spectroscopy
dc.subject	Brain tumours
dc.subject	Outlier detection
dc.subject	Data exploration
dc.subject	Data visualization
dc.subject	Dimensionality reduction
dc.subject	Cervell -- Tumors
dc.subject	Informàtica mèdica
dc.subject	Sistemes d'ajuda a la decisió
dc.title	Exploratory characterization of outliers in a multi-centre 1H-MRS brain tumour dataset
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/article
dc.description.abstract	As part of the AIDTumour research project, the analysis of MRS data corresponding to various tumour pathologies is used to assist expert diagnosis. The high dimensionality of the MR spectra might obscure atypical aspects of the data that would jeopardize their automated classification and, as a result, the process of computer-based diagnostic assistance. In this paper, we put forward a method to overcome this potential problem that combines automatic outlier detection, visualization through dimensionality reduction, and expert opinion.

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