Classification of 3-D MRI Brain Data Using Modified Maximum Uncertainty Linear Discriminant Analysis

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Classification of 3-D MRI Brain Data Using Modified Maximum Uncertainty Linear Discriminant Analysis

Popis výsledku v původním jazyce

Recent studies have demonstrated that diagnostics of schizophrenia based on image data is a difficult task because of extensive overlaps of brain regions distinguishing patients with schizophrenia from healthy controls and also because of the small sample size problem. An algorithm for the automatic classification of first-episode schizophrenia patients and healthy controls based on deformations and gray matter (GM) density images extracted from their MRI intensity data is introduced here. The deformations and GM density images are reduced by principal component analysis, which is here based on the covariance matrix of persons (pPCA). The reduced image data is then classified with the use of modified maximum uncertainty linear discriminant analysis (MLDA), which gives better sensitivity than original MLDA. The classification efficiency of the proposed algorithm is comparable with other state-of-art studies in the schizophrenia research.

Název v anglickém jazyce

Classification of 3-D MRI Brain Data Using Modified Maximum Uncertainty Linear Discriminant Analysis

Popis výsledku anglicky

Recent studies have demonstrated that diagnostics of schizophrenia based on image data is a difficult task because of extensive overlaps of brain regions distinguishing patients with schizophrenia from healthy controls and also because of the small sample size problem. An algorithm for the automatic classification of first-episode schizophrenia patients and healthy controls based on deformations and gray matter (GM) density images extracted from their MRI intensity data is introduced here. The deformations and GM density images are reduced by principal component analysis, which is here based on the covariance matrix of persons (pPCA). The reduced image data is then classified with the use of modified maximum uncertainty linear discriminant analysis (MLDA), which gives better sensitivity than original MLDA. The classification efficiency of the proposed algorithm is comparable with other state-of-art studies in the schizophrenia research.

Druh

D - Stať ve sborníku

CEP obor

FL - Psychiatrie, sexuologie

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2010

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of Medical Image Understanding and Analysis 2010

ISBN

978-0-9566150-0-8

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

83-87

Název nakladatele

University of Warwick

Místo vydání

Coventry

Místo konání akce

Warwick

Datum konání akce

1. 1. 2010

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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