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

D 2010

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

JANOUŠOVÁ, Eva; Daniel SCHWARZ a Tomáš KAŠPÁREK

Základní údaje

Originální název

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

Autoři

JANOUŠOVÁ, Eva; Daniel SCHWARZ a Tomáš KAŠPÁREK

Vydání

Coventry, United Kingdom, Proceedings of Medical Image Understanding and Analysis 2010, od s. 83-87, 5 s. 2010

Nakladatel

University of Warwick

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

30000 3. Medical and Health Sciences

Stát vydavatele

Česká republika

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Označené pro přenos do RIV

Organizační jednotka

Lékařská fakulta

ISBN

978-0-9566150-0-8

Klíčová slova česky

Klasifikace, analýza hlavních komponent, lineární diskriminační analýza, MRI, výpočetní neuroanatomie, schizofrenie

Klíčová slova anglicky

Classification, Principal Component Analysis, Linear Discriminant Analysis, MRI, Computational Neuroanatomy, Schizophrenia

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 21. 7. 2010 18:10, RNDr. Eva Koriťáková, Ph.D.

Anotace

V originále

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.

Návaznosti

NS10347, projekt VaV

Název: Moderní metody rozpoznávání pro analýzu obrazových dat v neuropsychiatrickém výzkumu

Investor: Ministerstvo zdravotnictví ČR, Moderní metody rozpoznávání pro analýzu obrazových dat v neuropsychiatrickém výzkumu

NS9893, projekt VaV

Název: Predikce průběhu iniciálních fází schizofrenie pomocí morfologie mozku

Investor: Ministerstvo zdravotnictví ČR, Predikace průběhu iniciálních fází schizofrenie pomocí morfologie mozku

Citovat

JANOUŠOVÁ, Eva; Daniel SCHWARZ a Tomáš KAŠPÁREK. Classification of 3-D MRI Brain Data Using Modified Maximum Uncertainty Linear Discriminant Analysis. In Proceedings of Medical Image Understanding and Analysis 2010. Coventry, United Kingdom: University of Warwick, 2010, s. 83-87. ISBN 978-0-9566150-0-8.

@inproceedings{891808,
   author = {Janoušová, Eva and Schwarz, Daniel and Kašpárek, Tomáš},
   address = {Coventry, United Kingdom},
   booktitle = {Proceedings of Medical Image Understanding and Analysis 2010},
   keywords = {Classification, Principal Component Analysis, Linear Discriminant Analysis, MRI, Computational Neuroanatomy, Schizophrenia},
   language = {eng},
   location = {Coventry, United Kingdom},
   isbn = {978-0-9566150-0-8},
   pages = {83-87},
   publisher = {University of Warwick},
   title = {Classification of 3-D MRI Brain Data Using Modified Maximum Uncertainty Linear Discriminant Analysis},
   url = {http://www2.warwick.ac.uk/fac/sci/dcs/events/miua2010/proceedings/},
   year = {2010}
}

TY  - CONF
ID  - 891808
AU  - Janoušová, Eva - Schwarz, Daniel - Kašpárek, Tomáš
PY  - 2010
TI  - Classification of 3-D MRI Brain Data Using Modified Maximum Uncertainty Linear Discriminant Analysis
PB  - University of Warwick
CY  - Coventry, United Kingdom
SN  - 9780956615008
KW  - Classification, Principal Component Analysis, Linear Discriminant Analysis, MRI, Computational Neuroanatomy, Schizophrenia
UR  - http://www2.warwick.ac.uk/fac/sci/dcs/events/miua2010/proceedings/
N2  - 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.
ER  -

JANOUŠOVÁ, Eva; Daniel SCHWARZ a Tomáš KAŠPÁREK. Classification of 3-D MRI Brain Data Using Modified Maximum Uncertainty Linear Discriminant Analysis. In \textit{Proceedings of Medical Image Understanding and Analysis 2010}. Coventry, United Kingdom: University of Warwick, 2010, s.~83-87. ISBN~978-0-9566150-0-8.

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