Brain Image Classification Based on Automated Morphometry and
Penalised Linear Discriminant Analysis with Resampling

D 2015

Brain Image Classification Based on Automated Morphometry and Penalised Linear Discriminant Analysis with Resampling

JANOUŠOVÁ, Eva; Daniel SCHWARZ; Giovanni MONTANA a Tomáš KAŠPÁREK

Základní údaje

Originální název

Brain Image Classification Based on Automated Morphometry and Penalised Linear Discriminant Analysis with Resampling

Autoři

JANOUŠOVÁ, Eva (203 Česká republika, garant, domácí); Daniel SCHWARZ (203 Česká republika, domácí); Giovanni MONTANA (826 Velká Británie a Severní Irsko) a Tomáš KAŠPÁREK (203 Česká republika, domácí)

Vydání

Warsaw, Los Alamitos, Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, od s. 263-268, 6 s. 2015

Nakladatel

Polskie Towarzystwo Informatyczne, IEEE

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Polsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Kód RIV

RIV/00216224:14110/15:00088877

Organizační jednotka

Lékařská fakulta

ISBN

978-83-60810-66-8

ISSN

DOI

https://doi.org/10.15439/2015F147

UT WoS

000376494300031

EID Scopus

2-s2.0-84958051750

Klíčová slova anglicky

pattern recognition; computational neuroanatomy; classification; penalized linear discriminant analysis with resampling; deformation-based morphometry; magnetic resonance imaging; schizophrenia

Štítky

EL OK

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 13. 12. 2016 12:30, Ing. Mgr. Věra Pospíšilíková

Anotace

V originále

This paper presents a new data-driven classification pipeline for discriminating two groups of individuals based on the medical images of their brain. The algorithm combines deformation-based morphometry and penalised linear discriminant analysis with resampling. The method is based on sparse representation of the original brain images using deformation logarithms reflecting the differences in the brain in comparison to the normal template anatomy. The sparse data enables efficient data reduction and classification via the penalised linear discriminant analysis with resampling. The classification accuracy obtained in an experiment with magnetic resonance brain images of first episode schizophrenia patients and healthy controls is comparable to the related state-of-the-art studies.

Návaznosti

NT13359, projekt VaV

Název: Pokročilé metody rozpoznávání MR obrazů mozku pro podporu diagnostiky neuropsychiatrických poruch

Investor: Ministerstvo zdravotnictví ČR, Zapojení prvku umělé inteligence do plánování efektivního operačního programu

Citovat

JANOUŠOVÁ, Eva; Daniel SCHWARZ; Giovanni MONTANA a Tomáš KAŠPÁREK. Brain Image Classification Based on Automated Morphometry and Penalised Linear Discriminant Analysis with Resampling. Online. In Maria Ganzha, Leszek Maciaszek, Marcin Paprzycki. Proceedings of the 2015 Federated Conference on Computer Science and Information Systems. Warsaw, Los Alamitos: Polskie Towarzystwo Informatyczne, IEEE, 2015, s. 263-268. ISBN 978-83-60810-66-8. Dostupné z: https://doi.org/10.15439/2015F147.

@inproceedings{1352750,
   author = {Janoušová, Eva and Schwarz, Daniel and Montana, Giovanni and Kašpárek, Tomáš},
   address = {Warsaw, Los Alamitos},
   booktitle = {Proceedings of the 2015 Federated Conference on Computer Science and Information Systems},
   doi = {https://doi.org/10.15439/2015F147},
   editor = {Maria Ganzha, Leszek Maciaszek, Marcin Paprzycki},
   keywords = {pattern recognition; computational neuroanatomy; classification; penalized linear discriminant analysis with resampling; deformation-based morphometry; magnetic resonance imaging; schizophrenia},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Warsaw, Los Alamitos},
   isbn = {978-83-60810-66-8},
   pages = {263-268},
   publisher = {Polskie Towarzystwo Informatyczne, IEEE},
   title = {Brain Image Classification Based on Automated Morphometry and Penalised Linear Discriminant Analysis with Resampling},
   url = {https://fedcsis.org/proceedings/2015/pliks/147.pdf},
   year = {2015}
}

TY  - CONF
ID  - 1352750
AU  - Janoušová, Eva - Schwarz, Daniel - Montana, Giovanni - Kašpárek, Tomáš
PY  - 2015
TI  - Brain Image Classification Based on Automated Morphometry and Penalised Linear Discriminant Analysis with Resampling
PB  - Polskie Towarzystwo Informatyczne, IEEE
CY  - Warsaw, Los Alamitos
SN  - 9788360810668
KW  - pattern recognition
KW  - computational neuroanatomy
KW  - classification
KW  - penalized linear discriminant analysis with resampling
KW  - deformation-based morphometry
KW  - magnetic resonance imaging
KW  - schizophrenia
UR  - https://fedcsis.org/proceedings/2015/pliks/147.pdf
N2  - This paper presents a new data-driven classification pipeline for discriminating two groups of individuals based on the medical images of their brain. The algorithm combines deformation-based morphometry and penalised linear discriminant analysis with resampling. The method is based on sparse representation of the original brain images using deformation logarithms reflecting the differences in the brain in comparison to the normal template anatomy. The sparse data enables efficient data reduction and classification via the penalised linear discriminant analysis with resampling. The classification accuracy obtained in an experiment with magnetic resonance brain images of first episode schizophrenia patients and healthy controls is comparable to the related state-of-the-art studies.
ER  -

JANOUŠOVÁ, Eva; Daniel SCHWARZ; Giovanni MONTANA a Tomáš KAŠPÁREK. Brain Image Classification Based on Automated Morphometry and Penalised Linear Discriminant Analysis with Resampling. Online. In Maria Ganzha, Leszek Maciaszek, Marcin Paprzycki. \textit{Proceedings of the 2015 Federated Conference on Computer Science and Information Systems}. Warsaw, Los Alamitos: Polskie Towarzystwo Informatyczne, IEEE, 2015, s.~263-268. ISBN~978-83-60810-66-8. Dostupné z: https://doi.org/10.15439/2015F147.

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