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

JANOUŠOVÁ, Eva, Daniel SCHWARZ and 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, p. 83-87. ISBN 978-0-9566150-0-8.

Basic information

• Original name: Classification of 3-D MRI Brain Data Using Modified Maximum Uncertainty Linear Discriminant Analysis
• Authors: JANOUŠOVÁ, Eva, Daniel SCHWARZ and Tomáš KAŠPÁREK.
• Edition: Coventry, United Kingdom, Proceedings of Medical Image Understanding and Analysis 2010, p. 83-87, 5 pp. 2010.
• Publisher: University of Warwick

Other information

• Original language: English
• Type of outcome: Proceedings paper
• Field of Study: 30000 3. Medical and Health Sciences
• Country of publisher: Czech Republic
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Organization unit: Faculty of Medicine
• ISBN: 978-0-9566150-0-8
• Keywords (in Czech): Klasifikace, analýza hlavních komponent, lineární diskriminační analýza, MRI, výpočetní neuroanatomie, schizofrenie
• Keywords in English: Classification, Principal Component Analysis, Linear Discriminant Analysis, MRI, Computational Neuroanatomy, Schizophrenia
• Tags: International impact, Reviewed

Abstract

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.

Links

• NS10347, research and development project: Name: Moderní metody rozpoznávání pro analýzu obrazových dat v neuropsychiatrickém výzkumu

Investor: Ministry of Health of the CR

• NS9893, research and development project: Name: Predikce průběhu iniciálních fází schizofrenie pomocí morfologie mozku

Investor: Ministry of Health of the CR