Clustered component analysis for FMRI signal estimation and classification

Author

Bouman, Charles A. ; Chen, Sea ; Lowe, Mark J.

Author_Institution

Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN, USA

Volume

1

fYear

2000

fDate

2000

Firstpage

609

Abstract

In this paper, we introduce a method for estimating the statistically distinct neural responses in an sequence of functional magnetic resonance images (fMRI). The crux of our method is a technique which we call clustered component analysis (CCA). Clustered component analysis is a method for identifying the distinct component vectors in a multivariate data set. CCA is distinct from principal components analysis (PCA), and independent components analysis (ICA), because it is not constrained to produce orthogonal component vectors and it does not assume that components are independent. CCA employs Bayesian estimation methods such as expectation-maximization (EM) and Rissanen order identification to determine the best set of component vectors

Keywords

Bayes methods; biomedical MRI; estimation theory; image classification; iterative methods; medical image processing; neural nets; neurophysiology; pattern clustering; Bayesian estimation; CCA; FMRI signal estimation; Rissanen order identification; classification; clustered component analysis; distinct component vectors; expectation-maximization; functional magnetic resonance images; multivariate data set; statistically distinct neural responses; stimulus reponse; Bayesian methods; Clustering algorithms; Estimation; Independent component analysis; Magnetic analysis; Magnetic resonance imaging; Principal component analysis; Radiology; Signal analysis; Signal to noise ratio;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2000. Proceedings. 2000 International Conference on

Conference_Location

Vancouver, BC

ISSN

1522-4880

Print_ISBN

0-7803-6297-7

Type

conf

DOI

10.1109/ICIP.2000.901032

Filename

901032