Automatic selection of ROIs in functional imaging using Gaussian mixture models

被引：36

作者：

Gorriz, J. M. ^{[1
]}

Lassl, A. ^{[1
]}

Ramirez, J. ^{[1
]}

Salas-Gonzalez, D. ^{[1
]}

Puntonet, C. G. ^{[2
]}

Lang, E. W. ^{[3
]}

机构：

[1] Univ Granada, Dept Teoria Se Nal Telemat & Comunicac, E-18071 Granada, Spain

[2] Univ Granada, Dept Arquitectura & Tecnol Comp, E-18071 Granada, Spain

[3] Univ Regensburg, Dept Computat Intelligence & Machine Learning, D-8400 Regensburg, Germany

来源：

NEUROSCIENCE LETTERS | 2009年 / 460卷 / 02期

关键词：

SPECT brain imaging classification; Computer-aided diagnosis; Alzheimer's disease; ALZHEIMERS-DISEASE; SPECT IMAGES; CLASSIFICATION;

D O I：

10.1016/j.neulet.2009.05.039

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

We present an automatic method for selecting regions of interest (ROIs) of the information contained in three-dimensional functional brain images using Gaussian mixture models (GMMs), where each Gaussian incorporates a contiguous brain region with similar activation. The novelty of the approach is based on approximating the grey-level distribution of a brain image by a sum of Gaussian functions, whose parameters are determined by a maximum likelihood criterion via the expectation maximization (EM) algorithm. Each Gaussian or cluster is represented by a multivariate Gaussian function with a center coordinate and a certain shape. This approach leads to a drastic compression of the information contained in the brain image and serves as a starting point for a variety of possible feature extraction methods for the diagnosis of brain diseases. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

引用

页码：108 / 111

页数：4

共 18 条

[11] FITTING MIXTURE-MODELS TO GROUPED AND TRUNCATED DATA VIA THE EM ALGORITHM [J].

MCLACHLAN, GJ ;

JONES, PN .

BIOMETRICS, 1988, 44 (02) :571-578

[12]

NEWMAN J, 2008, NEUROIMAGE, V29, P177

[13] SPECT in Alzheimer's disease: features associated with bilateral parietotemporal hypoperfusion [J].

Nitrini, R ;

Buchpiguel, CA ;

Caramelli, P ;

Bahia, VS ;

Mathias, SC ;

Nascimento, CMR ;

Degenszajn, J ;

Caixeta, L .

ACTA NEUROLOGICA SCANDINAVICA, 2000, 101 (03) :172-176

[14]

Platt JC, 1999, ADVANCES IN KERNEL METHODS, P185

[15] SMALL SAMPLE-SIZE EFFECTS IN STATISTICAL PATTERN-RECOGNITION - RECOMMENDATIONS FOR PRACTITIONERS [J].

RAUDYS, SJ ;

JAIN, AK .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1991, 13 (03) :252-264

[16]

Scholkopf B., 1998, Advances in Kernel Methods: Support Vector Learning

[17]

Stoeckel J, 2004, LECT NOTES COMPUT SC, V3217, P654

[18]

Vapnik V.N., 1998, STAT LEARNING THEORY, V1

← 1 2 →