Projecting independent components of SPECT images for computer aided diagnosis of Alzheimer's disease

被引：39

作者：

Alvarez Illan, I. ^{[1
]}

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

Ramirez, J. ^{[1
]}

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

Lopez, M. ^{[1
]}

Segovia, F. ^{[1
]}

Padilla, P. ^{[1
]}

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

机构：

[1] Univ Granada, Dept Signal Theory Networking & Commun, E-18071 Granada, Spain

[2] Univ Granada, Dept Comp Architecture & Technol, E-18071 Granada, Spain

来源：

PATTERN RECOGNITION LETTERS | 2010年 / 31卷 / 11期

关键词：

Alzheimer's disease; Independent Component Analysis; Computer aided diagnosis; Support vector machine; Supervised learning; ALGORITHMS; PERFUSION; HMPAO;

D O I：

10.1016/j.patrec.2010.03.004

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Finding sensitive and appropriate technologies for early detection of the Alzheimer's disease (AD) are of fundamental importance to develop early treatments. Single Photon Emission Computed Tomography (SPECT) images are non-invasive observation tools to assist the diagnosis, commonly processed through unsupervised statistical tests, or assessed visually. In this work, we present a computer aided diagnosis system based on supervised learning methods, exploring two different novel approaches. Independent Component Analysis (ICA) was used within this work to extract the relevant features from the image database and reduce the feature space dimensionality, to build a SVM with the resulting data. The proposed approach led to an error estimation below the 9%, and was able to detect the AD perfusion pattern and classify new subjects in an unsupervised manner. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：1342 / 1347

页数：6

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