Fuzzy Computer-aided Diagnosis of Alzheimer's Disease Using MRI and PET Statistical Features

被引：0

作者：

Krashenyi, Igor ^{[1
]}

Popov, Anton ^{[1
]}

Ramirez, Javier ^{[2
]}

Manuel Gorriz, Juan ^{[2
]}

机构：

[1] Natl Tech Univ Ukraine, Kyiv Polytech Inst, Phys & Biomed Elect Dept, Kiev, Ukraine

[2] Univ Granada, Dept Signal Theory Telemat & Commun, Granada, Spain

来源：

2016 IEEE 36TH INTERNATIONAL CONFERENCE ON ELECTRONICS AND NANOTECHNOLOGY (ELNANO) | 2016年

关键词：

Alzheimer's disease; positron emission tomography; fuzzy logic; magnetic resonance imaging; fuzzy inference system;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, MRI and PET image features from selected brain regions are used as inputs in a fuzzy inference classification system for automatic diagnosis of Alzheimer's Disease (AD). Mean values of voxel intensity in spatial regions of interest which are extracted from normalized MRI and PET scans of brain gray matter were used as features. Area under receiver operating characteristic (AUC) was used as a classification performance measure, being function of the number of brain anatomical and functional regions of interest from which the features were extracted. In the result, combination of features from 7 MRI regions and 39 PET regions gave the highest performance of classification (AUC=0.94).

引用

页码：187 / 191

页数：5

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