哈尔滨工业大学工学硕士学位论文
Abstract
In recent years, the Alzheimer disease (AD) has become a major threat of human health with its incidence rate ascending year by year. Traditionally, AD diagnosis mainly depends on the doctor's diagnosis, MMSE and other unprecise methods, easily to misjudge thus delaying the timing of treatment. Early diagnosis of AD is very important for patients, because once happened, we would miss the opportunity to cure the disease, there will be no way to treat effectively. Early diagnosis and treatment of AD can help to improve the quality of life of the patients and delay the course of disease. The resting-state functional magnetic resonance imaging (rs-fMRI) which precisely reflects the brain changes on the resting state of individuals provides a quantitative approach to extract features, which has been introduced to distinguish AD patients from normal population, and achieve the result of early diagnosis.
In this study, we carried out a comprehensive study of feature extraction based on rs-fMRI. In view of the fact that the current academic research only focuses on the unilateral features of MRI data, we constructed the feature model and screened the features based on the brain functional network, regional homogeneity (ReHo) and amplitude of low-frequency (ALFF) fluctuation. In the experiment, we firstly extracted ReHo and ALFF value of each voxel from each sample’s MRI data, then we partitioned the brain according to the template. and the mean value and variance of those parameters were regarded as the feature components. In the next steps, we constructed the brain functional network and analyzed the network according to the graph theory, so we got all the features.
The recognition performance of some pattern recognition algorithms were compared, and the best classifier were applied to the feature selecting experiment in the next step. The result shows that, those algorithms with simple principle didn’t shows weak performance in the experiment, such as Support V ector Machine (SVM) with linear kernel function and Native Bayesian method, and SVM with linear kernel were selected as the best classifier to take part in the next experiment.
We proposed a neural fingerprint building method based on Fisher score and linear-kernel SVM, and constructed the rs-fMRI neural fingerprint of AD, we can get a recognition of 100% using linear-kernel SVM on our feature sets. Firstly we calculated the Fisher score of each feature, and sorted the features according to the score. Then, under the participation of SVM, we finally got the best feature subsets.
Keywords: Alzheimer disease, magnetic resonance imaging, feature extraction, neural fingerprint
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哈尔滨工业大学工学硕士学位论文
目录
摘要 ............................................................................................................................... I Abstract ............................................................................................................................. I I 第1章绪论 (1)
1.1课题背景及研究的目的和意义 (1)
1.2国内外研究现状及分析 (2)
1.2.1fMRI在脑疾病领域的研究现状 (3)
1.2.2模式分类在神经影像领域的应用现状 (5)
1.3主要研究内容及论文结构 (5)
1.3.1本文主要研究内容 (5)
1.3.2论文结构安排 (6)
第2章磁共振成像简介 (7)
2.1引言 (7)
2.2磁共振成像发展概述 (7)
2.3磁共振成像技术原理 (8)
2.4磁共振成像的分类 (11)
2.4.1磁共振结构像 (11)
2.4.2磁共振功能像 (13)
2.5静息态功能磁共振成像的量化分析简介 (14)
2.6本章小结 (15)
第3章功能磁共振图像的特征提取 (16)
3.1引言 (16)
3.2临床数据的来源 (16)
3.2.1ADNI数据库简介 (16)
3.2.2临床数据扫描参数 (16)
3.3数据预处理 (17)
3.4基于局部一致性参数和低频振幅参数的特征提取 (19)
3.4.1参数定义 (19)
3.4.2体素级参数提取 (20)
3.4.3脑区级参数提取 (24)
3.5基于脑功能连接网络的图论参数特征提取 (24)
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哈尔滨工业大学工学硕士学位论文
3.6本章小结 (31)
第4章功能磁共振图像的特征选择及神经指纹的建立 (32)
4.1引言 (32)
4.2不同模式识别分类器的性能检测及最佳分类器的选取 (32)
4.2.1逻辑回归 (32)
4.2.2支持向量机 (34)
4.2.3朴素贝叶斯分类 (37)
4.2.4分类器效果测试 (38)
4.3基于线性核函数支持向量机与Fisher score构建神经指纹 (40)
4.4本章小结 (44)
结论 (46)
参考文献 (48)
攻读硕士学位期间发表的论文及其它成果 (52)
哈尔滨工业大学学位论文原创性声明和使用权限 (53)
致谢 (54)
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