基于脑皮层形态学的多任务模型及其分类研究
发布时间：2018-08-04 09:20
【摘要】：大脑是人身体中最重要、最复杂的器官,相当于计算机的CPU,支配着人们的思维和各种行为,研究大脑的组织结构和工作机理对于人类身体健康具有重要意义。阿尔兹海默症(AD),是神经系统退行性的一种疾病,而且该疾病的起病和发展不容易被人们所发现,临床上记忆力、沟通和语言能力、行为能力、认知功能等持续下降甚至全面丧失,导致全面性痴呆,民间称老年痴呆症。轻度认知障碍(MCI)是介于正常衰老和痴呆之间的一种中间状态,与正常(NC)老人相比,患者有一定的轻度认知功能减退,但是还不足以表现为痴呆,是老年痴呆的高发人群。由于现今还无法治愈阿尔兹海默症,其病情具有不可逆性,在还没有转化为阿尔兹海默症的轻度认知障碍这一阶段进行预防和干预,对于控制患者的病情和延缓发病痴呆有明显效果。在越来越多的相关研究中表明,AD患者与正常老年人、MCI患者与正常老年人之间脑部组织结构发生了变化,脑皮层存在差异,借助核磁共振成像(MRI)技术,对采集到的大脑图像数据进行处理分析,研究AD和MCI脑结构组织的变化,对降低AD的发病和死亡率有重要的意义,为实现AD的早期预测和自动诊断提供理论依据。本文的主要贡献如下:1.对AD、MCI、NC这些被试人群的MRI脑影像数据,以基于曲面形态学的分析方法,计算出被试人群脑形态学的三种指标:皮层厚度、灰质体积、皮层复杂度。对三种被试人群,基于每种指标数据,利用统计分析的方法,比较每两种人群的大脑结构组织差异,证明每种指标下AD和NC及MCI和NC两种模型脑组织存在显著差异,为多任务学习选取特征提供依据,同时找出有哪些脑区存在异常。2.对AD和NC,MCI和NC两种模型分别进行分类实验。在该领域尝试运用多任务学习(MTL)方法来选择特征,对脑皮层形态学的三种指标:脑皮层厚度、灰质体积、皮层复杂度看成三个任务,选择特征时任务之间存在一定的联系。同时与F-score、mRMR两种特征选择方法进行比较。在特征选择的基础上,选择一个合适的分类器更为重要,本文尝试采用极限学习机(ELM)的分类方法,同时结合SVM-Linear、SVM-RBF两种分类器,探索目前MRI中常用的特征选择方法与分类器在AD和NC以及MCI和NC分类上的最优组合。
[Abstract]:The brain is the most important and complex organ in the human body, which is equivalent to the computer CPU, which dominates people's thinking and various behaviors. It is of great significance to study the structure and working mechanism of the brain for human health. Alzheimer's disease (AD),) is a degenerative disease of the nervous system, and the onset and development of the disease are not easily discovered. Cognitive function and other continuous decline or even total loss, leading to comprehensive dementia, known as Alzheimer's disease. Mild cognitive impairment (MCI) is a kind of intermediate state between normal aging and dementia. Compared with normal (NC) patients, the patients have mild cognitive impairment, but not enough to show dementia, which is a high risk group of senile dementia. Because there is no cure for Alzheimer's disease, and its condition is irreversible, prevention and intervention are carried out at a stage that has not yet transformed into mild cognitive impairment of Alzheimer's disease. To control the patient's condition and delay the onset of dementia has obvious effect. More and more related studies have shown that the brain tissue structure has changed between AD patients and normal elderly people. There are differences in cerebral cortex between AD patients and normal elderly patients. Magnetic resonance imaging (MRI) technique is used. Processing and analyzing the collected brain image data and studying the changes of brain structure in AD and MCI are of great significance to reduce the morbidity and mortality of AD and provide theoretical basis for early prediction and automatic diagnosis of AD. The main contributions of this paper are as follows: 1. In this paper, the MRI brain image data of the subjects were analyzed based on curved surface morphology, and three indexes of brain morphology were calculated: thickness of cortex, volume of gray matter and complexity of cortex. Based on the data of each index, the differences of brain structure in each group were compared by statistical analysis. It was proved that there were significant differences between AD and NC and MCI and NC in each index. To provide the basis for selecting the characteristics of multitask learning, and to find out which brain regions have abnormal. 2. 2. Classification experiments were carried out on AD and NCU MCI and NC models respectively. In this field, the multi-task learning (MTL) method is used to select the features. The cortical thickness, gray matter volume, cortical complexity are regarded as three tasks, and there is a certain relationship between the tasks when selecting the features. At the same time, it is compared with two feature selection methods of F-scorex mRMR. On the basis of feature selection, it is more important to select a suitable classifier. In this paper, we try to use the classification method of extreme learning machine (ELM) and combine SVM-Linear-SVM-RBF classifier. This paper explores the optimal combination of feature selection methods and classifiers in AD and NC and MCI and NC classification in MRI.
【学位授予单位】：山东师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R749.16;TP391.41
本文编号：2163374
[Abstract]:The brain is the most important and complex organ in the human body, which is equivalent to the computer CPU, which dominates people's thinking and various behaviors. It is of great significance to study the structure and working mechanism of the brain for human health. Alzheimer's disease (AD),) is a degenerative disease of the nervous system, and the onset and development of the disease are not easily discovered. Cognitive function and other continuous decline or even total loss, leading to comprehensive dementia, known as Alzheimer's disease. Mild cognitive impairment (MCI) is a kind of intermediate state between normal aging and dementia. Compared with normal (NC) patients, the patients have mild cognitive impairment, but not enough to show dementia, which is a high risk group of senile dementia. Because there is no cure for Alzheimer's disease, and its condition is irreversible, prevention and intervention are carried out at a stage that has not yet transformed into mild cognitive impairment of Alzheimer's disease. To control the patient's condition and delay the onset of dementia has obvious effect. More and more related studies have shown that the brain tissue structure has changed between AD patients and normal elderly people. There are differences in cerebral cortex between AD patients and normal elderly patients. Magnetic resonance imaging (MRI) technique is used. Processing and analyzing the collected brain image data and studying the changes of brain structure in AD and MCI are of great significance to reduce the morbidity and mortality of AD and provide theoretical basis for early prediction and automatic diagnosis of AD. The main contributions of this paper are as follows: 1. In this paper, the MRI brain image data of the subjects were analyzed based on curved surface morphology, and three indexes of brain morphology were calculated: thickness of cortex, volume of gray matter and complexity of cortex. Based on the data of each index, the differences of brain structure in each group were compared by statistical analysis. It was proved that there were significant differences between AD and NC and MCI and NC in each index. To provide the basis for selecting the characteristics of multitask learning, and to find out which brain regions have abnormal. 2. 2. Classification experiments were carried out on AD and NCU MCI and NC models respectively. In this field, the multi-task learning (MTL) method is used to select the features. The cortical thickness, gray matter volume, cortical complexity are regarded as three tasks, and there is a certain relationship between the tasks when selecting the features. At the same time, it is compared with two feature selection methods of F-scorex mRMR. On the basis of feature selection, it is more important to select a suitable classifier. In this paper, we try to use the classification method of extreme learning machine (ELM) and combine SVM-Linear-SVM-RBF classifier. This paper explores the optimal combination of feature selection methods and classifiers in AD and NC and MCI and NC classification in MRI.
【学位授予单位】：山东师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R749.16;TP391.41
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