基于磁共振图像的阿尔茨海默病神经指纹研究
发布时间：2018-07-15 15:56
【摘要】：随着全球逐步步入老龄化,阿尔茨海默病(Alzheimer Disease,AD)渐已成为当今社会威胁人类身体健康、家庭生活质量和社会良性发展的最为严重的疾病之一。但是鉴于目前阿尔茨海默病只能延缓无法做到完全治愈,因此对于阿尔茨海默病的早期诊断也就愈发重要。目前对于AD的诊断仍然依赖于临床症状和专业医师的主观判断,磁共振成像技术(Magnetic Resonance Imaging,MRI)的出现提供了一种非介入式的无创脑疾病检查方式。虽然目前利用MRI观察AD患者发现了诸如海马体积变化等现象,但由于多脑区图像的分割精度过低,因此尚未完全解决AD的特征量化问题。本研究基于多脑图谱自动分割,从图谱预选择方法出发,完成对弥散张量图像的分割并研究感兴趣区域的纹理特征,利用所选脑结构和纹理特征构建AD神经指纹模型,为AD的研究提供新的思路。由于AD在影像学上没有显著的病灶区,对AD的MR图像量化分析时就需要针对大脑的各组织结构进行具体研究,因此,对于大脑组织结构进行准确的分割也就显得尤为重要。针对目前分割精度最高的基于多图谱的图像分割结果精度尚有提升空间的现状,本文在多图谱图像分割方法的图谱选择阶段,提出了两种新的图谱预选择方法,一种是利用侧脑室结构标签进行图谱预选择,另一种是将侧脑室、脑白质、脑灰质以及脑脊液四个结构标签融合为一个新标签图谱进行图谱预选择,并使用约翰霍普金斯大学影像中心的图谱数据库和图像自动分割方法,实现了对于T1脑图像的分割,实验结果表明,本文提出的两种图谱预选方法提高了图像的分割精度的同时缩短了分割时间,为多模态参数图的分割并构建神经指纹奠定了基础。在利用新的图谱预选择方法实现了对T1图像精准分割的基础上,将T1结构图像分割结果映射到弥散张量成像(Diffusion Resonance Imaging,DTI)多模态参数图上,实现了对多模态参数图的精准分割。针对目前已有AD的量化特征不明显的现状,本文选取AD患者脑结构性状发生改变的28个重点区域,提取了13个纹理特征并利用过滤法进行特征筛选,最终针对三类多模态参数图确定了10个特征和23个感兴趣区域,构建了具有显著意义的AD神经指纹模型。本文的研究成果表明了通过改进图谱预选择方法可以有效提高多图谱磁共振图像的分割精度,同时验证了结合多模态参数图提取纹理特征构建神经指纹的可行性,为针对AD进行更全面的研究奠定了科学基础。
[Abstract]:With the aging of the world, Alzheimer disease (AD) has become one of the most serious diseases threatening human health, family life quality and social benign development. But given that Alzheimer's can only delay a complete cure, early diagnosis of Alzheimer's is becoming increasingly important. At present, the diagnosis of AD still depends on clinical symptoms and subjective judgment of professional doctors. Magnetic Resonance Imaging (MRI) provides a non-interventional non-invasive examination of brain diseases. Although MRI has been used to observe AD patients, such as hippocampal volume changes, but the segmentation accuracy of multi-brain images is too low, so the problem of AD feature quantization has not been completely solved. Based on the automatic segmentation of multi-brain atlas, the segmentation of diffuse Zhang Liang images and the study of texture features of regions of interest were completed based on the pre-selection method, and the AD neural fingerprint model was constructed by using the selected brain structure and texture features. To provide a new idea for the study of AD. Since AD has no obvious focus area on imaging, it is necessary to study the structure of brain tissue in the quantitative analysis of AD image. Therefore, it is very important to segment the structure of brain tissue accurately. In view of the fact that there is still room for improvement in the accuracy of image segmentation based on multi-atlas which has the highest segmentation accuracy at present, this paper proposes two new pre-selection methods for multi-atlas image segmentation in the phase of spectrum selection of multi-atlas image segmentation method. One is to preselect the map by using the label of the lateral ventricle structure, the other is to fuse the four structural labels of lateral ventricle, white matter, gray matter and cerebrospinal fluid into a new label for pre-selection. Using the map database of Johns Hopkins University Image Center and the automatic image segmentation method, the T1 brain image segmentation is realized. The experimental results show that, The two methods proposed in this paper not only improve the accuracy of image segmentation but also shorten the segmentation time, which lays a foundation for the segmentation of multimodal parameter images and the construction of neural fingerprints. Based on the accurate segmentation of T1 images by using a new method of map pre-selection, the segmentation results of T1 structure images are mapped to the multimodal parameter diagrams of Diffusion Resonance Imaging (Zhang Liang), and the precise segmentation of multimodal parametric images is realized. In view of the fact that the quantitative characteristics of AD are not obvious at present, this paper selects 28 key regions in which the brain structural traits of AD patients change, and extracts 13 texture features and selects them by filter method. Finally, 10 features and 23 regions of interest are determined for the three types of multimodal parameter maps, and a significant AD neural fingerprint model is constructed. The research results of this paper show that the segmentation accuracy of multispectral magnetic resonance image can be improved effectively by improving the pre-selection method of map. At the same time, the feasibility of constructing neural fingerprint by extracting texture feature from multi-modal parameter graph is verified. It lays a scientific foundation for a more comprehensive study of AD.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：R749.16;R445.2;TP391.41
[Abstract]:With the aging of the world, Alzheimer disease (AD) has become one of the most serious diseases threatening human health, family life quality and social benign development. But given that Alzheimer's can only delay a complete cure, early diagnosis of Alzheimer's is becoming increasingly important. At present, the diagnosis of AD still depends on clinical symptoms and subjective judgment of professional doctors. Magnetic Resonance Imaging (MRI) provides a non-interventional non-invasive examination of brain diseases. Although MRI has been used to observe AD patients, such as hippocampal volume changes, but the segmentation accuracy of multi-brain images is too low, so the problem of AD feature quantization has not been completely solved. Based on the automatic segmentation of multi-brain atlas, the segmentation of diffuse Zhang Liang images and the study of texture features of regions of interest were completed based on the pre-selection method, and the AD neural fingerprint model was constructed by using the selected brain structure and texture features. To provide a new idea for the study of AD. Since AD has no obvious focus area on imaging, it is necessary to study the structure of brain tissue in the quantitative analysis of AD image. Therefore, it is very important to segment the structure of brain tissue accurately. In view of the fact that there is still room for improvement in the accuracy of image segmentation based on multi-atlas which has the highest segmentation accuracy at present, this paper proposes two new pre-selection methods for multi-atlas image segmentation in the phase of spectrum selection of multi-atlas image segmentation method. One is to preselect the map by using the label of the lateral ventricle structure, the other is to fuse the four structural labels of lateral ventricle, white matter, gray matter and cerebrospinal fluid into a new label for pre-selection. Using the map database of Johns Hopkins University Image Center and the automatic image segmentation method, the T1 brain image segmentation is realized. The experimental results show that, The two methods proposed in this paper not only improve the accuracy of image segmentation but also shorten the segmentation time, which lays a foundation for the segmentation of multimodal parameter images and the construction of neural fingerprints. Based on the accurate segmentation of T1 images by using a new method of map pre-selection, the segmentation results of T1 structure images are mapped to the multimodal parameter diagrams of Diffusion Resonance Imaging (Zhang Liang), and the precise segmentation of multimodal parametric images is realized. In view of the fact that the quantitative characteristics of AD are not obvious at present, this paper selects 28 key regions in which the brain structural traits of AD patients change, and extracts 13 texture features and selects them by filter method. Finally, 10 features and 23 regions of interest are determined for the three types of multimodal parameter maps, and a significant AD neural fingerprint model is constructed. The research results of this paper show that the segmentation accuracy of multispectral magnetic resonance image can be improved effectively by improving the pre-selection method of map. At the same time, the feasibility of constructing neural fingerprint by extracting texture feature from multi-modal parameter graph is verified. It lays a scientific foundation for a more comprehensive study of AD.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：R749.16;R445.2;TP391.41
【参考文献】
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