【摘要】： 隨著人腦成像技術(shù)的發(fā)展,腦成像在腦科學和神經(jīng)科學,神經(jīng)外科等研究中具有越來越重要的地位。為對從個體人腦數(shù)據(jù)到人群中腦數(shù)據(jù)進行形態(tài)和功能的分析、比較,迫切需要研究數(shù)學方法和有效的計算手段,計算解剖學這一學科應運而生。針對人腦數(shù)據(jù)的計算神經(jīng)解剖學重點研究的內(nèi)容包括人腦圖譜的建模、形變模型、結(jié)構(gòu)和功能的映射分析等幾大問題的數(shù)學和計算方法。本文主要研究了計算神經(jīng)解剖學中若干個重要問題,即人腦圖像中的結(jié)構(gòu)和解剖標記點的提取、基于彈性模型的人腦形變配準技術(shù)、人腦皮層圖像的解剖分塊方法,最后研究了可編程GPU(圖形處理器)技術(shù)在數(shù)字腦可視化和圖像快速處理中的應用。 在人腦MRI圖像的分割方面。針對腦與非腦組織的分割問題,分別使用較為簡單的邊界分割方法和基于分水嶺的方法實現(xiàn)腦與非腦組織的分割。對腦組織的皮質(zhì)分割,實現(xiàn)了一個基于概率圖譜的模糊聚類方法,并研究了圖像中的組織灰度不均勻性對分割的影響。在人腦解剖標記點提取方面,闡述了基于等值面曲率模型的數(shù)學方法,并實驗驗證半自動方法提取標記點,為后續(xù)的圖像配準提供對應的解剖標記點。 對人腦圖像的彈性配準的有限元計算方法首先闡述了其離散化的方法,并對二維圖像的網(wǎng)格剖分提出了一個與圖像特征相關(guān)的網(wǎng)格劃分算法,使得網(wǎng)格具有一定的圖譜特征。同時利用解剖標記點作為預先的圖像剛性初配準,并作為有限元計算的位移條件加快計算的收斂速度和精度。 人腦皮層體數(shù)據(jù)圖像是由腦的溝回組成的有復雜形態(tài)的解剖結(jié)構(gòu),對其按功能和解剖特征分塊在fMRI數(shù)據(jù)分析和皮層腦溝回的自動識別方面都具有重要意義。我們提出了基于測地距離的K-均值空間聚類算法,提出聚類中心點的快速估計方法。從而實現(xiàn)了人腦皮層數(shù)據(jù)的與近似解剖特征的皮層分塊。 為提高數(shù)字腦體繪制的成像質(zhì)量和加快圖像處理,我們實現(xiàn)了一套基于可編程GPU的可視化和圖像處理的基本應用框架。提出了補償體繪制質(zhì)量的幾種方法。對大規(guī)模體繪制問題,我們提出了基于矢量量化壓縮后的體數(shù)據(jù)進行實時解碼和繪制,從而為大規(guī)模體數(shù)據(jù)的繪制帶來了新的基于硬件的快速方法。對GPU作為一種廉價的可并行計算的處理器,進行了一些并行圖像處理方法的實現(xiàn)研究,如Level Set方法,骨架提取算法等。結(jié)果表明,采用GPU計算可以得到很好的計算加速性能。
[Abstract]:With the development of brain imaging technology, brain imaging plays a more and more important role in brain science, neuroscience, neurosurgery and so on. In order to analyze the morphology and function of human brain data from individual human brain data to human brain data, it is urgent to study mathematical methods and effective calculation methods, and the subject of computational anatomy emerges as the times require. The main contents of the research on the computational neuroanatomy of human brain data include the modeling of human brain atlas, deformation model, mapping analysis of structure and function, and so on. In this paper, some important problems in computational neuroanatomy, such as the extraction of structures and anatomical markers in human brain images, the technique of human brain deformation registration based on elastic model, and the anatomical segmentation of human cortical images are studied in this paper. Finally, the application of programmable GPU technology in digital brain visualization and image processing is studied. In the human brain MRI image segmentation. To solve the problem of brain and non-brain tissue segmentation, a simple boundary segmentation method and a watershed based method are used to segment brain and non-brain tissue, respectively. A fuzzy clustering method based on probabilistic map is implemented for cortical segmentation of brain tissue, and the effect of tissue grayscale heterogeneity on segmentation is studied. In the aspect of human brain anatomical mark point extraction, the mathematical method based on isosurface curvature model is expounded, and the semi-automatic method is verified by experiment, which provides the corresponding anatomical mark points for the subsequent image registration. The finite element method for the elastic registration of human brain images is introduced in this paper. Firstly, the discretization method is introduced, and a mesh generation algorithm related to the image features is proposed for the mesh generation of two-dimensional images, which makes the meshes have a certain graph feature. At the same time, the anatomic mark point is used as the initial registration of image rigidity, and the displacement condition calculated by finite element method is used to accelerate the convergence speed and accuracy of the calculation. The cortical body data image of human brain is a complex anatomical structure composed of the sulcus gyrus of the brain, which is of great significance in the analysis of fMRI data and the automatic recognition of the cortical gyrus according to its function and anatomical characteristics. We propose a K-means space clustering algorithm based on geodesic distance and a fast estimation method for clustering center points. Thus, the cortical block of human brain cortical data and similar anatomical features is realized. In order to improve the imaging quality of digital brain volume rendering and speed up image processing, we have implemented a set of basic application framework of visualization and image processing based on programmable GPU. Several methods of compensating volume rendering quality are presented. To solve the large-scale volume rendering problem, we propose a real-time decoding and rendering of volume data based on vector quantization compression, which brings a new fast method based on hardware for large-scale volume data rendering. As a cheap parallel computing processor, GPU is studied in the realization of some parallel image processing methods, such as Level Set method, skeleton extraction algorithm and so on. The results show that the acceleration performance can be obtained by GPU calculation.
【學位授予單位】：東南大學
【學位級別】：博士
【學位授予年份】：2005
【分類號】：R319;R322

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本文編號：2191112