發(fā)布時(shí)間：2018-08-19 07:45

【摘要】： 隨著人腦成像技術(shù)的發(fā)展,腦成像在腦科學(xué)和神經(jīng)科學(xué),神經(jīng)外科等研究中具有越來(lái)越重要的地位。為對(duì)從個(gè)體人腦數(shù)據(jù)到人群中腦數(shù)據(jù)進(jìn)行形態(tài)和功能的分析、比較,迫切需要研究數(shù)學(xué)方法和有效的計(jì)算手段,計(jì)算解剖學(xué)這一學(xué)科應(yīng)運(yùn)而生。針對(duì)人腦數(shù)據(jù)的計(jì)算神經(jīng)解剖學(xué)重點(diǎn)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(wèn)題的數(shù)學(xué)和計(jì)算方法。本文主要研究了計(jì)算神經(jīng)解剖學(xué)中若干個(gè)重要問(wèn)題,即人腦圖像中的結(jié)構(gòu)和解剖標(biāo)記點(diǎn)的提取、基于彈性模型的人腦形變配準(zhǔn)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(yīng)用。 在人腦MRI圖像的分割方面。針對(duì)腦與非腦組織的分割問(wèn)題,分別使用較為簡(jiǎn)單的邊界分割方法和基于分水嶺的方法實(shí)現(xiàn)腦與非腦組織的分割。對(duì)腦組織的皮質(zhì)分割,實(shí)現(xiàn)了一個(gè)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(duì)分割的影響。在人腦解剖標(biāo)記點(diǎn)提取方面,闡述了基于等值面曲率模型的數(shù)學(xué)方法,并實(shí)驗(yàn)驗(yàn)證半自動(dòng)方法提取標(biāo)記點(diǎn),為后續(xù)的圖像配準(zhǔn)提供對(duì)應(yīng)的解剖標(biāo)記點(diǎn)。 對(duì)人腦圖像的彈性配準(zhǔn)的有限元計(jì)算方法首先闡述了其離散化的方法,并對(duì)二維圖像的網(wǎng)格剖分提出了一個(gè)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(shí)利用解剖標(biāo)記點(diǎn)作為預(yù)先的圖像剛性初配準(zhǔn),并作為有限元計(jì)算的位移條件加快計(jì)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(fù)雜形態(tài)的解剖結(jié)構(gòu),對(duì)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(dòng)識(shí)別方面都具有重要意義。我們提出了基于測(cè)地距離的K-均值空間聚類算法,提出聚類中心點(diǎn)的快速估計(jì)方法。從而實(shí)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(shí)現(xiàn)了一套基于可編程GPU的可視化和圖像處理的基本應(yīng)用框架。提出了補(bǔ)償體繪制質(zhì)量的幾種方法。對(duì)大規(guī)模體繪制問(wèn)題,我們提出了基于矢量量化壓縮后的體數(shù)據(jù)進(jìn)行實(shí)時(shí)解碼和繪制,從而為大規(guī)模體數(shù)據(jù)的繪制帶來(lái)了新的基于硬件的快速方法。對(duì)GPU作為一種廉價(jià)的可并行計(jì)算的處理器,進(jìn)行了一些并行圖像處理方法的實(shí)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(jì)算可以得到很好的計(jì)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(xué)位授予單位】：東南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2005
【分類號(hào)】：R319;R322

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本文編號(hào)：2191112