本文關(guān)鍵詞：腦部MRI圖像中深層核團的分割方法研究　出處：《北京工業(yè)大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： MRI 配準(zhǔn) 模糊連接度 腦深層核團

【摘要】：臨床上對帕金森病、癲癇、阿爾茨海默病等神經(jīng)系統(tǒng)疾病的治療主要為使用藥物和實施立體定向手術(shù)。腦內(nèi)很多深層核團,如尾狀核、殼核、蒼白球、丘腦等都是手術(shù)中常見的目標(biāo)靶區(qū)。靶區(qū)的精確定位是手術(shù)成敗的關(guān)鍵,決定著手術(shù)的最終療效。因此,提高靶區(qū)的定位精度是目前亟待解決的臨床問題。本文對磁共振圖像(Magnetic Resonance Imaging,MRI)圖像中腦內(nèi)深層核團的自動識別技術(shù)進行研究。全面通過挖掘MRI圖像腦部結(jié)構(gòu)特點,采用兩種研究思路,實現(xiàn)腦內(nèi)深層核團的精確分割。本文主要研究內(nèi)容包括:1.實現(xiàn)一種基于配準(zhǔn)框架的腦內(nèi)深層核團的自動分割算法。首先,利用模板圖像,將待分割的若干目標(biāo)子結(jié)構(gòu)視為一個由若干子結(jié)構(gòu)組成的樹型結(jié)構(gòu)模板,根據(jù)目標(biāo)識別的難度對模板中各子結(jié)構(gòu)劃分等級;其次,依據(jù)整合結(jié)構(gòu)模板,建立能夠評價目標(biāo)核團識別效果的能量函數(shù),其各構(gòu)成項從不同角度描述了衡量識別效果的準(zhǔn)則,且融合了各子結(jié)構(gòu)間的相互關(guān)聯(lián)信息;再次,利用基于馬爾科夫場(Markov Random Field,MRF)的腦組織分割方法對整合結(jié)構(gòu)模板中的根結(jié)構(gòu)進行分割,并將其作為后續(xù)識別過程的初始化信息;最后,利用基于B樣條的FFD配準(zhǔn)對初步識別的各目標(biāo)結(jié)構(gòu)進行優(yōu)化修正。利用本算法對15例MRI圖像進行實驗,結(jié)果表明,尾狀核、殼核、蒼白球和丘腦結(jié)構(gòu)分割結(jié)果與手工分割結(jié)果相似度均大于0.75。與其它基于配準(zhǔn)框架的分割算法相比,本算法具有更高準(zhǔn)確率,分割結(jié)果的平均相似度為0.82。本文利用一種獲取先驗形狀模型的新方法,實現(xiàn)在同一框架下依次對多個腦內(nèi)深層核團的自動分割。其準(zhǔn)確率高,無須人工干預(yù),具有較高臨床應(yīng)用價值。2.實現(xiàn)一種基于改進模糊連接度的丘腦及其子結(jié)構(gòu)分割算法。基于在傳統(tǒng)模糊連接度框架內(nèi)增加梯度特征、采用自適應(yīng)權(quán)重的前期工作基礎(chǔ),利用黑白top-hat變換增強圖像對比度;結(jié)合置信連接度理論,在計算模糊親和度之前,對目標(biāo)核團所在感興趣區(qū)域進行自動劃分,并計算該區(qū)域內(nèi)灰度與梯度的統(tǒng)計特征。本算法僅需一個種子像素即可自動獲取目標(biāo)感興趣區(qū),在模糊連接度框架內(nèi)引入了梯度特征,并可實現(xiàn)權(quán)重的自適應(yīng)調(diào)整,減少了人工干預(yù),提高了分割準(zhǔn)確性。采用本算法對25例MRI圖像進行實驗,結(jié)果表明,分割結(jié)果與手工分割結(jié)果相似度均大于0.75。與其它分割算法比較,本文算法在準(zhǔn)確率上具有明顯優(yōu)勢,同時具有時間代價小、魯棒性強、主觀影響小的優(yōu)點。本研究實現(xiàn)了基于不同研究思路的腦部MRI圖像深層核團的分割方法,可為醫(yī)生提供更加科學(xué)直觀的影像學(xué)定位參考,為立體定向手術(shù)中深層腦部子結(jié)構(gòu)的自動識別提供技術(shù)支持。
[Abstract]:Clinical treatment of Parkinson's disease, epilepsy, Alzheimer's disease and other neurological diseases is mainly drug use and stereotactic surgery. Many deep nuclei in the brain, such as caudate nucleus, putamen nucleus, globus pallidus. Thalamus is a common target area in surgery. The accurate location of target area is the key to the success or failure of the operation and determines the final outcome of the operation. It is an urgent clinical problem to improve the accuracy of target location. Magnetic Resonance Imaging is studied in this paper. The automatic recognition technology of deep nuclei in the brain of MRI images was studied. Two kinds of research ideas were adopted by mining the brain structure characteristics of MRI images. The main contents of this paper include: 1. An automatic segmentation algorithm based on registration framework is implemented. First, template image is used. The target substructures to be segmented are regarded as a tree structure template composed of several substructures, and each substructure in the template is classified according to the difficulty of target recognition. Secondly, according to the integrated structure template, the energy function can be established to evaluate the effectiveness of the target nucleus recognition, and its components describe the criteria to measure the recognition effect from different angles. The interrelation information of each substructure is fused. Thirdly, the method of brain tissue segmentation based on Markov Random Random is used to segment the root structure in the integrated structure template. It is used as the initialization information of the subsequent identification process. Finally, the FFD registration based on B-spline is used to optimize the target structure of the initial recognition. 15 cases of MRI images are tested using this algorithm. The results show that the caudate core and shell core are obtained. The similarity between the segmentation results of pallidus and thalamus is greater than that of manual segmentation. Compared with other algorithms based on registration frame, this algorithm has a higher accuracy. The average similarity of segmentation results is 0.82.This paper uses a new method to obtain a priori shape model to realize automatic segmentation of multiple deep nuclei in the same frame. Without manual intervention, it has higher clinical application value. 2. To implement a segmentation algorithm of thalamus and its substructure based on improved fuzzy connectivity, based on adding gradient features in the framework of traditional fuzzy connectivity. The image contrast is enhanced by black and white top-hat transform based on the previous work of adaptive weight. Combining with the theory of confidence connectivity, the region of interest of the target nuclei is automatically divided before the fuzzy affinity degree is calculated. The statistical features of grayscale and gradient in this region are calculated. In this algorithm, only one seed pixel is needed to automatically obtain the region of interest of the target, and the gradient feature is introduced in the framework of fuzzy connectivity. The adaptive adjustment of weight can be realized, the artificial intervention is reduced, and the segmentation accuracy is improved. 25 cases of MRI images are tested by this algorithm, and the results show that. The similarity between the segmentation results and manual segmentation results is greater than 0.75. Compared with other segmentation algorithms, this algorithm has obvious advantages in accuracy, at the same time, it has low time cost and strong robustness. This study has realized the segmentation method of deep nuclei in brain MRI image based on different research ideas, which can provide a more scientific and intuitive imaging location reference for doctors. It provides technical support for automatic recognition of substructure of deep brain in stereotactic surgery.
【學(xué)位授予單位】：北京工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：R445.2;TP391.41

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