MRI腦部組織分割方法研究
發(fā)布時(shí)間：2018-08-07 14:01
【摘要】：核磁共振成像具有軟組織成像效果好、空間分辨率高、非介入性、掃描角度靈活等優(yōu)點(diǎn),已成為腦疾病診斷的重要輔助手段。準(zhǔn)確分割腦部組織對后續(xù)解剖腦部疾病,諸如阿爾茨海默病、多發(fā)性硬化癥、帕金森以及精神分裂癥的分析與研究具有重要的指導(dǎo)意義。由于腦組織物理屬性不同,在MR圖像上呈現(xiàn)不同的灰度范圍,高斯混合模型(Gaussian Mixture Model,GMM)已成為一種描述灰度緩慢變化的理想模型。但是傳統(tǒng)的GMM以像素的獨(dú)立性假設(shè)為前提,組織的空間結(jié)構(gòu)信息往往被忽略。同時(shí),由于腦組織自身解剖結(jié)構(gòu)的復(fù)雜性,加上成像過程當(dāng)中出現(xiàn)的偏移場、部分容積效應(yīng)、噪聲等物理性因素,導(dǎo)致MR圖像的分段常量性被破壞。為了提高腦組織分割精度,本文重點(diǎn)研究了基于組織概率圖譜先驗(yàn)信息和后驗(yàn)鄰域信息的高斯混合模型腦組織三維分割算法。具體研究內(nèi)容如下:1、提出了一種基于圖譜先驗(yàn)信息的高斯混合模型腦組織三維分割算法(PA-GMM)。實(shí)驗(yàn)結(jié)果表明PA-GMM算法可以解決傳統(tǒng)GMM由于空間信息缺失而導(dǎo)致在噪聲和偏移場增大情況下誤分率提高的情況,有效提高了腦組織分割精度。2、MR圖像當(dāng)中存在偏移場,而當(dāng)偏移場過大時(shí)候,會(huì)嚴(yán)重影響最后算法的分割精度。因此,本文在PA-GMM基礎(chǔ)之上,實(shí)現(xiàn)了一種基于偏移場校正的PA-GMM算法。實(shí)驗(yàn)結(jié)果表明,該方法可以快速有效對MR圖像進(jìn)行3D分割。通過分割與偏移場校正交替迭代進(jìn)行,比傳統(tǒng)預(yù)處理階段先進(jìn)行偏移場校正,然后再進(jìn)行組織分割的效果要好。3、為了進(jìn)一步提高算法在高噪聲下的分割精度,利用后驗(yàn)概率的鄰域信息和腦組織概率圖譜的空間解剖結(jié)構(gòu)先驗(yàn)信息,重新設(shè)計(jì)混合系數(shù)的表達(dá)方式,提出了一種SNPA-MGMM分割算法。該算法不僅能夠在抑制噪聲方面上表現(xiàn)突出,而且能夠分割像GM和CSF這樣的復(fù)雜重疊區(qū)域,并且能夠保留邊緣細(xì)節(jié)信息。4、本文主要采用BrainWeb的模擬數(shù)據(jù)集和IBSR的兩組真實(shí)數(shù)據(jù)集(v1.0和v2.0)作為測試數(shù)據(jù),并將改進(jìn)后的算法與一些最新文獻(xiàn)和醫(yī)學(xué)軟件上的分割結(jié)果進(jìn)行對比,最后利用專家手動(dòng)分割的結(jié)果(俗稱金標(biāo)準(zhǔn))進(jìn)行定量分析與比較。實(shí)驗(yàn)結(jié)果表明,本文提出的方法可以有效提高組織分割精度。
[Abstract]:Magnetic resonance imaging (MRI), which has the advantages of good soft tissue imaging, high spatial resolution, non-interventional and flexible scanning angle, has become an important auxiliary method for the diagnosis of brain diseases. Accurate segmentation of brain tissue is of great significance in the analysis and research of subsequent anatomical brain diseases such as Alzheimer's disease, multiple sclerosis, Parkinson's disease and schizophrenia. Because of the different physical properties of brain tissue, the Gao Si hybrid model (Gaussian Mixture model has become an ideal model for describing the slow change of gray scale. However, the traditional GMM is based on the assumption of pixel independence, and the spatial structure information of the organization is often ignored. At the same time, due to the complexity of the anatomical structure of brain tissue, the offset field, partial volume effect, noise and other physical factors in the imaging process, the segmental constant of Mr image is destroyed. In order to improve the accuracy of brain tissue segmentation, this paper focuses on the 3D segmentation algorithm of Gao Si mixed model based on prior information of tissue probability map and posteriori neighborhood information. The main contents are as follows: 1. A Gao Si hybrid model of brain tissue segmentation algorithm (PA-GMM) based on the prior information of the map is proposed. The experimental results show that the PA-GMM algorithm can solve the problem that the misdivision rate increases in the case of increased noise and offset field caused by the absence of spatial information in the traditional GMM, and can effectively improve the segmentation accuracy of brain tissue. 2. There exists an offset field in the brain tissue segmentation accuracy. When the offset field is too large, it will seriously affect the segmentation accuracy of the final algorithm. Therefore, a PA-GMM algorithm based on offset field correction is implemented on the basis of PA-GMM. Experimental results show that the proposed method can be used to segment Mr images quickly and effectively. By alternating iteration of segmentation and offset field correction, the effect of migration field correction is better than that of traditional preprocessing stage, and then the effect of tissue segmentation is better. In order to further improve the segmentation accuracy of the algorithm under high noise, Using the neighborhood information of posterior probability and the prior information of spatial anatomical structure of brain tissue probability map, a new SNPA-MGMM segmentation algorithm is proposed by redesigning the expression of mixed coefficients. The algorithm can not only suppress noise, but also segment complex overlapping regions such as GM and CSF. And can keep edge detail information. 4. This paper mainly uses BrainWeb's simulated data set and IBSR's two groups of real data sets (v1.0 and v2.0) as test data, and compares the improved algorithm with some new literature and medical software segmentation results. Finally, the expert manual segmentation results (commonly known as gold standard) for quantitative analysis and comparison. Experimental results show that the proposed method can effectively improve the accuracy of tissue segmentation.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R445.2;TP391.41
[Abstract]:Magnetic resonance imaging (MRI), which has the advantages of good soft tissue imaging, high spatial resolution, non-interventional and flexible scanning angle, has become an important auxiliary method for the diagnosis of brain diseases. Accurate segmentation of brain tissue is of great significance in the analysis and research of subsequent anatomical brain diseases such as Alzheimer's disease, multiple sclerosis, Parkinson's disease and schizophrenia. Because of the different physical properties of brain tissue, the Gao Si hybrid model (Gaussian Mixture model has become an ideal model for describing the slow change of gray scale. However, the traditional GMM is based on the assumption of pixel independence, and the spatial structure information of the organization is often ignored. At the same time, due to the complexity of the anatomical structure of brain tissue, the offset field, partial volume effect, noise and other physical factors in the imaging process, the segmental constant of Mr image is destroyed. In order to improve the accuracy of brain tissue segmentation, this paper focuses on the 3D segmentation algorithm of Gao Si mixed model based on prior information of tissue probability map and posteriori neighborhood information. The main contents are as follows: 1. A Gao Si hybrid model of brain tissue segmentation algorithm (PA-GMM) based on the prior information of the map is proposed. The experimental results show that the PA-GMM algorithm can solve the problem that the misdivision rate increases in the case of increased noise and offset field caused by the absence of spatial information in the traditional GMM, and can effectively improve the segmentation accuracy of brain tissue. 2. There exists an offset field in the brain tissue segmentation accuracy. When the offset field is too large, it will seriously affect the segmentation accuracy of the final algorithm. Therefore, a PA-GMM algorithm based on offset field correction is implemented on the basis of PA-GMM. Experimental results show that the proposed method can be used to segment Mr images quickly and effectively. By alternating iteration of segmentation and offset field correction, the effect of migration field correction is better than that of traditional preprocessing stage, and then the effect of tissue segmentation is better. In order to further improve the segmentation accuracy of the algorithm under high noise, Using the neighborhood information of posterior probability and the prior information of spatial anatomical structure of brain tissue probability map, a new SNPA-MGMM segmentation algorithm is proposed by redesigning the expression of mixed coefficients. The algorithm can not only suppress noise, but also segment complex overlapping regions such as GM and CSF. And can keep edge detail information. 4. This paper mainly uses BrainWeb's simulated data set and IBSR's two groups of real data sets (v1.0 and v2.0) as test data, and compares the improved algorithm with some new literature and medical software segmentation results. Finally, the expert manual segmentation results (commonly known as gold standard) for quantitative analysis and comparison. Experimental results show that the proposed method can effectively improve the accuracy of tissue segmentation.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R445.2;TP391.41
【參考文獻(xiàn)】
相關(guān)博士學(xué)位論文 前4條
1 楊紅U，
本文編號：2170251
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