結(jié)構(gòu)磁共振影像特征信息提取方法研究
發(fā)布時(shí)間：2018-11-23 10:12
【摘要】：阿爾茲海默癥(Alzheimer's Disease,AD)是一種最常見(jiàn)的癡呆癥,發(fā)病者大多為老年人。AD是一種神經(jīng)變性紊亂疾病,關(guān)于其發(fā)病機(jī)制目前仍未有非常明確的說(shuō)法。AD病情的發(fā)展是一個(gè)漸進(jìn)、平緩的過(guò)程,患者的記憶、注意力、語(yǔ)言等認(rèn)知能力將逐漸減退或者受損,最終患者會(huì)出現(xiàn)昏迷狀況,通常死于感染等并發(fā)癥。隨著現(xiàn)在國(guó)家人口老齡化日益嚴(yán)重,AD的發(fā)病率會(huì)越來(lái)越高,由其引發(fā)的開(kāi)銷和損失也是不可估量的。對(duì)于AD患者以及他們的家人來(lái)說(shuō),除了巨大的經(jīng)濟(jì)負(fù)擔(dān)以外,更難以承受的則是精神和情感上的巨大壓力與折磨。 臨床和神經(jīng)病理學(xué)研究已經(jīng)極大地推進(jìn)了人們對(duì)AD病理生理和疾病發(fā)展的認(rèn)識(shí),但是目前還沒(méi)有任何診斷方法可以對(duì)活體個(gè)體進(jìn)行AD的確診,只有在個(gè)體死亡后對(duì)其進(jìn)行尸檢才能確診。所以,尋找一種無(wú)創(chuàng)傷性的AD臨床診斷方法具有重要的理論和實(shí)際意義。 隨著醫(yī)學(xué)影像技術(shù)的發(fā)展,基于醫(yī)學(xué)影像的臨床評(píng)估方法已經(jīng)逐漸成為AD臨床診斷中一個(gè)重要組成部分,其中又以結(jié)構(gòu)磁共振成像(structural magnetic resonance imaging, sMRI)的使用最為廣泛。sMRI圖像能夠客觀記錄下從疾病潛伏期到發(fā)作期整個(gè)過(guò)程中AD患者腦結(jié)構(gòu)生物標(biāo)記的變化,這些數(shù)據(jù)能夠從根本上改變?nèi)藗儗?duì)這種疾病的認(rèn)識(shí),并且能夠影響和引導(dǎo)疾病的后續(xù)診斷和治療。 傳統(tǒng)的sMRI圖像處理方法工作量大、耗時(shí)長(zhǎng)、過(guò)程復(fù)雜,對(duì)使用者的先驗(yàn)知識(shí)要求較高,現(xiàn)在需要一種全自動(dòng)的sMRI數(shù)據(jù)分析方法來(lái)識(shí)別和檢測(cè)受試者腦結(jié)構(gòu)中潛在的AD生物標(biāo)記。盲源分離、子空間學(xué)習(xí)和機(jī)器學(xué)習(xí)等信號(hào)處理方法的進(jìn)展為全自動(dòng)的sMRI數(shù)據(jù)分析方法提供了可能的技術(shù)手段,利用這些信號(hào)處理方法可以從受試者的sMRI數(shù)據(jù)中提取出關(guān)鍵信息并對(duì)其是否患有AD進(jìn)行診斷。 獨(dú)立成分分析(independent component analysis,ICA)作為一種盲源分離方法,近幾年有不少學(xué)者將其用于AD的sMRI數(shù)據(jù)分析。在這些研究中,研究者們假設(shè)各受試者的sMRI影像之間是相互獨(dú)立的,然后用ICA對(duì)向量化后的sMRI影像做特征提取,提取出的特征用于后續(xù)對(duì)受試者的分類診斷。但這種模型在用ICA做特征提取時(shí)需要用到一組受試者的sMRI影像,當(dāng)有新增單個(gè)受試者時(shí)無(wú)法立刻對(duì)其sMRI數(shù)據(jù)進(jìn)行特征提取進(jìn),進(jìn)而導(dǎo)致無(wú)法診斷。這一缺點(diǎn)使得這種診斷方法不符合臨床診斷的需求,即在臨床上希望每有一個(gè)新增受試者都可以即刻對(duì)其進(jìn)行診斷。針對(duì)這一問(wèn)題本文提出一種新的ICA特征提取模型,基于該特征提取模型的診斷方法可以對(duì)新增單個(gè)受試者進(jìn)行診斷。該模型假設(shè)每個(gè)sMRI影像的各體素之間是相互獨(dú)立的,然后先利用一組sMRI訓(xùn)練數(shù)據(jù)訓(xùn)練出解混矩陣,這樣當(dāng)有新增sMRI數(shù)據(jù)時(shí)可以利用訓(xùn)練好的解混矩陣即刻對(duì)其進(jìn)行特征提取,進(jìn)而進(jìn)行后續(xù)的診斷,滿足臨床診斷的需求。仿真實(shí)驗(yàn)證明,本文提出的新的基于ICA的診斷方法可以達(dá)到與原ICA診斷方法相當(dāng)?shù)脑\斷準(zhǔn)確率,且更符合實(shí)際診斷時(shí)的需求。 包括ICA在內(nèi)的很多線性特征提取方法在AD的sMRI數(shù)據(jù)分析上表現(xiàn)出了良好的性能,但是這些線性特征提取方法都需要將原始的三維sMRI影像向量化之后才能對(duì)數(shù)據(jù)進(jìn)行分析。這樣處理帶來(lái)的后果是原始三維圖像數(shù)據(jù)中的空間信息會(huì)遭到破壞,造成了大量有效信息的丟失；同時(shí),sMRI影像的數(shù)據(jù)量非常大,因此向量化之后得到的向量維數(shù)非常高,而受試者的數(shù)量是有限的,這樣就可能會(huì)導(dǎo)致小樣本問(wèn)題(under sample problem)。針對(duì)這些問(wèn)題,本文主要提出了一種基于非相關(guān)多線性主成分分析(uncorrelated multilinear principal component analysis, UMPCA)和拉普拉斯分值(laplacian score, LS)的新分類診斷方法。UMPCA是一種多線性子空間學(xué)習(xí)方法,用其對(duì)sMRI數(shù)據(jù)進(jìn)行特征提取可以用直接張量模型來(lái)表示三維影像數(shù)據(jù)并對(duì)其進(jìn)行處理,而不需要將原始的三維sMRI數(shù)據(jù)向量化,保留了原始數(shù)據(jù)的空間結(jié)構(gòu)信息,避免了前面所提到的向量化帶來(lái)的問(wèn)題；另外,特征提取后的信息仍有可能有一定的冗余度,在提取sMRI數(shù)據(jù)特征信息后加入了LS特征選擇的過(guò)程,可以進(jìn)一步減少冗余信息,降低計(jì)算復(fù)雜度,選取出區(qū)別度高的特征,有效地提高了之后診斷過(guò)程的準(zhǔn)確率。仿真實(shí)驗(yàn)表明,同現(xiàn)有的診斷方法相比,本文提出的UMPCA-LS分類診斷方法準(zhǔn)確率更高。
[Abstract]:Alzheimer's Disease (AD) is one of the most common forms of dementia. AD is a neurodegenerative disorder, and its pathogenesis is still not well-defined. The development of AD is a gradual and gradual process, and the cognitive ability of the patient's memory, attention, language and so on will be gradually reduced or damaged, and the final patient will be in a coma, usually with complications such as infection. As the aging population of the country is becoming more and more serious, the incidence of AD is getting higher and higher, and the expenses and losses caused by it are also inestimable. For AD patients and their families, in addition to the huge economic burden, the more difficult to bear is the great pressure and torment of the spirit and the emotion. The clinical and neuropathological study has greatly advanced people's understanding of the pathophysiology of AD and the development of the disease, but there is no diagnostic method for the diagnosis of AD in living individuals, only after the individual has died Therefore, it is important to find a non-traumatic AD clinical diagnosis method. With the development of the medical image technology, the clinical evaluation method based on the medical image has gradually become an important part in the clinical diagnosis of AD, in which the structure magnetic resonance imaging (sMRI) is used. The most widely used. sMRI images can objectively record the changes in the biological markers of the brain structure of AD patients from the latent period of the disease to the whole process of the attack period, which can fundamentally change people's understanding of the disease, and can influence and guide the follow-up of the disease. the traditional sMRI image processing method has the advantages of large workload, long time consumption, complex process, high requirements on the prior knowledge of the user, and a fully-automatic sMRI data analysis method is needed to identify and detect the subpotential in the brain structure of the subject. the progress of signal processing methods such as blind source separation, subspace learning, and machine learning provides a possible technical means to extract key information from the subject's sMRI data and determine whether it An independent component analysis (ICA) is used as a blind source separation method. In recent years, a number of scholars have been used to The sMRI data analysis of AD. In these studies, the researchers assumed that the sMRI images of each subject were independent of each other and then extracted the quantized sMRI image with ICA and the extracted features were used for Follow-up to the subject's classification diagnosis. However, this model requires a set of subject's sMRI images when using ICA for feature extraction, and it is not possible to immediately characterize the sMRI data when there is a new single subject The disadvantage is that this diagnostic method does not meet the need for clinical diagnosis, i.e., clinically, there is a need for every additional subject This paper presents a new ICA feature extraction model based on the feature extraction model. Add a single subject to make a diagnosis. The model assumes that each of the voxels of each sMRI image is independent of each other, and then uses a set of sMRI training data to train the demixing matrix so that when there is new sMRI data, it can be characterized by a well-trained demixing matrix Take, and follow up The simulation results show that the new ICA-based diagnostic method can achieve the same diagnostic accuracy as the original ICA diagnostic method, and More realistic diagnosis needs. Many linear feature extraction methods, including ICA, show good performance in the sMRI data analysis of AD, but these linear feature extraction methods require the original three-dimensional sMRI image After quantization, the data can be analyzed. The consequence of this process is that the spatial information in the original three-dimensional image data is destroyed, resulting in a loss of a large amount of effective information; at the same time, the amount of data of the sMRI image is very large, so that the number of vector dimensions obtained after quantization is not Often high, and the number of subjects is limited, which may result in a small sample problem (under In view of these problems, a non-correlated multi-linear principal component analysis (UMPCA) and a Laplacian value (laplacian sco) are proposed in this paper. the new classification and diagnosis method of re, ls) is a multi-linear subspace learning method, which can be used for extracting the sMRI data, and the three-dimensional image data can be represented by a direct tensor model and processed without the need to vectorize the original three-dimensional sMRI data, the space structure information of the original data is reserved, the problem caused by the backward quantization mentioned above is avoided; in addition, the extracted information can still have certain redundancy, and the process of selecting the LS feature selection after extracting the sMRI data characteristic information can further the redundant information is reduced, the computational complexity is reduced, the characteristics of high difference are selected, The accuracy of the post-diagnosis process is improved. The simulation results show that the UMPCA presented in this paper is compared with the existing diagnostic method.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP391.41;R445.2
本文編號(hào)：2351216
[Abstract]:Alzheimer's Disease (AD) is one of the most common forms of dementia. AD is a neurodegenerative disorder, and its pathogenesis is still not well-defined. The development of AD is a gradual and gradual process, and the cognitive ability of the patient's memory, attention, language and so on will be gradually reduced or damaged, and the final patient will be in a coma, usually with complications such as infection. As the aging population of the country is becoming more and more serious, the incidence of AD is getting higher and higher, and the expenses and losses caused by it are also inestimable. For AD patients and their families, in addition to the huge economic burden, the more difficult to bear is the great pressure and torment of the spirit and the emotion. The clinical and neuropathological study has greatly advanced people's understanding of the pathophysiology of AD and the development of the disease, but there is no diagnostic method for the diagnosis of AD in living individuals, only after the individual has died Therefore, it is important to find a non-traumatic AD clinical diagnosis method. With the development of the medical image technology, the clinical evaluation method based on the medical image has gradually become an important part in the clinical diagnosis of AD, in which the structure magnetic resonance imaging (sMRI) is used. The most widely used. sMRI images can objectively record the changes in the biological markers of the brain structure of AD patients from the latent period of the disease to the whole process of the attack period, which can fundamentally change people's understanding of the disease, and can influence and guide the follow-up of the disease. the traditional sMRI image processing method has the advantages of large workload, long time consumption, complex process, high requirements on the prior knowledge of the user, and a fully-automatic sMRI data analysis method is needed to identify and detect the subpotential in the brain structure of the subject. the progress of signal processing methods such as blind source separation, subspace learning, and machine learning provides a possible technical means to extract key information from the subject's sMRI data and determine whether it An independent component analysis (ICA) is used as a blind source separation method. In recent years, a number of scholars have been used to The sMRI data analysis of AD. In these studies, the researchers assumed that the sMRI images of each subject were independent of each other and then extracted the quantized sMRI image with ICA and the extracted features were used for Follow-up to the subject's classification diagnosis. However, this model requires a set of subject's sMRI images when using ICA for feature extraction, and it is not possible to immediately characterize the sMRI data when there is a new single subject The disadvantage is that this diagnostic method does not meet the need for clinical diagnosis, i.e., clinically, there is a need for every additional subject This paper presents a new ICA feature extraction model based on the feature extraction model. Add a single subject to make a diagnosis. The model assumes that each of the voxels of each sMRI image is independent of each other, and then uses a set of sMRI training data to train the demixing matrix so that when there is new sMRI data, it can be characterized by a well-trained demixing matrix Take, and follow up The simulation results show that the new ICA-based diagnostic method can achieve the same diagnostic accuracy as the original ICA diagnostic method, and More realistic diagnosis needs. Many linear feature extraction methods, including ICA, show good performance in the sMRI data analysis of AD, but these linear feature extraction methods require the original three-dimensional sMRI image After quantization, the data can be analyzed. The consequence of this process is that the spatial information in the original three-dimensional image data is destroyed, resulting in a loss of a large amount of effective information; at the same time, the amount of data of the sMRI image is very large, so that the number of vector dimensions obtained after quantization is not Often high, and the number of subjects is limited, which may result in a small sample problem (under In view of these problems, a non-correlated multi-linear principal component analysis (UMPCA) and a Laplacian value (laplacian sco) are proposed in this paper. the new classification and diagnosis method of re, ls) is a multi-linear subspace learning method, which can be used for extracting the sMRI data, and the three-dimensional image data can be represented by a direct tensor model and processed without the need to vectorize the original three-dimensional sMRI data, the space structure information of the original data is reserved, the problem caused by the backward quantization mentioned above is avoided; in addition, the extracted information can still have certain redundancy, and the process of selecting the LS feature selection after extracting the sMRI data characteristic information can further the redundant information is reduced, the computational complexity is reduced, the characteristics of high difference are selected, The accuracy of the post-diagnosis process is improved. The simulation results show that the UMPCA presented in this paper is compared with the existing diagnostic method.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP391.41;R445.2
【參考文獻(xiàn)】
相關(guān)期刊論文 前2條
1 趙小杰;龍志穎;郭小娟;姚力;;阿爾茨海默氏癥研究中的磁共振成像數(shù)據(jù)分析[J];軟件學(xué)報(bào);2009年05期
2 呂彬;何暉光;趙明昌;呂科;張志強(qiáng);盧光明;;基于磁共振圖像的腦皮層厚度測(cè)量方法[J];中國(guó)醫(yī)學(xué)影像技術(shù);2008年06期
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