本文關(guān)鍵詞：腦圖像多層次智能分類(lèi)算法研究及其應(yīng)用　出處：《中國(guó)科學(xué)院長(zhǎng)春光學(xué)精密機(jī)械與物理研究所》2017年博士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 腦圖像 腦疾病 多層次特征 混合特征選擇 多核支持向量機(jī)

【摘要】：隨著醫(yī)學(xué)影像技術(shù)的不斷成熟和發(fā)展,基于腦圖像的大腦結(jié)構(gòu)和腦功能分析,已經(jīng)成為近年來(lái)的研究熱點(diǎn)。采用傳統(tǒng)的手動(dòng)勾畫(huà)方式進(jìn)行特征提取和腦圖像分析,極大的增加了臨床醫(yī)生的負(fù)擔(dān)。通過(guò)機(jī)器學(xué)習(xí)的方法,自動(dòng)提取腦圖像中的特征,用于腦疾病的診斷和預(yù)測(cè),同時(shí),找出與疾病相關(guān)的影像學(xué)標(biāo)記,已成為研究趨勢(shì)。自動(dòng)的特征提取方法與智能的分類(lèi)算法,可以提高醫(yī)生對(duì)腦疾病診斷的效率,具有較高的應(yīng)用價(jià)值。在實(shí)現(xiàn)對(duì)腦疾病輔助診斷的過(guò)程中,首要的任務(wù)是提取出能使分類(lèi)器性能最優(yōu)的特征。研究腦圖像自動(dòng)特征提取方法,特別是建立出能夠全面反映大腦結(jié)構(gòu)信息的高維度、多層次特征,是分類(lèi)算法研究中的難點(diǎn)與熱點(diǎn)問(wèn)題。目前,腦圖像研究中使用的特征大多數(shù)是基于體素的或是基于表面的,這些特征大多是單層次的,不能夠全面、綜合的表達(dá)腦疾病相關(guān)的大腦結(jié)構(gòu)信息。本論文提出一種腦圖像多層次智能分類(lèi)算法,用于腦疾病的輔助分析。本論文進(jìn)行的主要研究工作和創(chuàng)新點(diǎn)如下:(1)通過(guò)對(duì)自動(dòng)特征提取方法進(jìn)行研究,提出一種基于腦圖像的多層次智能分類(lèi)算法,有助于找出大腦結(jié)構(gòu)的影像學(xué)標(biāo)記。多層次特征包括低層次感興趣區(qū)域(Region of interest,ROI)特征(大腦體積和皮層厚度等)和高層次腦網(wǎng)絡(luò)特征(ROI之間的功能連接)。采用基于Filter和基于Wrapper的混合特征選擇方法分別對(duì)兩類(lèi)特征進(jìn)行降維。針對(duì)每一種特征,分別使用徑向基核函數(shù)(Radial basis function,RBF)構(gòu)造核矩陣,再通過(guò)適當(dāng)?shù)臋?quán)重因子將兩類(lèi)核矩陣集成為一個(gè)基于支持向量機(jī)(Support vector machine,SVM)的多核分類(lèi)器。采用嵌套的交叉驗(yàn)證方法進(jìn)行算法的評(píng)估。多層次特征能夠更全面的表達(dá)大腦結(jié)構(gòu)信息,分類(lèi)算法具有較強(qiáng)的適用性,可以應(yīng)用于不同腦疾病的分析和分類(lèi)。(2)應(yīng)用自動(dòng)的圖像處理方法,進(jìn)行了2型糖尿病腦結(jié)構(gòu)影像學(xué)標(biāo)記物的分析,找出了具有統(tǒng)計(jì)學(xué)意義的影像學(xué)標(biāo)記,可以提升對(duì)2型糖尿病的早期辨識(shí)。2型糖尿病是一種常見(jiàn)的代謝性疾病,會(huì)對(duì)腦組織造成不可逆的損傷,引起認(rèn)知障礙等并發(fā)癥。通過(guò)影像學(xué)方法對(duì)患者大腦結(jié)構(gòu)進(jìn)行檢測(cè),有助于對(duì)2型糖尿病的診斷和治療。之前的影像學(xué)研究大多數(shù)使用的是單一的大腦體積或者皮層厚度等測(cè)量值,不能夠綜合的反映2型糖尿病大腦結(jié)構(gòu)的改變。本論文通過(guò)使用性能較好的腦組織分割算法和腦皮層重建算法,可以準(zhǔn)確計(jì)算出灰質(zhì)體積、皮層厚度和皮層表面積等測(cè)量值。這三種測(cè)量值可以從不同層面反映出大腦的結(jié)構(gòu)信息,如,灰質(zhì)體積表示神經(jīng)元的總體數(shù)量,皮層厚度表示縱向排列的神經(jīng)元數(shù)量,皮層表面積表示橫向排列的神經(jīng)元數(shù)量。同時(shí)研究這三種測(cè)量值對(duì)于全面分析2型糖尿病大腦結(jié)構(gòu)改變有重要意義。(3)應(yīng)用多層次智能分類(lèi)算法進(jìn)行了自尊程度與大腦結(jié)構(gòu)的關(guān)聯(lián)分析。為了更好的理解自尊程度這一復(fù)雜的認(rèn)知心理學(xué)過(guò)程,需要對(duì)大腦自我認(rèn)知的神經(jīng)機(jī)制進(jìn)行充分的研究。目前基于自尊程度的神經(jīng)影像學(xué)研究,大都采用的是基于全腦形態(tài)學(xué)分析方法或基于體素的腦體積分析方法,只能對(duì)從先驗(yàn)信息中得到的特定腦區(qū)進(jìn)行分析。雖然這些方法初步揭示了自尊程度與大腦結(jié)構(gòu)之間的關(guān)系,但是不能很好的解釋神經(jīng)網(wǎng)絡(luò)活動(dòng)與自尊程度的關(guān)聯(lián)性。本論文采用多層次智能分類(lèi)算法,具有較高的分類(lèi)準(zhǔn)確率(96.66%)、特異性(99.77%)和敏感性(95.67%),同時(shí),找出了對(duì)自尊程度敏感的腦區(qū),不僅能夠彌補(bǔ)目前研究方法的不足,而且能夠同時(shí)提供大腦形態(tài)學(xué)信息和roi之間的功能連接信息,可以幫助研究人員更好的理解不同自尊程度的大腦模型,對(duì)臨床研究和科學(xué)研究有重要意義。(4)雖然目前國(guó)內(nèi)外有一些常用的醫(yī)學(xué)圖像處理軟件平臺(tái)可以完成圖像的自動(dòng)處理、進(jìn)行腦圖像的分析以及輔助腦疾病的診斷,但是還不能很好的從圖像中自動(dòng)提取出所需要的多層次特征。本論文中開(kāi)發(fā)了brainlab全自動(dòng)腦圖像處理與分析系統(tǒng),能夠?qū)δX圖像進(jìn)行自動(dòng)的處理和多層次特征提取,輔助醫(yī)生進(jìn)行腦疾病的早期診斷和分析,在臨床應(yīng)用中具有重要意義。
[Abstract]:With the development of medical imaging technology, analysis of brain structure and brain function of brain based on image, has become a research hotspot in recent years. The traditional way of manual delineation of feature extraction and analysis of brain image, greatly increased the burden of clinicians through a machine learning method. The automatic feature extraction of brain images for the prediction and diagnosis of brain diseases, and find out the disease associated imaging marker, has become a research trend. The classification algorithm and intelligent automatic feature extraction method, can improve the medical diagnosis of brain disease efficiency, and has high application value. In the process of implementation of the diagnosis of brain diseases first, the task is to extract the feature classifier optimal performance. Research on automatic feature extraction method of brain images, especially the establishment of a fully reflect the high dimensional brain structure information Of multi level feature, is a hot issue and difficulty in classification algorithm research. At present, the use of the characteristics of brain images are based on a voxel or is based on the surface, these features are mostly single level, can not be a comprehensive expression, brain disease related brain structures. This paper presents comprehensive information a brain image multilevel intelligent classification algorithm for the analysis of auxiliary brain diseases. The main research work and innovations are as follows: (1) based on the research of automatic feature extraction method, puts forward a multi-level intelligent classification algorithm based on brain image, helps to find out the structure of the brain imaging marker. Multi level features include the low level of region of interest (Region of, interest, ROI) features (brain volume and cortical thickness etc.) and the high level of brain network characteristics (ROI between functional connectivity). Based on Filter and based on Wrap A hybrid feature selection method of per were used to reduce the dimensionality of the two kinds of features. For each feature, respectively using RBF kernel function (Radial basis, function, RBF) to construct the kernel matrix, then the appropriate weight factor will be two types of nuclear matrix into one based on support vector machine (Support vector machine, SVM) the multi kernel classifier evaluation. Using cross validation method for nested algorithm. Multi level feature can brain structure information expression more comprehensive, classification algorithm has strong applicability, can be applied to the analysis and classification of different brain diseases. (2) application of the automatic image processing method, analyzed the structure of the brain type 2 diabetes imaging markers, were found statistically significant imaging markers for early identification, can improve the type.2 diabetes type 2 diabetes is a common metabolic disease, will not cause of brain tissue Reversible injury, complications of cognitive disorders. To detect the brain structure of the patients through the imaging method, is helpful to the diagnosis and treatment of type 2 diabetes. Previous imagingstudies most use single brain volume or cortical thickness measurements, cannot reflect the change of brain structure in type 2 diabetes mellitus. Through the use of better performance of brain tissue segmentation algorithm and cortical reconstruction algorithm can accurately calculate the gray matter volume, the thickness of cortex and cortex surface area measurements. The three kinds of measurements can reflect the structural information from different aspects such as the brain, said the total number of neurons in gray matter volume, cortical thickness the number of vertical arrangement of neurons, cortical surface area indicates the number of horizontal array of neurons. At the same time of the three kinds of measurements for a comprehensive analysis of type 2 diabetes brain. The change has important significance. (3) the application of multilevel intelligent classification algorithm are analyzed and correlated self-esteem and brain structure. In order to better understand the self-esteem of the cognitive psychology process, need the neural mechanism on the brain self cognition are fully studied. Based on the current neuroimaging study of self-esteem, mostly the full brain morphological analysis method based on the method of analysis of brain volume or voxel based analysis of specific regions of the brain can only be obtained from prior information. Although these methods reveal the relation between self-esteem and the degree of brain structure, but the correlation explain the activity of neural network and the level of self-esteem is not very good. This paper adopts multilevel intelligent classification algorithm has high classification accuracy (96.66%), specificity (99.77%) and sensitivity (95.67%), at the same time, to find out the level of self-esteem Sensitive areas of the brain, not only can make up for the current research methods, but also can provide information between brain morphology and the function of ROI connection information, the brain model understanding of different levels of self-esteem can help researchers better, has important significance for clinical research and scientific research. (4) although there are some medical images processing software platform can complete the automatic image processing commonly used, diagnosis of brain image analysis and auxiliary brain diseases, but also not very good from the image to extract multi-level features needed. This paper developed a BrainLAB automatic brain image processing and analysis system to brain image processing and multi-level automatic feature extraction, early diagnosis and analysis of doctor assisted brain diseases, has important significance in clinical application.

【學(xué)位授予單位】：中國(guó)科學(xué)院長(zhǎng)春光學(xué)精密機(jī)械與物理研究所
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：R741.044;TP391.41

【相似文獻(xiàn)】

相關(guān)期刊論文 前4條

1 韓莉，

本文編號(hào)：1399394