本文關鍵詞：腦圖像多層次智能分類算法研究及其應用　出處：《中國科學院長春光學精密機械與物理研究所》2017年博士論文　論文類型：學位論文

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

【摘要】：隨著醫(yī)學影像技術的不斷成熟和發(fā)展,基于腦圖像的大腦結構和腦功能分析,已經成為近年來的研究熱點。采用傳統(tǒng)的手動勾畫方式進行特征提取和腦圖像分析,極大的增加了臨床醫(yī)生的負擔。通過機器學習的方法,自動提取腦圖像中的特征,用于腦疾病的診斷和預測,同時,找出與疾病相關的影像學標記,已成為研究趨勢。自動的特征提取方法與智能的分類算法,可以提高醫(yī)生對腦疾病診斷的效率,具有較高的應用價值。在實現對腦疾病輔助診斷的過程中,首要的任務是提取出能使分類器性能最優(yōu)的特征。研究腦圖像自動特征提取方法,特別是建立出能夠全面反映大腦結構信息的高維度、多層次特征,是分類算法研究中的難點與熱點問題。目前,腦圖像研究中使用的特征大多數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、綜合的表達腦疾病相關的大腦結構信息。本論文提出一種腦圖像多層次智能分類算法,用于腦疾病的輔助分析。本論文進行的主要研究工作和創(chuàng)新點如下:(1)通過對自動特征提取方法進行研究,提出一種基于腦圖像的多層次智能分類算法,有助于找出大腦結構的影像學標記。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)和高層次腦網絡特征(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兩類特征進行降維。針對每一種特征,分別使用徑向基核函數(Radial basis function,RBF)構造核矩陣,再通過適當的權重因子將兩類核矩陣集成為一個基于支持向量機(Support vector machine,SVM)的多核分類器。采用嵌套的交叉驗證方法進行算法的評估。多層次特征能夠更全面的表達大腦結構信息,分類算法具有較強的適用性,可以應用于不同腦疾病的分析和分類。(2)應用自動的圖像處理方法,進行了2型糖尿病腦結構影像學標記物的分析,找出了具有統(tǒng)計學意義的影像學標記,可以提升對2型糖尿病的早期辨識。2型糖尿病是一種常見的代謝性疾病,會對腦組織造成不可逆的損傷,引起認知障礙等并發(fā)癥。通過影像學方法對患者大腦結構進行檢測,有助于對2型糖尿病的診斷和治療。之前的影像學研究大多數使用的是單一的大腦體積或者皮層厚度等測量值,不能夠綜合的反映2型糖尿病大腦結構的改變。本論文通過使用性能較好的腦組織分割算法和腦皮層重建算法,可以準確計算出灰質體積、皮層厚度和皮層表面積等測量值。這三種測量值可以從不同層面反映出大腦的結構信息,如,灰質體積表示神經元的總體數量,皮層厚度表示縱向排列的神經元數量,皮層表面積表示橫向排列的神經元數量。同時研究這三種測量值對于全面分析2型糖尿病大腦結構改變有重要意義。(3)應用多層次智能分類算法進行了自尊程度與大腦結構的關聯(lián)分析。為了更好的理解自尊程度這一復雜的認知心理學過程,需要對大腦自我認知的神經機制進行充分的研究。目前基于自尊程度的神經影像學研究,大都采用的是基于全腦形態(tài)學分析方法或基于體素的腦體積分析方法,只能對從先驗信息中得到的特定腦區(qū)進行分析。雖然這些方法初步揭示了自尊程度與大腦結構之間的關系,但是不能很好的解釋神經網絡活動與自尊程度的關聯(lián)性。本論文采用多層次智能分類算法,具有較高的分類準確率(96.66%)、特異性(99.77%)和敏感性(95.67%),同時,找出了對自尊程度敏感的腦區(qū),不僅能夠彌補目前研究方法的不足,而且能夠同時提供大腦形態(tài)學信息和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,對臨床研究和科學研究有重要意義。(4)雖然目前國內外有一些常用的醫(yī)學圖像處理軟件平臺可以完成圖像的自動處理、進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提取出所需要的多層次特征。本論文中開發(fā)了brainlab全自動腦圖像處理與分析系統(tǒng),能夠對腦圖像進行自動的處理和多層次特征提取,輔助醫(yī)生進行腦疾病的早期診斷和分析,在臨床應用中具有重要意義。
[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.

【學位授予單位】：中國科學院長春光學精密機械與物理研究所
【學位級別】：博士
【學位授予年份】：2017
【分類號】：R741.044;TP391.41

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