發(fā)布時間：2018-07-07 14:36

  本文選題：功能磁共振成像 + 結構磁共振成像��； 參考：《北京理工大學》2015年博士論文

【摘要】：近年來,MRI圖像分析方法被越來越多地應用于大腦結構和功能研究以及神經(jīng)性疾病的計算機輔助診斷中。另一方面,隨著人工智能的發(fā)展,機器學習技術,尤其是其中的稀疏特征學習也被越來越多地引入MRI圖像分析中,在分類和預測建模方面扮演著重要角色。因此,研究新的MRI圖像分析方法,是更好地深度挖掘MRI圖像信息,進而促進腦科學研究及計算機輔助診斷技術發(fā)展的關鍵。然而,MRI圖像分析常常面臨小樣本、高特征維度的問題,由此導致的過擬合、噪聲特征和冗余特征嚴重降低了模型性能。稀疏特征學習方法能夠很好地解決上述難點,并已經(jīng)成功地應用于信號處理、模式識別和計算機視覺領域。本論文致力于研究新的適用于MRI圖像分析的稀疏特征學習方法,通過設計新的代價函數(shù)約束項來挑選擁有最佳分辨性能的圖像特征,從而提升模型分類和預測性能。本研究涉及的稀疏特征學習方法既包括稀疏單任務學習,如稀疏貝葉斯學習和基于1L范數(shù)的稀疏學習,也包括稀疏多任務學習,如組Lasso,Dirty model和稀疏組Lasso。通過對上述方法進行不同程度的創(chuàng)新,將其應用于認知神經(jīng)科學和神經(jīng)疾病診斷研究中,取得了良好的效果。本文的工作及創(chuàng)新之處主要包括以下5個部分:1.建立了一個基于多體素模式分析的學習模型,在初級視皮層上對空間視覺刺激進行解碼研究,解決了單體素分析方法忽視了體素之間的相關信息這一缺點。進一步,建立了一種多分類的稀疏貝葉斯學習模型,將特征選擇與視覺解碼結合起來。該模型能夠在選擇最相關特征的同時利用挑選出的特征進行視覺解碼,具有很好的整合性。實驗結果表明,該方法從2000個初級視皮層體素中挑選出9個最相關體素,使用挑選出的體素進行解碼,分類精度達到91.6%。同時,將挑選出的9個體素映射回原始腦空間,從另一個角度驗證了初級視皮層具有視網(wǎng)膜映射特性。該方法為基于功能MRI的視覺研究提供了一種新的途徑。2.首次提出一種基于結構MRI圖像和1L范數(shù)稀疏特征學習的煙霧病診斷方法,解決了傳統(tǒng)數(shù)字造影方法有損、技術復雜及代價高昂的缺點,使得將煙霧病診斷作為常規(guī)檢查成為可能。具體來說,該方法首先提取結構MRI圖像的皮層厚度特征,每幅圖像得到約2萬個特征。然后建立了三種基于1L范數(shù)的稀疏特征學習模型,包括Lasso,彈性網(wǎng)和L1-logistic回歸,通過特征選擇實現(xiàn)特征約簡,最后用挑選出的特征訓練支持向量機分類器。實驗結果表明,提出的診斷方法取得了較好的診斷精度(分類精度),其中基于彈性網(wǎng)特征學習的方法取得了最高的診斷精度,達到82.36%,對應ROC曲線下的面積0.833,顯著優(yōu)于未經(jīng)過特征選擇直接使用支持向量機對所有提取特征分類的結果(分類精度71.72%,對應ROC曲線下面積0.787)。3.利用兒童(6歲到15歲)的結構MRI圖像,建立了基于多核支持向量回歸的智商估計模型,并在建模過程中提出一種改進Dirty model多任務特征學習方法用于特征選擇,較好地實現(xiàn)了對兒童的智商估計。具體來說,首先提取兒童結構MRI圖像的灰質/白質特征,將對灰質/白質的特征選擇分別看做一個學習任務,利用提出的改進Dirty model選擇與智商相關的灰質/白質特征。分別計算挑選出的灰質/白質特征的核函數(shù),送入多核支持向量回歸模型中進行智商估計。實驗結果顯示,提出的方法估計的智商分數(shù)與兒童真實智商分數(shù)的相關性為0.718,與真實智商分數(shù)之間的均方根誤差為8.695。該智商估計模型的意義在于,為今后預測嬰幼兒智商,從而根據(jù)其預測智商定制合理的早期學習計劃提供了方法學基礎。4.提出一種基于分層模型和改進Dirty model的多被試解碼方法,較好地解決了傳統(tǒng)基于功能MRI解碼方法需要對每個被試單獨建模的缺點。傳統(tǒng)的解碼方法以體素作為特征,但不同被試之間對于相同實驗刺激的體素激活模式差異較大,難以對所有被試建立一個統(tǒng)一的模型。為解決這一問題,提出一種分層模型,以體素作為低層特征,學習出更加魯棒的高層特征,然后利用提出的改進Dirty model對高層特征進行選擇,并使用選擇出的高層特征,對所有被試建立一個統(tǒng)一的解碼模型。將提出的多被試解碼方法用于2D/3D視覺刺激的功能MRI數(shù)據(jù)進行驗證,分類精度達到89.4%,顯著高于直接使用體素作為特征的方法(73.4%)。該方法為研究不同被試間的神經(jīng)特性提供了方法學上的支持。5.提出一種基于結構MRI圖像和稀疏特征學習的自閉癥診斷方法,為解決傳統(tǒng)自閉癥診斷方法以行為學評分為標準,而實際中很難用一種行為學評分去診斷該疾病這一問題提供了一種新的方法學途徑,并在建模過程中提出一種全新的Canonical圖匹配稀疏組Lasso多任務特征學習算法用于特征選擇。具體地說,首先提取結構MRI圖像的灰質/白質特征,通過典型相關分析將原始灰質/白質特征映射到一個新的Canonical空間。將對類標簽以及SRS_TOTAL行為學評分的Canonical特征選擇分別看做一個學習任務,利用提出的方法選擇與自閉癥分類最相關的Canonical特征,然后用支持向量機進行分類。實驗結果顯示,提出的方法診斷精度為75.4%,ROC曲線下面積為0.804,顯著優(yōu)于基于原始灰質/白質特征的最新方法,也優(yōu)于現(xiàn)有的其它基于Canonical特征的方法。
[Abstract]:In recent years, MRI image analysis methods have been used more and more in the study of brain structure and function and computer aided diagnosis of neuropathic diseases. On the other hand, with the development of artificial intelligence, machine learning technology, especially the sparse feature learning among them, has also been introduced into MRI image analysis more and more, in classification and prediction Modeling plays an important role. Therefore, the study of the new MRI image analysis method is the key to the better depth mining of MRI image information, thus promoting the development of brain science research and computer aided diagnosis technology. However, MRI image analysis often faces small samples, high characteristic dimensions, resulting in overfitting, noise characteristics and redundancy. The redundant features seriously reduce the performance of the model. The sparse feature learning method can solve the above difficulties well, and has been successfully applied to the field of signal processing, pattern recognition and computer vision. This paper is devoted to the study of the new sparse feature learning method for MRI image analysis and the design of new cost function constraints. The sparse feature learning methods involved in this study include sparse single task learning, such as sparse Bayesian learning and sparse learning based on 1L norm, and sparse multitask learning, such as group Lasso, Dirty model, and sparse group Lasso. through pairs. This method has been applied to cognitive neuroscience and neural disease diagnosis research and has achieved good results. The work and innovation of this paper mainly include the following 5 parts: 1. a learning model based on the model analysis of the multibody element is established to decode the spatial visual stimuli on the primary visual cortex. In addition, a multi classification sparse Bayesian learning model is established, which combines feature selection with visual decoding. The model can be used to select the most relevant features and use the selected features for visual decoding, which is very good. The experimental results show that the method selects 9 most relevant voxels from 2000 primary visual cortex voxels, decodes the selected voxels, and the classification accuracy reaches 91.6%.. The selected 9 individual elements are mapped back to the original brain space and the retina mapping characteristics of the primary visual cortex are verified from another angle. The method provides a new way for visual research based on functional MRI..2. first proposes a method of diagnosis of moyamoya disease based on structural MRI image and 1L norm sparse feature learning. It solves the disadvantages of traditional digital contrast method, which is complicated and costly. It makes it possible to make the diagnosis of smoke fog as a routine examination. For example, this method first extracts the cortical thickness characteristics of the structure MRI image, and each image gets about 20 thousand features. Then three sparse feature learning models based on 1L norm are established, including Lasso, elastic network and L1-logistic regression. Feature reduction is implemented by feature selection. Finally, the selected feature is used to train the support vector machine classifier. The experimental results show that the proposed method has achieved good diagnostic accuracy (classification accuracy), and the method based on the feature learning of the elastic network has achieved the highest diagnostic accuracy, reached 82.36%, corresponding to the area of 0.833 under the ROC curve, and is significantly better than the node without the feature selection using the support vector machine to classify all the extracted features. (classification accuracy 71.72%, corresponding to the area under the ROC curve 0.787).3. using the structure MRI images of children (6 to 15 years old), an IQ estimation model based on multi kernel support vector regression is established. In the modeling process, an improved Dirty model multi task feature learning method is proposed for feature selection, and the IQ estimation of children is better realized. Specifically, the gray matter / white matter features of the children's structure MRI images are first extracted and the selection of gray matter / white matter features is considered as a learning task respectively. The gray matter / white matter characteristics associated with the IQ are selected by the proposed improved Dirty model. The kernel functions of the selected gray matter / white matter are calculated and sent to the multicore support vector back. The results show that the correlation between the estimated IQ score and the true IQ score of the proposed method is 0.718, and the root mean square error between the true IQ score and the true IQ score is 8.695., the IQ estimation model is for the future prediction of infant IQ, so that it is reasonable to customize the IQ according to its IQ prediction. The early learning plan provides a methodological basis for.4. to propose a multi decode method based on a hierarchical model and an improved Dirty model, which is a good solution to the shortcomings of the traditional functional MRI decoding method that needs to be modeled individually for each subject. The traditional decoding method is characterized by voxels, but different subjects have the same experimental stings between the different subjects. In order to solve this problem, a hierarchical model is proposed to learn more robust high level features, and then use the proposed improved Dirty model to select the high level features and use the selected high-level features. A unified decoding model is established for all subjects. The proposed multi test decoding method is used to verify the functional MRI data of 2D/3D visual stimuli. The classification accuracy is 89.4%, which is significantly higher than that of the direct use of voxel (73.4%). This method provides a methodological support for the study of the neural characteristics between different subjects. An autism diagnosis method based on structural MRI image and sparse feature learning is proposed to solve the traditional autism diagnosis method, which is based on behavioral score. In practice, it is difficult to use a kind of behavioral score to diagnose the disease. A new way is provided in the process of modeling, and a new Cano is proposed in the modeling process. Nical graph matching sparse group Lasso multi task feature learning algorithm is used for feature selection. Specifically, first extract gray / white texture features of structural MRI images, map the original gray / white matter features to a new Canonical space through canonical correlation analysis, and select the Canonical features of class labels and SRS_TOTAL behavioral scores. Do not look at a learning task, use the proposed method to select the most relevant Canonical features with the autism classification, and then use the support vector machine to classify them. The experimental results show that the proposed method has a diagnostic accuracy of 75.4% and the area under the ROC curve is 0.804, which is significantly better than the latest method based on the original gray matter / white matter characteristics, and is better than the existing one. Other methods based on Canonical features.
【學位授予單位】：北京理工大學
【學位級別】：博士
【學位授予年份】：2015
【分類號】：TP391.41

【共引文獻】

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