【作者】 陆明；

【导师】 赵海峰；

【作者基本信息】 安徽大学 ， 计算机应用技术， 2015， 硕士

【摘要】 颅脑创伤(Traumatic Brain Injury,简称TBI)计算机辅助诊断系统能够提高疾病的诊断精确度和效率。然而,利用计算机来辅助诊断颅脑创伤也面临诸多挑战,比如脑组织结构复杂、各组织在核磁共振图像中的灰度对比度较低等难题。颅脑创伤计算机辅助诊断系统主要涉及图像分类和目标检测及提取,而图像分类与目标检测及提取是当前模式识别领域重要的研究方向,在人机交互、计算智能等领域有着十分重要的应用。人们依据人脑在处理信号时所表现出的稀疏性,提出了稀疏表示理论,这是一种新的信号表示方法,目前已经广泛应用于信号处理、计算机视觉分析等领域。当使用稀疏表示模型来表示信号时,可将其分解为超完备字典的线性组合,其中的系数向量具有稀疏性,稀疏表示模型对信号的噪声更加稳健。论文重点调研了稀疏表示理论、基于稀疏表示的分类器的设计等基础理论知识,还重点调研了基于稀疏表示模型的颅脑MR图像分类和目标检测及提取的应用设计,在阅读了相关资料后,论文在现有研究成果的基础上展开了更进一步的深入研究,论文主要研究工作可以概括如下：(1)首先概述了颅脑创伤基础知识以及核磁共振脑组织图像的特点,其次详细介绍了研究颅脑创伤图像的重要意义以及现阶段颅脑创伤图像处理所面临的困难,接着概述了图像分类和目标检测的国内外研究现状,然后,详细分析了稀疏表示的数学模型以及稀疏表示模型的求解算法,这里论文重点介绍了凸松弛优化算法和贪婪算法中的匹配追踪算法,最后介绍了稀疏表示模型在分类器中的应用以及在图像分类中的应用。(2)研究利用颅脑MR图像的分类来判断目标对象是否有颅脑创伤。适用于高维模式分类的方法已经广泛用于分析颅脑图像的结构和功能以辅助诊断颅脑创伤,例如支持向量机。大多数现存方法是从脑组织数据提取特征,然后建立单一分类器来进行分类。但是,由于噪声和脑图像少等原因,很难用单一分类器来取得很高的分类性能。论文中,研究了使用MR图像的纹理特征来区分目标对象有没有颅脑创伤。我们提出了基于局部随机碎片子空间集成的方法,并且以纹理参数为特征来分类。首先,将每个颅脑图片划分为许多小的局部碎片,之后再从碎片池中随机选取一个碎片子集来建立弱分类器,此处使用基于稀疏表示的分类器方法来建立每个弱分类器。然后集成所有的弱分类器以判断切片所属类别。在对每个研究对象的所有MR切片进行分类之后,再根据切片阂值来判断此对象是否有脑创伤。最终的分类结果表明以MRI纹理为特征、基于局部随机碎片子空间集成的分类方法能够有效判断对象有没有脑创伤。(3)研究利用RPCA算法来有效检测及提取脑创伤部位,并且通过相关实验来进行验证。在提取脑创伤部位时,目前仍然以传统的分割方法为主。但是,每种分割方法都有其局限性,而且颅脑MR图像具有特定性质,故目前还没有一个有效的分割方法能够很好地提取脑创伤部位。为了能够自动、快速、准确地检测和提取脑创伤部位,论文提出了一个基于稳健主成分分析的脑创伤部位检测与提取算法。该算法在主成分分析算法的基础上,引入稀疏表示模型。最终的实验结果表明,该算法比传统分割算法鲁棒性更好、准确度更高,并且是全自动的、不受旋转平移等因素的影响。更多还原

【Abstract】 Visualization of traumatic brain injury (TBI) can improve the accuracy and efficiency of the diagnosis of many brain diseases. However, computer aided diagnosis of brain injury is also facing many challenges, such as the complex structure of the brain and the low gray contrast of the tissues in the MR images, etc.The computer-aided diagnosis system of TBI is mainly related to image classification and object detection and extraction. Image classification and object detection and extraction is currently an important research direction in the field of pattern recognition, and has important applications in human-computer interaction, computational intelligence and other fields. A theory of sparse representation was proposed according to the sparse characteristic of brain images, and this becomes a new signal representation and has been widely used in signal processing, computer vision analysis and other fields. When a signal is represented in sparse representation, it is decomposed into a linear combination of over-complete dictionary, in which the coefficient vector is sparse. Sparse representation is more robust against signal noise. This thesis focuses on sparse representation, and also focuses on classification and object detection of MR images. Based on related references and existing research, further in-depth study was carried out. In this thesis, the work can be summarized as follows:First of all, I summarize the basics of traumatic brain injury and the characteristics of magnetic resonance images, followed by detailing the importance of the research on TBI at this stage and the difficulties in processing images with traumatic brain injury. Then I introduce the domestic and international research status of the image classification and object detection, and summarize a detailed analysis of the mathematical model based on sparse representation. This thesis describes the convex optimization algorithm and greedy algorithm, and then introduces the classification of MR images based on sparse representation. Secondly, this thesis studies the classification methods of MR images to determine whether an object is with TBI. The classification methods of high-dimensional pattern, e.g., support vector machines (SVM), have been widely investigated for analysis of structure and function of brain images (such as MRI) to assist the diagnosis of TBI. Most existing classification methods extract features from MR data and then construct a single classifier to perform classification. However, due to noise and small sample size of MR data, it is a challenge to train a single global classifier that can be robust enough to achieve a good classification performance. In this thesis, we investigate the feasibility of using MRI-based textures to classify subjects with and without TBI. We propose a local patch-based subspace ensemble method which builds multiple individual classifiers based on different subsets of local patches and then combines them for more accurate and robust classification. We divide each brain image into a number of local patches and select a subset of patches randomly from the patch pool to build a weak classifier. Here, the sparse representation-based classifier (SRC) method is used to construct each weak classifier. Then, we combine multiple weak classifiers to make the final decision. The results show that the local patch-based subspace ensemble classification using MRI textures can effectively classify subjects/slices with and without TBI.Thirdly, this thesis studies how to use RPCA algorithm to segment the injured area. The existing extraction methods of the TBI area, still based on the traditional segmentation methods. However, there is not yet an effective segmentation method that can achieve a good extraction performance of injured area because each method has its own limitation and MR images have specific properties. In order to achieve automatic, rapid and accurate detection and extraction of injured area, this thesis presents a robust principal component analysis algorithm (RPCA) based on sparse representation. The final results show that this algorithm is more robust and faster than the traditional algorithms.更多还原