【作者】 刘复昌；

【导师】 夏德深；

【作者基本信息】 南京理工大学 ， 模式识别与智能系统， 2009， 博士

【摘要】 核磁共振成像(MRI)技术已经成为心脏疾病临床诊断的重要辅助手段。该技术能够无侵入地检测人体的组织和器官,而且其成像机理使得该方法对生物体内如心脏这样的软组织特别有效。运用心脏核磁共振图像不仅能观察到心脏的形态结构,还可以估测心室的整体功能及局部心肌功能。特别是左心室心肌的运动情况能够反映心脏的泵血功能,为多种心脏疾病的诊断提供依据,因此成为当前研究的热点。为了获取对左心室功能评价的参数,需要对左心室进行三维表面重建及运动重建。核磁共振图像左心室内外轮廓分割是左心室三维表面重建及运动重建的基础。分割左心室的内外轮廓,能够对运动分析提供边界信息,同时为重建得到的三维运动场以及各种分析结果提供载体。因此,左心室内外轮廓的分割对于后续左心室功能分析起着重要作用,影响后续三维表面及运动重建的精度。核磁共振成像技术分为不加标记线的核磁共振成像(Untagged MRI)和带标记线核磁共振成像(Tagged MRI)。不加标记线的核磁共振成像,能够反映更为精细的生理结构,但由于心肌的同质性,难以找到密集的对应特征点,因而难以准确反映心肌运动。带标记线核磁共振成像通过射频脉冲以非侵入性的方式将磁饱和模式加到待成像的机体上,在核磁共振图像上表现为黑色条纹,即为标记线。由于标记线会随机体一起运动,因此标记线的运动反映了机体的位移,该技术为精确研究心肌内部的复杂形变提供了有效的途径。本文对带标记线和不带标记线两种MR图像左心室分割进行了深入研究,主要有以下几方面的工作:1)分析了不带标记线MR图像左心室分割的难点,提出一种自动分割左心室内外轮廓的算法:先利用MR图像上左心室心肌的内外轮廓类似圆形的先验形状知识,用Hough变换估计左心室的初始轮廓,使初始轮廓较准确地定位于真实轮廓边缘附近,从而克服初始轮廓对测地线轮廓模型分割结果的影响;然后再在测地线轮廓模型基础上结合K-均值聚类提供的区域信息及心肌的生理结构约束,对左心室的内外轮廓同时进行分割。心肌的生理结构约束可以用来控制左心室内外轮廓的相对位置,区域信息用来给图像分割提供目标类别信息。实验表明该分割算法具有较好的分割结果。2)研究了基于图论的主动轮廓模型方法,并在此基础上提出了一种基于图论的主动轮廓模型和形状统计相结合的不带标记线MR图像左心室交互分割方法。考虑到基于图论的主动轮廓模型方法分割时易发生边缘泄漏,采用了形状统计来约束基于图论的主动轮廓模型分割心脏MR图像。在演化曲线过程中,采用了点分布模型来描述形状统计,将曲线投影到形状允许空间以施加形状约束。由于加入了先验形状约束,使得受遮挡的目标也能够很好的分割,目标形状的平移、旋转和缩放对分割结果没有影响,而且对噪声也不敏感。该方法捕捉范围大、对初始轮廓不敏感,很好地克服了虚假强边界的干扰及边缘泄漏且需要手工设置参数仅有一个,此外该方法速度较快。而且该方法提供了对分割结果的交互式修改,大大地方便了人机交互。3)从图像分类的思路出发,提出了一种结合特征分类的主动形状模型分割方法,以此来自动分割不带标记线MR图像左心室内外轮廓。即在偏最小二乘(Partial LeastSquare,PLS)框架下结合邻域空间上下文类别信息,提出了基于上下文的PLS(ContextualPLS,CPLS)特征维数削减方法,并且考虑邻域加权来改进基于上下文的典型相关投影(Canonical Contextual Correlation Projection,CCCP),提出了一种新的类标号编码方式,高斯加权CCCP(Gaussian Weighed CCCP,GCCCP)和高斯加权PLS(Gaussian WeightedPLS,GPLS)特征维数削减方法,用这些方法抽取最优特征,训练K近邻分类器代替主动形状模型的轮廓灰度匹配法来确定边缘点进行轮廓分割。新的特征维数削减方法可以更好地提高分类性能和降低计算时间。实验结果证明了此分割方法可以达到较以往方法更高的分割精度和更好的稳定性。4)研究并提出了一种基于主动形状模型及特征融合策略的带标记线MR图像左心室自动分割方法。即从基于典型相关分析的特征融合角度对LM滤波器组提取的带标记线MR图像左心室纹理特征进行特征融合,再用融合后的特征构造支持向量机(SVM)分类器,通过分类器来确定边缘点,驱动主动形状模型分割左心室。特征融合具有明显的优势,即保留了参与融合的多特征的有效鉴别信息,又在一定程度上消除了由于各种因素带来的冗余信息,对分类识别具有重要意义。实验证明了通过基于典型相关分析的特征融合可以降低分类错误率,提高分类性能。而用分类器代替经典ASM模型的基于轮廓灰度的匹配法来确定边缘点可以较好地分割纹理图像。5)研究了结合形状统计的变分图像分割方法,并在此理论框架下提出了一种结合纹理分类与形状统计的带标记线MR图像左心室内外轮廓自动分割方法。该算法将SVM分类器引入到变分框架下,通过利用SVM对S滤波器组提取的纹理特征所得的分类结果构造新的曲线内外区域能量表示,改进了经典的Mumford-Shah模型中的内外区域能量项,把Mumford-Shah模型推广到纹理图像的分割。对于乳突肌干扰及边缘断裂现象,采用先验形状统计来引导曲线的演化,使分割结果鲁棒性增强,为临床应用提供了一种可行的分割方法。实验结果证明了此方法的有效性。更多还原

【Abstract】 Recently, Magnetic resonance imaging (MRI) has become an important assistant measure in the clinical diagnosis of heart diseases. It has been widely used, because it is no invasive and harmful and especially efficient to parenchyma. Through the cardiac MR images, the physicians not only can observe the structure of the heart, but also can estimate the global function and local myocardium function of the ventricles. It can be used to diagnose many heart diseases. Especially, Left ventricle (LV) is the pump of the blood circulation of the whole body. It plays an important role in cardiac function. So LV is the focus in the current research. The parameters of global function and local myocardium function of LV are significant in the clinical diagnosis. In order to estimate these parameters, we need to reconstruct the left ventricle surface and motion. Segmentation of left ventricle MR images is the basis of LV surface and motion reconstruction. Therefore, it is very important to segement LV precisely.MRI is divided into tow category: tagged MRI and untagged MRI. Untagged MRI can provide the high quality images of myocardium, but can not give the myocardium motion information. By applying a special radio-frequency pulse to alter the magnetic property of selective tissues, Tagged MRI creates dark tags on generated images. Since tag stripes are magnetically embedded, they move with the underlying tissue during heart deformation, thus providing myocardium motion information. Tagged MRI has provided a powerful tool to study the lefte ventricle motion. We analyse the characteristic of tagged MR and untagged MR images and propose some novel models and algorithms. Our work mainly includes the following parts:(1) It is proposed that a new method to segment the epicardium and endocardium in left ventricle MR images automatically. Based on the prior shape of epicardium and endocardium which are like circles, it uses Hough transform to detect circles for initial contours of LV. This method modifies the geodesic active contour model by integrating K-means clustering information and anatomical constraints. K-means can provide regional information and anatomical constraints can provide shape constraints for segmentation. Taking the initial contour detected by Hough transform, it can segment both the epicardium and endocardium automatically and accurately. Experimental results demonstrate the effectiveness of this method.(2) It is proposed that a method based on graph cuts active contours and shape statistics to segment left ventricle MR images interactively. This method uses Graph cuts based active contours to convert the image segmentation into the globally optimal partition after we transform the image into a graph. Then, we introduce shape statistics into Graph cuts based active contours. The introduction of shape statistics can prevent the deformation curve from leaking out of actual boundaries. This method has a large capture range and is insensitive to initial contour and occlusion. Only one parameter need to tune manually. Its speed is fast and results are not infected by transformation and rotation and scale. It also provides an interactive modification of segmentation results.(3) It is proposed that an improved ASM method integrateing feature classification for the automatic segmentation of left ventricle. Image classification based on features can provide regional information. The classifaction accuray can be improved by dimensionality reduction. We improve PLS taking spatial label context into account and also use a new way of encoding the class label of CCP and PLS using Gaussian weighting function. We call them CPLS, GCCP and GPLS respectly. These new feature dimensionality reduction methods have better performance. We use these methods to reduce the gray level features of image and to low the classification error as well as consuming time. And KNN classifier was trained with dimensionality reduced features. Instead of sampling the normalized derivative profiles scheme of original ASM, we improve it with a new way that the feature at each position along the profile perpendicular to the object contour is fed into a trained classifier to determine the edge point. The experiments show that the new method can get accurate result robustly.(4) It is proposed that an improved ASM method for automatic segmentation of left ventricle tagged MR images. Based on the idea of feature fusion, we used canonical correlation analysis (CCA) to combine the features extracted from tagged MR images by LM filter bank. Then, a classifier was constructed to determine edge point using SVM. CCA can decrease the classification error and improve the classification performance. Instead of sampling the normalized derivative profiles, the feature at each position along the profile perpendicular to the object contour is fed into a trained classifier to determine the edge point. Experimental results show that our method can achieve a highly accurate and robust performance.(5) It is proposed that an improved texture classification and shape statistics variational approach for the automatic segmentation of the epicardium and endocardium of left ventricle. We introduce texture classification information and shape statistical knowledge into the Mumford-Shah model, and then use the output of support vector machine (SVM) classifier relying on S filter banks to construct a new region-based image energy term. The introduction of shape statistics can improve the segmentation with broken boundaries. Segmentation results demonstrate that our method can achieve a higher segmentation precision and provide a promising way to clinical application.更多还原