心脏磁共振图像左心室分割算法研究
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摘要
近年来,心血管疾病(cardiovascular disease,CVD)已经成为人类生命健康的头号杀手。随着人类生活水平的提高和医学技术的发展,对心血管疾病的早期定量诊断和风险评估对延长人类预期寿命,提高人类的生活质量,具有非常关键的作用。
心脏在心电信号控制下作周期性收缩和舒张运动,同时向全身各处供血以维持组织的新陈代谢功能。随着现代医学成像技术的发展,能够对心脏进行动态成像的现代医学影像设备主要包括磁共振成像(magnetic resonance imaging,MRI)、计算机断层成像(x-ray computer tomography,CT)和超声成像(ultrasonicimaging,US)等。这些影像设备在成像速度、时间和空间分辨率方面快速提高,基本实现了对心脏的3D动态成像。
心脏磁共振成像具有较好的软组织对比度、无放射性、无需注射或服用示踪剂、和能够任意平面进行成像的能力。另外,由于对血流具有较高对比度,因此,心脏磁共振成像还可用于评价血流的流速、流量。总的来说,心脏磁共振成像能准确地反映心脏的解剖结构、形态功能、血流特性和心肌活性。
影像设备的进步带来了图像数据的大幅度增加。心脏成像通常是空间上的2D或3D成像,在心动周期内随时间动态变化构成所谓4D数据。传统在观片灯上观察2D图像的工作方式已逐渐不能适合医学应用的需要。首先,4D数据很难以2D方式直观显示。过去观察静态投影或断层图像辅以空间想象的图像解读方法,面对高维动态心脏医学图像数据时比较困难;其次,只依靠放射医生的主观经验很难做到可重复的提取和定量分析具有临床诊断意义的信息。个体差别、可重复性、工作效率等极大地限制了现代影像设备在心血管检查临床实践中的充分应用。虽然部分新设备也带有简单的图像分析软件,但是它们一般是基于简单的几何模型来提供传统全局参数粗略估计。在医学应用中,临床医生迫切需要新的计算机辅助分析工具用于从海量医学图像中提取出客观、定量、有临床意义的诊断信息来辅助诊断各种疾病。在心脏磁共振图像中,各种解剖结构尤其是左心室的分割则是心脏磁共振成像在临床应用的重要瓶颈之一。
图像分割是图像处理中最重要、基础也是研究内容最为广泛的领域之一,研究者曾提出过基于不同理论框架和不同图像特征的图像分割方法。图像分割技术的成功应用与其处理对象和应用领域密切相关。心脏医学图像有其固有特点,必须结合图像分割理论、具体医学图像特点以及感兴趣的解剖结构来研究有效的心脏医学图像分割算法。
本文针对心脏磁共振图像,在左心室内外膜的分割方面上做了以下工作:
一.提出小波多尺度框架下的动态方向梯度矢量流模型,实现了对心脏磁共振图像左心室内壁的鲁棒分割。
基于图像梯度的经典主动轮廓模型是解决图像分割问题的有力工具。由于心脏磁共振图像中右心室和心外壁其他组织的影响,单纯的基于梯度的曲线演化在提取左心室内膜时很容易发生泄漏,这是传统主动轮廓模型的固有缺陷之一。2006年Chen Jierong等人提出了动态梯度矢量流主动轮廓模型(DynamicDirectional Gradient Vector Flow,DDGVF),该模型能够提取具有特定极性的目标边缘,且具有对轮廓线初始位置的敏感性低,能够向图像中的深度凹陷区域演化等优点,但是,由于该模型首先需要对图像进行高斯平滑,这样会导致目标边缘模糊,从而产生分割误差。
小波多尺度分析可以同时在时(空)域和频域上进行分解信号,是信号分析发展史上里程碑式的进展,被广泛应用于信号处理、图像分析、模式识别、计算机视觉等研究领域。由于小波基对信号的平滑和降采样的作用,小波分解高层的相邻低频系数之间的相关性降低,近似于高斯分布。而且小波分解高层,构成图像主要能量的灰度信息大部分得到了保留,噪声大部分被抑制。这些特点提示可以在小波分解后不同尺度的低频图像中采用相同的分割模型,而将最终的分割结果在尺度间进行传播,利用低分辨率尺度图像的分割结果来约束高分辨率尺度图像中的曲线演化,从而得到稳健而精确的结果。
为了实现心脏磁共振图像左心室内膜的鲁棒分割,本算法在小波分解最高层采用动态方向梯度矢量流主动轮廓线模型得到粗略的左心室内膜边缘,然后,将所得结果作为下一层低频近似图像的初始轮廓,利用当前尺度下的图像信息来推动曲线演化,继续细化左心室内膜轮廓的分割,最终,以最低尺度上的分割结果作为左心室内膜轮廓。算法的具体步骤如下:
(一)利用高斯函数的一阶偏导数作为小波基函数,对心脏磁共振图像做二进小波变换,得到各层的低频图像。从小波分解的最高层层低频图像开始,计算小波变换后的模值图像和幅角并计算沿幅角方向上的模极大值,得到模极大值图像,由模极大值图像计算动态方向梯度矢量流场;
(二)在高尺度上,利用计算所得动态方向梯度矢量流场主动轮廓模型引导轮廓线演化,提取目标边缘;
(三)在当前尺度上,以上一尺度所得到的最终结果作为该尺度的初始轮廓线,并利用该尺度上的模极大值图像计算动态方向梯度矢量流场,并用动态方向梯度矢量流场主动轮廓模型提取该尺度上的目标边缘;
(四)重复步骤(三),直至最低尺度,这时所得到的结果即为最终结果。
由于综合利用了图像中多个尺度上的边缘信息,算法具有不易发生边界泄漏和对轮廓线初始位置依赖性小等优点。在实际临床心脏磁共振图像上的实验表明,算法分割结果和人工分割结果的误差在合理范围内。
二.提出了一种基于几何主动轮廓模型的分割算法,结合边缘保持自适应各向异性滤波和K均值聚类分析方法,实现了对心脏磁共振图像左心室内壁的鲁棒分割。
由于心脏快速非刚性运动和快速成像序列物理原理等各种原因,心脏磁共振图像中心肌与左右心室的边界比较模糊。另外,心脏磁共振图像中不仅含有丰富的纹理信息,还含有许多噪声和伪影。在提取左心室轮廓的过程中,图像中的噪声和无关的纹理信息也会影响最终结果。如果不采用特定的预处理措施很难保证分割结果的鲁棒性和精确性。
传统的预处理方法是基于高斯卷积核的平滑运算。这种方法虽然可以去除一部分噪声,但是也会使目标区域的边缘变得更加模糊,从而失去一部分边缘信息。而且,高斯平滑对于图像中的无关纹理信息没有任何作用。自适应边缘保持各向异性滤波算法是基于图像灰度信息的局部不连续性和纹理不连续性的平滑方法,该方法不仅可以平滑图像中噪声和伪影,而且可以保留目标区域的边缘信息并去除一部分无关纹理信息。对于心脏磁共振图像来说,在左右心室血池内和心肌内部区域的像素灰度值变化较小,因此这些区域内的局部不连续性和纹理不连续性都较小,经过该算法平滑后提高了灰度均匀性。左右心室与心肌交界处局部不连续性和纹理不连续性都较大,经过平滑后保留了不连续性。而对于受噪声影响的像素点和一些无关纹理区域,虽然其局部不连续度较大,但是纹理不连续度较小,因而在平滑后也将其变为灰度均匀区域。
K均值算法是一种基于区域灰度信息的动态聚类方法,通过K均值聚类可以提高左右心室血池和心肌区域内的像素灰度值的均匀性,从而提高左右心室血池与心肌处的对比度。
在提取左心室内膜边缘时,本文采用了一种包含边缘信息和区域信息的几何主动轮廓模型。该模型中引入了轮廓线与符号距离函数之间的偏离程度作为轮廓线内能,使得采用水平集方法求解时无需对轮廓线作周期性的重复初始化,从而极大地提高了分割模型的收敛速度和鲁棒性。通过对临床心脏磁共振图像数据的实验结果显示,该方法所得分割结果和专家手动分割结果的距离在合理范围内。
三.提出一种包含区域、边缘与先验信息的心脏磁共振图像内外壁联合分割算法
为了计算左心室质量(left ventricle mass,LVM)、每搏输出量和射血分数等重要心功能参数,需要提取左心室的内外膜轮廓。左心室内膜分割的主要影响因素包括噪声及灰度不均匀性、右心室血池及乳头肌。左心室外膜分割的主要影响因素包括噪声及灰度不均匀性、左心室血池、左心室外膜周围的组织和外膜处边缘信息的丢失和不完整。本文提出一种包含区域信息、边缘信息和约束条件的变分框架,实现了左心室内外膜的鲁棒分割。本文所提出的变分框架主要包括三个部分:
(一)基于边缘信息的轮廓线引导项
虽然动态方向梯度矢量主动轮廓模型能够正确识别边缘的极性,但是其本质上仍属于参数主动轮廓模型,处理轮廓线几何拓扑结构变化的能力较差。本文将动态方向梯度矢量流与几何主动轮廓模型相结合,提出了能够使轮廓线双向演化并且区分左心室内外膜的轮廓线引导项,同时将其用水平集方法表达并求解,提高了轮廓线处理几何拓扑形变的能力。由于动态方向梯度矢量流场是定义在演化曲线上的外力场,因此,本文采用了fast marching方法将其扩展至整个图像域。
(二)基于区域信息的轮廓线引导项
对于噪声较多,边缘信息比较复杂的图像来说,单纯依靠图像中的边缘信息的分割方法对于初始位置较为敏感,鲁棒性较差,而基于区域统计信息的分割方法则具有较好的稳定性和抗干扰性,对于初始位置的依赖性也较低。基于这种考虑,本文在假设心脏磁共振图像的灰度分布符合高斯分布的基础上,提出了基于区域灰度最大后验估计的轮廓线引导项。为了提高轮廓线的收敛速度,本文中还在变分框架中加上了基于轮廓线与符号距离函数之间的偏差的约束项,从而省略了水平集方法中的周期性重复初始化
(三)基于心脏解剖结构的约束条件
由于射频场不均匀及其噪声的影响,在心脏磁共振图像的左心室外膜处经常会出现边缘信息丢失或者边缘信息不完整的情况,此时,采用主动轮廓模型进行分割时会发生轮廓线的泄漏。为了防止泄漏现象的发生,本文引入了基于心脏解剖结构信息的约束条件。具体来说,首先,将左心室外膜分为不同的区域,根据解剖知识可以知道在那些区域容易丢失边缘信息(一般是图像中心脏与肺和肝脏的相接处),其次,由于在一个心动周期内,心肌厚度相差不大,因此,左心室外膜距离左心室质心的最大距离不能超过一定的阈值。如果左心室外膜距离左心室质心的最大距离接近该阈值,则要降低轮廓线的演化速度;如果超出该阈值,则应停止演化或改变轮廓线的演化方向。
通过对临床心脏磁共振图像数据的实验结果显示,本文所提出的变分框架能够较好地提取左心室内外膜轮廓,抗干扰性好,鲁棒性高,在一定程度上能够防止轮廓线在左心室外膜处泄漏,能够排除左心室内壁乳头肌的干扰,能够降低轮廓线对于其初始位置的敏感性。最终所得分割结果和专家手动分割结果的距离在合理范围内。
In recent years, cardiovascular diseases(CVD) have become the most dangerous killer to human health. With the improvement of living condition and development of modern medical techniques , the early quantitative diagnosis and accurate evaluation of CVD are critical to improving quality of life and prolong life expectancy.
Under the control of electrocardio signal, the heart contracts and relaxes periodically in order to pump blood to the whole body to maintain the metabolism of the tissues. With the rapid development of medical imaging techniques, there are the modern medical imaging equipments which are capable of cardiovascular imaging such as magnetic resonance imaging (MRI), computer tomography (CT) and ultrasonic (US) . It is the dramatically shortening of imaging time and rapid improving in spatial and temporal resolution that make these equipments feasible for dynamic 3D cardiovascular imaging.
MR is an ideal modality for cardiac imaging for it's good soft tissue contrast, nonradioactive, no need of tracer and arbitrary imaging plane. Additionally, it is feasible to evaluate the velocity and volume of blood flow through cardiac MR because of its good contrast for blood. As a whole, cardiac MR can show the cardiac anatomy, morphology and function, the properties of blood flow and viability of heart muscle.
The development of cardiac imaging equipments leads to enormous image data. Typically, cardiac imaging acquires 4D data which include 3D volume data changed with time during the cardiac cycle. Traditional method of 2D diagnosis by clinical experts' subjective observation is problematic. Firstly, it is hard to show 4D data in 2D form, the traditional way of observing projection image or tomography imaging can hardly pick up any clinical significant information from 4D data. Secondly, it is hard to get quantitative information merely by subjective analysis because manual delineation of chamber boundaries is time-consuming and prone to intra observer and inter observer variability. These facts limit the full exploitation of the imaging ability. Although some of the imaging equipments provide some simple computer aided image analysis software, they work based on some simple geometric models to calculate some global cardiac function indices. Therefore, new computer aided medical image analysis software aimed at getting clinical significant information for objective quantitative analysis is indispensable for diagnosis of cardiac diseases. It is the segmentation of anatomy structure such as left ventricle that has become the bottle-neck of cardiac image analysis which limit it's clinical application.
Image segmentation is one of the most fundamental、the most important and the most widespread research topics in the research areas of image processing and computer vision. Many segmentation methods which are based on different theory frameworks and image features have been proposed in recent years. The successful implementation of image segmentation technique in medical images is closely related to the objects which it deals with and the practical environment. Because the cardiac images have unique properties, it is necessary to combine segmentation techniques, the specified image features and anatomy structure of interesting together to achieve successful segmentation.
In this thesis, we have proposed several algorithms for segmentation of left ventricle in cardiac MR images. The main contributions of this thesis are listed as following.
一. A novel dynamic directional gradient vector flow active contour model in thewavelet multiscale framework is proposed in this thesis. The proposed algorithmis capable of delineating the endocardium borders in cardiac short axis MR imagesrobustly.
The classical active contour model is a powerful tool for image segmentation. However, when delieating left ventricle contour in cardiac MR images with classical active contour, the interference of right ventricle blood pool and the tissues outside the epicardium often lead to leakage of the contour which evolve solely based on gradient information. In 2006, Chen Jierong et al. proposed dynamic directional gradient vector active contour model which could seek edge with different direction and force the contour evolve into deeply concave region with low sensitivity to initial position of the contour, but the prerequisite gaussian smoothing would blur the edge and produce erroneous results.
Wavelets are a powerful mathematical tool for hierarchically decomposing signals both in frequency and spatial domain. The wavelets multiscale analysis is a milestone in signal analysis and is applied widly in the reseach area of signal analysis、image analysis、pattern recognitiona and computer vision. Because of the smoothing and downsampling of wavelet basis, the adjacent low frequency coefficients in higher scale have weak correlations which is similar to gaussian distribution. Additionally, in higher scale of wavelet decomposition, the gray information which include the main energy of the image are preserved and a lot of noise are suppressed. Thus, it is feasible to apply the same segmentation model in the low frequency images on different scales and restrain the evolution of the contour on high scales with the result on lower scales in order to get accurate and robust segmentation.
In order to get robust segmentation of left ventricle, the rude contour is delineated on the higher scale with DDGVF active contour firstly. On the current scale, the result of higher scale is used as initial position and the evolution of the contour is driven by image information. The process is repeated until the last scale is reached and the final segmentation result is left ventricle contour.
signal can be described in terms of a coarse approximation, plus details that range from broad to narrow. Regardless of whether the function of interest is an image, a curve, or a surface, wavelets provide an elegant technique for representing the levels of detail present. Wavelet theory uses a two-dimensional expansion set to characterize and give a time-frequency localization of a one-dimensional signal. Since this is a linear system, the signal can be reconstructed by a weighted sum of the basis functions. In contrast to the one-dimensional Fourier basis localized in only frequency, the wavelet basis is two-dimensional - localized in both frequency and time. A signal's energy, therefore, is usually well represented by just a few wavelet expansion coefficients. The algorithm can be descried by following procedures:
(一) The cardiac MR images are transformed using dyadic wavelet whose wavelet basis is the first derivative of gaussian function to obtain the low frequency images on each scale. From the highest scale to the lowest scale, the modulus and angle on each low frequency image are calculated to obtain the maximum of modulus which is used to calculate DDGVF;
(二) On the higher scale, the contour of left ventricle is delineated with corresponding DDGVF active contour model;
(三) On the current scale, the result of previous scale is used as initial contour andthe DDGVF is also calculated with the modulus maximum;
(四) The process is repeated until the last scale is reached and the final result is left ventricle.
Because the edge information on each scale are utilized together, the active contour can avoid leakage and has low sensitivity to the initial position. Encouraging experimental results are provided using real cardiac MR data.
二. A novel algorithm based on geometric active contour model which integrates the edge preserved adaptive anisotropic diffusion filtering and K means clustering is proposed in this paper for robust segmentation of left ventricle in cardiac MR images.
Because of the non-rigid rapid motion of heart and the physical principle of fast imaging sequence, the boundary among myocardium and ventricles is often blured. In addition, the cardiac MR images contain not only rich texture information, but also much noise and artifacts. In the process of delineating the contour of left ventricle, the noise and irrelevant texture will have great effect upon the final segmentation. The accuracy and robustness could not be guaranteed without appropriate preprocessing.
The classical preprocessing is smoothing based on gaussian kernal. Through the gaussian smoothing would remove a portion of noise, it also would blur the edge of the object, which would result in the loss of edge information, moreover, the gaussian smoothing has no effect on irrelevant texture. The edge preserved adaptive anisotropic diffusion filtering based upon local discontinuity and texture discontinuity can not only remove much noise、artifacts and unrelated texture, but also perserve the edge information. In cardiac MR images, the pixels in the ventricle blood pool and myocardium have low local discontinuity and texture discontinuity for the similarity in gray level, thus the homogeneity of these region will be improved after smoothing. On the interface of myocardium and ventricle, the local discontinuity and texture discontinuity are high, therefore the discontinuity is perserved after smoothing. The pixels produced by noise and unrelated texture with high local discontinuity and low texture discontinuity will be removed after the smoothing.
K means algorithm is a dynamic clustering method based on global region information. Through the K means clustering, the homogeneity in the ventricle blood pool and myocardium and the contrast between them will be improved. The endocardium is delineated with a geometric active contour model which integrates the boundary information and global region intensity information. In this model, deviation of the contour with signed distance function is used as internal energy so that the reinitialization in the classical level set method become needless and the rate of convergence and robustness will be increased. The proposed algorithm has been applied to real images with promising results.
三. An novel approach integrating global region intensity, boundary and prior thatused for associate segmentation of endocardium and epicardium is proposed.
In order to calculate the left ventricle mass、ejection fraction and stroke volume, the endocarium and epicardium should all be delineated and the inhomogeneity of gray level、ventricle blood pool and the loss and incomplete boundary information will interfere with the segmentation. A variational framework integrating visual information with prior knowledge is proposed so as to delineated the endocardium and epicardium robustly. The framework can be separate to three parts:
(一) The boundary driven term
The DDGVF active contour model is a parametric active contour model which can't handle the topology changes of the contour. In this paper, a novel boundary attraction term is designed to embody the merits of DDGVF and geometric active contour model. The endocardium and epicardium can be differentiated by DDGVF and the topology changes can be handled through level set representations. The DDGVF is an dynamic external force field defined on the interface and should be extended to the whole image domain.
(二) The region driven term
Because of the noise and complicated edge information, the segmentation methods which primarily rely on boundary information have great dependency of initial conditions and bad robustness, however, the approaches relied on global intensity properties have low sensitivities to initial conditions and good robustness and anti-interference. In this paper, a statistical region component based on bayesian maximum a posterior estimation is proposed on the assumption that the gray level of the pixels in the cardiac MR images agree with gaussian distribution. In order to increase to velocity of convergence, the deviation between the contour and signed distance function is used as an constraint to omit the procedure of reinitiation in the level set methods.
(三) The prior knowledge
Because of the inhomogeneity of radio-frequency field and the interference of noise, the visual information related with considered application can be misleading, physically corrupted and some time incomplete. Therefore, it can lead to leakage of the active contour model without taking into account specific application constraints. In order to deal with these limitations and physically corrupted visual information, the constraints based on anatomical structures is proposed. Firstly, the epicardium is partitioned into four regions and through anatomical knowledge, the region with great probability to contain physically corrupted visual information can be found. Secondly, because the thickness of myocardium can't change much during one cardiac cycle, the maximum euclidean distance between the epicardium and the centroid of left ventricle can't exceed a specific threshold. If the distance is close to the threshold, the evolving velocity of the contour should be slowed down, if it exceed the threshold, the evolution should be changed or stopped.
Experiments on real cardiac MR images with excouraging results show that the endocardium and epicardium can be delineated with good anti-interference and robustness, the dependency can be exempted from initial conditions and leakage and intervention of papillary can't be prevented.
心脏在心电信号控制下作周期性收缩和舒张运动,同时向全身各处供血以维持组织的新陈代谢功能。随着现代医学成像技术的发展,能够对心脏进行动态成像的现代医学影像设备主要包括磁共振成像(magnetic resonance imaging,MRI)、计算机断层成像(x-ray computer tomography,CT)和超声成像(ultrasonicimaging,US)等。这些影像设备在成像速度、时间和空间分辨率方面快速提高,基本实现了对心脏的3D动态成像。
心脏磁共振成像具有较好的软组织对比度、无放射性、无需注射或服用示踪剂、和能够任意平面进行成像的能力。另外,由于对血流具有较高对比度,因此,心脏磁共振成像还可用于评价血流的流速、流量。总的来说,心脏磁共振成像能准确地反映心脏的解剖结构、形态功能、血流特性和心肌活性。
影像设备的进步带来了图像数据的大幅度增加。心脏成像通常是空间上的2D或3D成像,在心动周期内随时间动态变化构成所谓4D数据。传统在观片灯上观察2D图像的工作方式已逐渐不能适合医学应用的需要。首先,4D数据很难以2D方式直观显示。过去观察静态投影或断层图像辅以空间想象的图像解读方法,面对高维动态心脏医学图像数据时比较困难;其次,只依靠放射医生的主观经验很难做到可重复的提取和定量分析具有临床诊断意义的信息。个体差别、可重复性、工作效率等极大地限制了现代影像设备在心血管检查临床实践中的充分应用。虽然部分新设备也带有简单的图像分析软件,但是它们一般是基于简单的几何模型来提供传统全局参数粗略估计。在医学应用中,临床医生迫切需要新的计算机辅助分析工具用于从海量医学图像中提取出客观、定量、有临床意义的诊断信息来辅助诊断各种疾病。在心脏磁共振图像中,各种解剖结构尤其是左心室的分割则是心脏磁共振成像在临床应用的重要瓶颈之一。
图像分割是图像处理中最重要、基础也是研究内容最为广泛的领域之一,研究者曾提出过基于不同理论框架和不同图像特征的图像分割方法。图像分割技术的成功应用与其处理对象和应用领域密切相关。心脏医学图像有其固有特点,必须结合图像分割理论、具体医学图像特点以及感兴趣的解剖结构来研究有效的心脏医学图像分割算法。
本文针对心脏磁共振图像,在左心室内外膜的分割方面上做了以下工作:
一.提出小波多尺度框架下的动态方向梯度矢量流模型,实现了对心脏磁共振图像左心室内壁的鲁棒分割。
基于图像梯度的经典主动轮廓模型是解决图像分割问题的有力工具。由于心脏磁共振图像中右心室和心外壁其他组织的影响,单纯的基于梯度的曲线演化在提取左心室内膜时很容易发生泄漏,这是传统主动轮廓模型的固有缺陷之一。2006年Chen Jierong等人提出了动态梯度矢量流主动轮廓模型(DynamicDirectional Gradient Vector Flow,DDGVF),该模型能够提取具有特定极性的目标边缘,且具有对轮廓线初始位置的敏感性低,能够向图像中的深度凹陷区域演化等优点,但是,由于该模型首先需要对图像进行高斯平滑,这样会导致目标边缘模糊,从而产生分割误差。
小波多尺度分析可以同时在时(空)域和频域上进行分解信号,是信号分析发展史上里程碑式的进展,被广泛应用于信号处理、图像分析、模式识别、计算机视觉等研究领域。由于小波基对信号的平滑和降采样的作用,小波分解高层的相邻低频系数之间的相关性降低,近似于高斯分布。而且小波分解高层,构成图像主要能量的灰度信息大部分得到了保留,噪声大部分被抑制。这些特点提示可以在小波分解后不同尺度的低频图像中采用相同的分割模型,而将最终的分割结果在尺度间进行传播,利用低分辨率尺度图像的分割结果来约束高分辨率尺度图像中的曲线演化,从而得到稳健而精确的结果。
为了实现心脏磁共振图像左心室内膜的鲁棒分割,本算法在小波分解最高层采用动态方向梯度矢量流主动轮廓线模型得到粗略的左心室内膜边缘,然后,将所得结果作为下一层低频近似图像的初始轮廓,利用当前尺度下的图像信息来推动曲线演化,继续细化左心室内膜轮廓的分割,最终,以最低尺度上的分割结果作为左心室内膜轮廓。算法的具体步骤如下:
(一)利用高斯函数的一阶偏导数作为小波基函数,对心脏磁共振图像做二进小波变换,得到各层的低频图像。从小波分解的最高层层低频图像开始,计算小波变换后的模值图像和幅角并计算沿幅角方向上的模极大值,得到模极大值图像,由模极大值图像计算动态方向梯度矢量流场;
(二)在高尺度上,利用计算所得动态方向梯度矢量流场主动轮廓模型引导轮廓线演化,提取目标边缘;
(三)在当前尺度上,以上一尺度所得到的最终结果作为该尺度的初始轮廓线,并利用该尺度上的模极大值图像计算动态方向梯度矢量流场,并用动态方向梯度矢量流场主动轮廓模型提取该尺度上的目标边缘;
(四)重复步骤(三),直至最低尺度,这时所得到的结果即为最终结果。
由于综合利用了图像中多个尺度上的边缘信息,算法具有不易发生边界泄漏和对轮廓线初始位置依赖性小等优点。在实际临床心脏磁共振图像上的实验表明,算法分割结果和人工分割结果的误差在合理范围内。
二.提出了一种基于几何主动轮廓模型的分割算法,结合边缘保持自适应各向异性滤波和K均值聚类分析方法,实现了对心脏磁共振图像左心室内壁的鲁棒分割。
由于心脏快速非刚性运动和快速成像序列物理原理等各种原因,心脏磁共振图像中心肌与左右心室的边界比较模糊。另外,心脏磁共振图像中不仅含有丰富的纹理信息,还含有许多噪声和伪影。在提取左心室轮廓的过程中,图像中的噪声和无关的纹理信息也会影响最终结果。如果不采用特定的预处理措施很难保证分割结果的鲁棒性和精确性。
传统的预处理方法是基于高斯卷积核的平滑运算。这种方法虽然可以去除一部分噪声,但是也会使目标区域的边缘变得更加模糊,从而失去一部分边缘信息。而且,高斯平滑对于图像中的无关纹理信息没有任何作用。自适应边缘保持各向异性滤波算法是基于图像灰度信息的局部不连续性和纹理不连续性的平滑方法,该方法不仅可以平滑图像中噪声和伪影,而且可以保留目标区域的边缘信息并去除一部分无关纹理信息。对于心脏磁共振图像来说,在左右心室血池内和心肌内部区域的像素灰度值变化较小,因此这些区域内的局部不连续性和纹理不连续性都较小,经过该算法平滑后提高了灰度均匀性。左右心室与心肌交界处局部不连续性和纹理不连续性都较大,经过平滑后保留了不连续性。而对于受噪声影响的像素点和一些无关纹理区域,虽然其局部不连续度较大,但是纹理不连续度较小,因而在平滑后也将其变为灰度均匀区域。
K均值算法是一种基于区域灰度信息的动态聚类方法,通过K均值聚类可以提高左右心室血池和心肌区域内的像素灰度值的均匀性,从而提高左右心室血池与心肌处的对比度。
在提取左心室内膜边缘时,本文采用了一种包含边缘信息和区域信息的几何主动轮廓模型。该模型中引入了轮廓线与符号距离函数之间的偏离程度作为轮廓线内能,使得采用水平集方法求解时无需对轮廓线作周期性的重复初始化,从而极大地提高了分割模型的收敛速度和鲁棒性。通过对临床心脏磁共振图像数据的实验结果显示,该方法所得分割结果和专家手动分割结果的距离在合理范围内。
三.提出一种包含区域、边缘与先验信息的心脏磁共振图像内外壁联合分割算法
为了计算左心室质量(left ventricle mass,LVM)、每搏输出量和射血分数等重要心功能参数,需要提取左心室的内外膜轮廓。左心室内膜分割的主要影响因素包括噪声及灰度不均匀性、右心室血池及乳头肌。左心室外膜分割的主要影响因素包括噪声及灰度不均匀性、左心室血池、左心室外膜周围的组织和外膜处边缘信息的丢失和不完整。本文提出一种包含区域信息、边缘信息和约束条件的变分框架,实现了左心室内外膜的鲁棒分割。本文所提出的变分框架主要包括三个部分:
(一)基于边缘信息的轮廓线引导项
虽然动态方向梯度矢量主动轮廓模型能够正确识别边缘的极性,但是其本质上仍属于参数主动轮廓模型,处理轮廓线几何拓扑结构变化的能力较差。本文将动态方向梯度矢量流与几何主动轮廓模型相结合,提出了能够使轮廓线双向演化并且区分左心室内外膜的轮廓线引导项,同时将其用水平集方法表达并求解,提高了轮廓线处理几何拓扑形变的能力。由于动态方向梯度矢量流场是定义在演化曲线上的外力场,因此,本文采用了fast marching方法将其扩展至整个图像域。
(二)基于区域信息的轮廓线引导项
对于噪声较多,边缘信息比较复杂的图像来说,单纯依靠图像中的边缘信息的分割方法对于初始位置较为敏感,鲁棒性较差,而基于区域统计信息的分割方法则具有较好的稳定性和抗干扰性,对于初始位置的依赖性也较低。基于这种考虑,本文在假设心脏磁共振图像的灰度分布符合高斯分布的基础上,提出了基于区域灰度最大后验估计的轮廓线引导项。为了提高轮廓线的收敛速度,本文中还在变分框架中加上了基于轮廓线与符号距离函数之间的偏差的约束项,从而省略了水平集方法中的周期性重复初始化
(三)基于心脏解剖结构的约束条件
由于射频场不均匀及其噪声的影响,在心脏磁共振图像的左心室外膜处经常会出现边缘信息丢失或者边缘信息不完整的情况,此时,采用主动轮廓模型进行分割时会发生轮廓线的泄漏。为了防止泄漏现象的发生,本文引入了基于心脏解剖结构信息的约束条件。具体来说,首先,将左心室外膜分为不同的区域,根据解剖知识可以知道在那些区域容易丢失边缘信息(一般是图像中心脏与肺和肝脏的相接处),其次,由于在一个心动周期内,心肌厚度相差不大,因此,左心室外膜距离左心室质心的最大距离不能超过一定的阈值。如果左心室外膜距离左心室质心的最大距离接近该阈值,则要降低轮廓线的演化速度;如果超出该阈值,则应停止演化或改变轮廓线的演化方向。
通过对临床心脏磁共振图像数据的实验结果显示,本文所提出的变分框架能够较好地提取左心室内外膜轮廓,抗干扰性好,鲁棒性高,在一定程度上能够防止轮廓线在左心室外膜处泄漏,能够排除左心室内壁乳头肌的干扰,能够降低轮廓线对于其初始位置的敏感性。最终所得分割结果和专家手动分割结果的距离在合理范围内。
In recent years, cardiovascular diseases(CVD) have become the most dangerous killer to human health. With the improvement of living condition and development of modern medical techniques , the early quantitative diagnosis and accurate evaluation of CVD are critical to improving quality of life and prolong life expectancy.
Under the control of electrocardio signal, the heart contracts and relaxes periodically in order to pump blood to the whole body to maintain the metabolism of the tissues. With the rapid development of medical imaging techniques, there are the modern medical imaging equipments which are capable of cardiovascular imaging such as magnetic resonance imaging (MRI), computer tomography (CT) and ultrasonic (US) . It is the dramatically shortening of imaging time and rapid improving in spatial and temporal resolution that make these equipments feasible for dynamic 3D cardiovascular imaging.
MR is an ideal modality for cardiac imaging for it's good soft tissue contrast, nonradioactive, no need of tracer and arbitrary imaging plane. Additionally, it is feasible to evaluate the velocity and volume of blood flow through cardiac MR because of its good contrast for blood. As a whole, cardiac MR can show the cardiac anatomy, morphology and function, the properties of blood flow and viability of heart muscle.
The development of cardiac imaging equipments leads to enormous image data. Typically, cardiac imaging acquires 4D data which include 3D volume data changed with time during the cardiac cycle. Traditional method of 2D diagnosis by clinical experts' subjective observation is problematic. Firstly, it is hard to show 4D data in 2D form, the traditional way of observing projection image or tomography imaging can hardly pick up any clinical significant information from 4D data. Secondly, it is hard to get quantitative information merely by subjective analysis because manual delineation of chamber boundaries is time-consuming and prone to intra observer and inter observer variability. These facts limit the full exploitation of the imaging ability. Although some of the imaging equipments provide some simple computer aided image analysis software, they work based on some simple geometric models to calculate some global cardiac function indices. Therefore, new computer aided medical image analysis software aimed at getting clinical significant information for objective quantitative analysis is indispensable for diagnosis of cardiac diseases. It is the segmentation of anatomy structure such as left ventricle that has become the bottle-neck of cardiac image analysis which limit it's clinical application.
Image segmentation is one of the most fundamental、the most important and the most widespread research topics in the research areas of image processing and computer vision. Many segmentation methods which are based on different theory frameworks and image features have been proposed in recent years. The successful implementation of image segmentation technique in medical images is closely related to the objects which it deals with and the practical environment. Because the cardiac images have unique properties, it is necessary to combine segmentation techniques, the specified image features and anatomy structure of interesting together to achieve successful segmentation.
In this thesis, we have proposed several algorithms for segmentation of left ventricle in cardiac MR images. The main contributions of this thesis are listed as following.
一. A novel dynamic directional gradient vector flow active contour model in thewavelet multiscale framework is proposed in this thesis. The proposed algorithmis capable of delineating the endocardium borders in cardiac short axis MR imagesrobustly.
The classical active contour model is a powerful tool for image segmentation. However, when delieating left ventricle contour in cardiac MR images with classical active contour, the interference of right ventricle blood pool and the tissues outside the epicardium often lead to leakage of the contour which evolve solely based on gradient information. In 2006, Chen Jierong et al. proposed dynamic directional gradient vector active contour model which could seek edge with different direction and force the contour evolve into deeply concave region with low sensitivity to initial position of the contour, but the prerequisite gaussian smoothing would blur the edge and produce erroneous results.
Wavelets are a powerful mathematical tool for hierarchically decomposing signals both in frequency and spatial domain. The wavelets multiscale analysis is a milestone in signal analysis and is applied widly in the reseach area of signal analysis、image analysis、pattern recognitiona and computer vision. Because of the smoothing and downsampling of wavelet basis, the adjacent low frequency coefficients in higher scale have weak correlations which is similar to gaussian distribution. Additionally, in higher scale of wavelet decomposition, the gray information which include the main energy of the image are preserved and a lot of noise are suppressed. Thus, it is feasible to apply the same segmentation model in the low frequency images on different scales and restrain the evolution of the contour on high scales with the result on lower scales in order to get accurate and robust segmentation.
In order to get robust segmentation of left ventricle, the rude contour is delineated on the higher scale with DDGVF active contour firstly. On the current scale, the result of higher scale is used as initial position and the evolution of the contour is driven by image information. The process is repeated until the last scale is reached and the final segmentation result is left ventricle contour.
signal can be described in terms of a coarse approximation, plus details that range from broad to narrow. Regardless of whether the function of interest is an image, a curve, or a surface, wavelets provide an elegant technique for representing the levels of detail present. Wavelet theory uses a two-dimensional expansion set to characterize and give a time-frequency localization of a one-dimensional signal. Since this is a linear system, the signal can be reconstructed by a weighted sum of the basis functions. In contrast to the one-dimensional Fourier basis localized in only frequency, the wavelet basis is two-dimensional - localized in both frequency and time. A signal's energy, therefore, is usually well represented by just a few wavelet expansion coefficients. The algorithm can be descried by following procedures:
(一) The cardiac MR images are transformed using dyadic wavelet whose wavelet basis is the first derivative of gaussian function to obtain the low frequency images on each scale. From the highest scale to the lowest scale, the modulus and angle on each low frequency image are calculated to obtain the maximum of modulus which is used to calculate DDGVF;
(二) On the higher scale, the contour of left ventricle is delineated with corresponding DDGVF active contour model;
(三) On the current scale, the result of previous scale is used as initial contour andthe DDGVF is also calculated with the modulus maximum;
(四) The process is repeated until the last scale is reached and the final result is left ventricle.
Because the edge information on each scale are utilized together, the active contour can avoid leakage and has low sensitivity to the initial position. Encouraging experimental results are provided using real cardiac MR data.
二. A novel algorithm based on geometric active contour model which integrates the edge preserved adaptive anisotropic diffusion filtering and K means clustering is proposed in this paper for robust segmentation of left ventricle in cardiac MR images.
Because of the non-rigid rapid motion of heart and the physical principle of fast imaging sequence, the boundary among myocardium and ventricles is often blured. In addition, the cardiac MR images contain not only rich texture information, but also much noise and artifacts. In the process of delineating the contour of left ventricle, the noise and irrelevant texture will have great effect upon the final segmentation. The accuracy and robustness could not be guaranteed without appropriate preprocessing.
The classical preprocessing is smoothing based on gaussian kernal. Through the gaussian smoothing would remove a portion of noise, it also would blur the edge of the object, which would result in the loss of edge information, moreover, the gaussian smoothing has no effect on irrelevant texture. The edge preserved adaptive anisotropic diffusion filtering based upon local discontinuity and texture discontinuity can not only remove much noise、artifacts and unrelated texture, but also perserve the edge information. In cardiac MR images, the pixels in the ventricle blood pool and myocardium have low local discontinuity and texture discontinuity for the similarity in gray level, thus the homogeneity of these region will be improved after smoothing. On the interface of myocardium and ventricle, the local discontinuity and texture discontinuity are high, therefore the discontinuity is perserved after smoothing. The pixels produced by noise and unrelated texture with high local discontinuity and low texture discontinuity will be removed after the smoothing.
K means algorithm is a dynamic clustering method based on global region information. Through the K means clustering, the homogeneity in the ventricle blood pool and myocardium and the contrast between them will be improved. The endocardium is delineated with a geometric active contour model which integrates the boundary information and global region intensity information. In this model, deviation of the contour with signed distance function is used as internal energy so that the reinitialization in the classical level set method become needless and the rate of convergence and robustness will be increased. The proposed algorithm has been applied to real images with promising results.
三. An novel approach integrating global region intensity, boundary and prior thatused for associate segmentation of endocardium and epicardium is proposed.
In order to calculate the left ventricle mass、ejection fraction and stroke volume, the endocarium and epicardium should all be delineated and the inhomogeneity of gray level、ventricle blood pool and the loss and incomplete boundary information will interfere with the segmentation. A variational framework integrating visual information with prior knowledge is proposed so as to delineated the endocardium and epicardium robustly. The framework can be separate to three parts:
(一) The boundary driven term
The DDGVF active contour model is a parametric active contour model which can't handle the topology changes of the contour. In this paper, a novel boundary attraction term is designed to embody the merits of DDGVF and geometric active contour model. The endocardium and epicardium can be differentiated by DDGVF and the topology changes can be handled through level set representations. The DDGVF is an dynamic external force field defined on the interface and should be extended to the whole image domain.
(二) The region driven term
Because of the noise and complicated edge information, the segmentation methods which primarily rely on boundary information have great dependency of initial conditions and bad robustness, however, the approaches relied on global intensity properties have low sensitivities to initial conditions and good robustness and anti-interference. In this paper, a statistical region component based on bayesian maximum a posterior estimation is proposed on the assumption that the gray level of the pixels in the cardiac MR images agree with gaussian distribution. In order to increase to velocity of convergence, the deviation between the contour and signed distance function is used as an constraint to omit the procedure of reinitiation in the level set methods.
(三) The prior knowledge
Because of the inhomogeneity of radio-frequency field and the interference of noise, the visual information related with considered application can be misleading, physically corrupted and some time incomplete. Therefore, it can lead to leakage of the active contour model without taking into account specific application constraints. In order to deal with these limitations and physically corrupted visual information, the constraints based on anatomical structures is proposed. Firstly, the epicardium is partitioned into four regions and through anatomical knowledge, the region with great probability to contain physically corrupted visual information can be found. Secondly, because the thickness of myocardium can't change much during one cardiac cycle, the maximum euclidean distance between the epicardium and the centroid of left ventricle can't exceed a specific threshold. If the distance is close to the threshold, the evolving velocity of the contour should be slowed down, if it exceed the threshold, the evolution should be changed or stopped.
Experiments on real cardiac MR images with excouraging results show that the endocardium and epicardium can be delineated with good anti-interference and robustness, the dependency can be exempted from initial conditions and leakage and intervention of papillary can't be prevented.
引文
[1]世界卫生统计年报:World Health Statistical Annual,2002,世界卫生组织
[2]中华人民共和国卫生部中国卫生统计年鉴2005,北京:人民卫生出版社2005.
[3]Sating,D.,et al.,Computer-assisted analysis of 4D cardiac MR image sequences after myocardial infarction.Methods Inf Med,2006.45(4):377-383.
[4]Dawson,D.,et al.,Quantitative 3-dimensional echocardiography for accurate and rapid cardiac phenotype characterization in mice.Circulation,2004.110(12):1632-1637.
[5]van der Geest,R.J.,et al.,Evaluation of a new method for automated detection of left ventricular boundaries in time series of magnetic resonance images using an Active Appearance Motion Model.J Cardiovasc Magn Reson,2004.6(3):609-617.
[6]Sugioka,K.,et al.,Rapid and accurate noninvasive assessment of global left ventricular systolic function using biplane advanced automated contour tracking method.J Am Soc Echocardiogr,2003.16(12):1237-1243.
[7]Podgoreanu,M.V.,et al.,Quantitative echocardiographic assessment of regional wall motion and left ventricular asynchrony with color kinesis in cardiac surgery patients.Anesth Analg,2003.96(5):1294-1300.
[8]Wolf,I.,et al.,ROPES:a semiantomated segmentation method for accelerated analysis of three-dimensional echocardiographic data.IEEE Trans Med Imaging,2002.21(9):1091-1104.
[9]van der Geest,R.J.and J.H.Reiber,Quantification in cardiac MRI.J Magn Reson Imaging,1999.10(5):602-608.
[10]Wise,R.G.,et al.,Geometrical models of left ventricular contraction from MRI of the normal and spontaneously hypertensive rat heart.Phys Med Biol,1999.44(10):2657-2676.
[1]柏树令,应大君,王海杰等.系统解剖学[M].北京:人民卫生出版社,2002
[2]李坤成,孙泽民,赵世华,D.Revel.心血管磁共振成像诊断学[M].北京:人民卫生出版社,1997.
[3]Raymond Y.,Kwong.Cardiovascular Magnetic Resonance Imaging[M].New Jersey,Humana Press,2008.
[4]Tanki,N.,et al.,Quantification of left ventricular volumes from cardiac cine MRI using active contour model combined with gradient vector flow[J].Magn Reson Med Sci,2005.4(4):p.191-6.
[5]John D.,Grizzard.Robert M.,et al.Cardiovascular MRI in Practice A Teaching File Approach[M].USA,Springer.
[6]Aladl,U.E.,et al.,Automated image registration of gated cardiac single-photon emission computed tomography and magnetic resonance imaging.J Magn Reson Imaging,2004.19(3):283-90.
[1]Kass M.,Witkin A.,Terzopoulos D.Snake:Active contour models[J].Int.J.Comput.Vis.,1988,1(4):321-331.
[2]Cohen L.D.,On active contour models and balloons[J].Comput.Vis.,Graph.,Image Process.:Image Understand.,1991,53(2):211-218.
[3]Xu C.,Prince J.L.Snake,shapes,and gradient vector flow[J].IEEE Trans.Image Process.,1998,7(3):359-369.
[4]Xu C.,Prince J.L.,Generalized gradient vector flow external forces for active contours[J].Signal Process.,1998,71:1131-139.
[5]Xu C.,Prince J.L.,Global optimality of gradient vector flow[C].In Proc.IEEE Conf.Comp.Vision and Part.Recog.,2000,15-17.
[6]Tang J.,Millington S.,Action S.T.,et al.Ankle cartilage surface segmentation using directional gradient vector flow snake[C].In Proc.Int.Conf.Image Processing,2004,2745-2748.
[7]Jierong C.,Foo SW.Dynamic Directional Gradient Vector Flow for Snakes[J].IEEE Trans.Image Process.,2006,15(6):1563-1571.
[8]Ray N.,Acton S.T.,Motion gradient vector flow:an external force for tracking rolling leukocytes with shape and size constrained active contours[J].IEEE Trans.Med.Imag.,2004,23(12):1466-1478.
[9]周寿军,梁斌,陈武凡,心脏序列图像运动估计新方法:基于广义模糊梯度矢量流场的形变曲线运动估计与跟踪[J].计算机学报,2003,26(11):1470-1478.
Shoujun Zhou,Bin Liang,Wufan Chen.A New Approach to the Motion Estimation of Cardiac Image Sequences:Active Contour Motion Tracking Based on the Generalized Fuzzy Gradient Vector Flow[J].Chinese Journal of Computer,2003,26(11):373-376.
[10]王元全,贾云得,一种新的心脏核磁共振图像分割方法[J].计算机学报,2007,30(1):129-136.
Wang Yuanqun,Jia Yunde,A Novel Approach for segmentation of Cardiac Magnetic Resonance Images[J].Chinese Journal of Computer,2007,30(1):129-136.
[11]Bing Li,Acton S.T.,Active Contour External Force Using Vector Field Convolution for Image Segmentation[J].IEEE Trans.Image Process.,16(8):2096-2106.
[12]Ranganth S.,Contour extraction from cardiac MRI studies using snake[J].IEEE Trans.Image Process.,1995,14(2):328-338.
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[5]Ranganth S.,Contour extraction from cardiac MRI studies using snake[J].IEEE Trans.Image Process.,1995,14(2):328-338.
[6]Cohen L.D.,On active contour models and balloons[J].Comput.Vis.,Graph.,Image Process.:Image Understand.,1991,53(2):211-218.
[7]Chakraborty A.,Staib L.H.,J.S.Duncan.Deformable boundary finding in medical images by integrating gradient and region information[J].IEEE Trans.Image Process.,1996,15(6):859-870.
[8]Xu C.,Prince J.L.,Snake,shapes,and gradient vector flow[J].IEEE Trans.Image Process.,1998,7(3):359-369.
[9]Tang J.,Millington S.,Action S.T.,et al.Anlde cartilage surface segmentation using directional gradient vector flow snake[C].In Prec.Int.Conf.Image Processing,2004,2745-2748.
[10]Jierong C.,Foo SW.Dynamic Directional Gradient Vector Flow for Snakes[J].IEEE Trans.Image Process.,2006,15(6):1563-1571.
[11]周寿军,梁斌,陈武凡,心脏序列图像运动估计新方法:基于广义模糊梯度矢量流场的形变曲线运动估计与跟踪[J].计算机学报,2003,26(11):1470-1478.
Shoujun Zhou,Bin Liang,Wufan Chen.A New Approach to the Motion Estimation of Cardiac Image Sequences:Active Contour Motion Tracking Based on the Generalized Fuzzy Gradient Vector Flow[J].Chinese Journal of Computer,2003,26(11):373-376.
[12]王元全,贾云得.一种新的心脏核磁共振图像分割方法[J].计算机学报,2007,30(1):373-376.
Wang Yuanqun,Jia Yunde,A Novel Approach for segmentation of Cardiac Magnetic Resonance Images[J].Chinese Journal of Computer,2007,30(1):129-136.
[13]Bing Li,Acton S.T.,Active Contour External Force Using Vector Field Convolution for Image Segmentation[J].IEEE Trans.Image Process.,2008,16(8):2096-2106
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[10]Cheng J,Foo S W.Dynamic Directional Gradient Vector Flow for Snakes[J].IEEE Transactions on Image Processing,2006,15(6):1563-1571.
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[17]王元全,贾云得.一种新的心脏核磁共振图像分割方法[J].计算机学报,2007,30(1):129-136.
[1]Alejandro F.Frangi,Wiro J.Niessen.Three-Dimensional Modeling for Functional Analysis of Cardiac Images:A Review[J].IEEE Trans Med Imaging,2001,20(1),2-25.
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[1]Heye Zhang,Chun Lok Wong,Pengcheng Shi.An Inverse Recovery of Cardiac Electrical Propagation from Image Sequences.MIAR 2006:132-139
[2]Heye Zhang,Chun Lok Wong,Pengcheng Shi:Estimation of Cardiac Electrical Propagation from Medical Image Sequence.MICCAI(2) 2006:528-535
[3]Tolli,T.,et al.,Artificially enlarged training set in image segmentation.Meal Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv,2006.9(Pt 1):p.75-82.
[4]Yah,A.T.,et al.,Characterization of microvascular dysfunction after acute myocardial infarction by cardiovascular magnetic resonance first-pass perfusion and late gadolinium enhancement imaging.J Cardiovasc Magn Reson,2006.8(6): p.831-7.
[5]Schuijf,J.D.,et al.,Quantification of myocardial infarct size and transmurality by contrast-enhanced magnetic resonance imaging in men.Am J Cardiol,2004.94(3):p.284-8.
[6]Petitjean,C,N.Rougon,and P.Cluzel,Assessment of myocardial function:a review of quantification methods and results using tagged MRI.J Cardiovasc Magn Reson,2005.7(2):p.501-16.
[2]中华人民共和国卫生部中国卫生统计年鉴2005,北京:人民卫生出版社2005.
[3]Sating,D.,et al.,Computer-assisted analysis of 4D cardiac MR image sequences after myocardial infarction.Methods Inf Med,2006.45(4):377-383.
[4]Dawson,D.,et al.,Quantitative 3-dimensional echocardiography for accurate and rapid cardiac phenotype characterization in mice.Circulation,2004.110(12):1632-1637.
[5]van der Geest,R.J.,et al.,Evaluation of a new method for automated detection of left ventricular boundaries in time series of magnetic resonance images using an Active Appearance Motion Model.J Cardiovasc Magn Reson,2004.6(3):609-617.
[6]Sugioka,K.,et al.,Rapid and accurate noninvasive assessment of global left ventricular systolic function using biplane advanced automated contour tracking method.J Am Soc Echocardiogr,2003.16(12):1237-1243.
[7]Podgoreanu,M.V.,et al.,Quantitative echocardiographic assessment of regional wall motion and left ventricular asynchrony with color kinesis in cardiac surgery patients.Anesth Analg,2003.96(5):1294-1300.
[8]Wolf,I.,et al.,ROPES:a semiantomated segmentation method for accelerated analysis of three-dimensional echocardiographic data.IEEE Trans Med Imaging,2002.21(9):1091-1104.
[9]van der Geest,R.J.and J.H.Reiber,Quantification in cardiac MRI.J Magn Reson Imaging,1999.10(5):602-608.
[10]Wise,R.G.,et al.,Geometrical models of left ventricular contraction from MRI of the normal and spontaneously hypertensive rat heart.Phys Med Biol,1999.44(10):2657-2676.
[1]柏树令,应大君,王海杰等.系统解剖学[M].北京:人民卫生出版社,2002
[2]李坤成,孙泽民,赵世华,D.Revel.心血管磁共振成像诊断学[M].北京:人民卫生出版社,1997.
[3]Raymond Y.,Kwong.Cardiovascular Magnetic Resonance Imaging[M].New Jersey,Humana Press,2008.
[4]Tanki,N.,et al.,Quantification of left ventricular volumes from cardiac cine MRI using active contour model combined with gradient vector flow[J].Magn Reson Med Sci,2005.4(4):p.191-6.
[5]John D.,Grizzard.Robert M.,et al.Cardiovascular MRI in Practice A Teaching File Approach[M].USA,Springer.
[6]Aladl,U.E.,et al.,Automated image registration of gated cardiac single-photon emission computed tomography and magnetic resonance imaging.J Magn Reson Imaging,2004.19(3):283-90.
[1]Kass M.,Witkin A.,Terzopoulos D.Snake:Active contour models[J].Int.J.Comput.Vis.,1988,1(4):321-331.
[2]Cohen L.D.,On active contour models and balloons[J].Comput.Vis.,Graph.,Image Process.:Image Understand.,1991,53(2):211-218.
[3]Xu C.,Prince J.L.Snake,shapes,and gradient vector flow[J].IEEE Trans.Image Process.,1998,7(3):359-369.
[4]Xu C.,Prince J.L.,Generalized gradient vector flow external forces for active contours[J].Signal Process.,1998,71:1131-139.
[5]Xu C.,Prince J.L.,Global optimality of gradient vector flow[C].In Proc.IEEE Conf.Comp.Vision and Part.Recog.,2000,15-17.
[6]Tang J.,Millington S.,Action S.T.,et al.Ankle cartilage surface segmentation using directional gradient vector flow snake[C].In Proc.Int.Conf.Image Processing,2004,2745-2748.
[7]Jierong C.,Foo SW.Dynamic Directional Gradient Vector Flow for Snakes[J].IEEE Trans.Image Process.,2006,15(6):1563-1571.
[8]Ray N.,Acton S.T.,Motion gradient vector flow:an external force for tracking rolling leukocytes with shape and size constrained active contours[J].IEEE Trans.Med.Imag.,2004,23(12):1466-1478.
[9]周寿军,梁斌,陈武凡,心脏序列图像运动估计新方法:基于广义模糊梯度矢量流场的形变曲线运动估计与跟踪[J].计算机学报,2003,26(11):1470-1478.
Shoujun Zhou,Bin Liang,Wufan Chen.A New Approach to the Motion Estimation of Cardiac Image Sequences:Active Contour Motion Tracking Based on the Generalized Fuzzy Gradient Vector Flow[J].Chinese Journal of Computer,2003,26(11):373-376.
[10]王元全,贾云得,一种新的心脏核磁共振图像分割方法[J].计算机学报,2007,30(1):129-136.
Wang Yuanqun,Jia Yunde,A Novel Approach for segmentation of Cardiac Magnetic Resonance Images[J].Chinese Journal of Computer,2007,30(1):129-136.
[11]Bing Li,Acton S.T.,Active Contour External Force Using Vector Field Convolution for Image Segmentation[J].IEEE Trans.Image Process.,16(8):2096-2106.
[12]Ranganth S.,Contour extraction from cardiac MRI studies using snake[J].IEEE Trans.Image Process.,1995,14(2):328-338.
[13]Makowski P.,Sangild T.,Therkildsen S.,Two-phase active contour method for semiautomatic segmentation of the heart and blood vessels from MRI images for 3D visualization[J].Computerized Medical Imaging and Graphics,2002,26(1): 9-17.
[14]Chakraborty A.,Staib L.H.,Duncan J.S.,Deformable boundary finding in medical images by integrating gradient and region information[J].IEEE Trans.Image Process.,1996,15(6):859-870.
[15]Osher S.J.,Sethian J.A.,Fronts propagation with curvature dependent speed:Algorithms based on Hamilton-Jacobi formulations[J].Journal of Computational Physics,1988,79(1):12-49.
[16]Sethian J.A.,Fast Marching Methods and Level Set Methods for Propagating Interfaces.
[17]Sethian J.A.,A Fast marching level set method for monotonically advancing fronts[J].Proc.Natl.Acad.Sci.USA.Applied Mathematics.1996,93:1591-1595.
[18]Adalsteinsson D.,Sethian J.A.,A fast level set method for propagating interfaces[J].Jour.Comp.Phys.1995,118(2):269-277.
[19]Caselles V.,Kimmel R.,Sapiro G.,Geodesic Active Contour[J].Int.J.Comput.Vis.1997,22(1):61-79.
[20]Paragios N.,Mellina-Gottardo O.,Ramesh V.,Gradient Vector Flow Fast Geometric Active Contours[J].IEEE Trans.Pattern Anal.Mach.Intell.,2004,26(3):402-407.
[21]Yezzi A.,Tsai J.A.,Willsky A.,A statistical approach to snakes for bimodal and trimodal imagery[C].Proceedings of ICCV,September 1999:898-903.
[22]Yu Y.,Acton S.T.,Active contours with area-weighted binary fows for segmenting low SNR imagery[C].Proceedings,IEEE International Conference on Image Processing,Barcelona,September 2003:14-17.
[23]Chan T.F.,Vese L.A.,Active contours without edge[J].IEEE Trans.Image Process,2001,10(2):266-277.
[24]Paragios N.,Deriche R.,Geodesic Active Region:A new framework to deal with frame partition problems in Computer Vision[J].Journal of Visual Communication and Image Representation,2002,13(1-2):249-268.
[25]Yunmei C,Tagare H.D.,Using Prior Shapes in Geometric Active Contours in a Variational Framework[J].Int.J.Compute.Vis.,2002,50(3):315-328.
[26]Cootes T.F.,Taylor C.J.,Cooper D.H.,et al.Active Shape Models——Their Training and Application[J].Computer Vision and Image Understanding,1995,61(1):38-59.
[27]Shang Y.,Dossel O.,Statistical 3D shape-model guided segmentation of cardiac images[J].Computer in Cardiology,2004:553-556.
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