一种基于主动轮廓模型的心脏核磁共振图像分割方法

提出一种基于主动轮廓模型的左室壁内、外膜分割方法.首先构造了主动轮廓模型的广义法向有偏梯度矢量流外力模型GNBGVF,作为对梯度矢量流(GVF)的改进,该外力场同时保持了切线方向和法线方向有偏的扩散,具有捕捉范围大、抗噪能力强,且在弱边界泄漏等问题上性能突出.就左室壁内膜的分割而言,考虑到左室壁的近似为圆形的特点,引入了圆形约束的能量项,有利于克服由于图像灰度不均、乳突肌等而导致的局部极小.对于左室壁外膜的分割,采用内膜的分割结果初始化,即通过重新组合梯度分量来构造外力场.该外力场能够克服原始梯度矢量流的不足,使得左室壁外膜边缘很弱时也能得到保持,可以自动、准确地分割外膜.实验结果表明,该方法能高效准确地分割左室壁内、外膜.     

一种心脏核磁共振图像左室壁内、外膜分割方法

为了充分利用心脏核磁共振图像(magnetic resonance image,简称MRI)中关于左心室的解剖和功能信息,必须先分割左室壁内、外膜.提出一种基于Snake模型的左室壁内、外膜分割方法.首先提出了Snake模型的卷积虚拟静电场外力模型CONVEF(convolutional virtual electric field),该外力场捕捉范围大、抗噪能力强、在C形凹陷区域等问题上性能突出,而且基于卷积运算,采用快速Fourier变换可以实时计算.就左室壁内膜的分割而言,考虑到左室壁的形状近似为圆形,引入基于圆形约束的能量项.对于左室壁外膜的分割,充分挖掘了左室壁内、外膜形状上的相似性和位置上的相关性,构造了形状相似性内能和一个新的边缘图,该边缘图用来计算新的外力场.基于所有这些策略并采用内膜的分割结果初始化,可以自动、准确地分割外膜.通过对一套活体心脏MR(magnetic resonance)图像进行分割并和手工分割结果和GGVF(generalized gradient vector flow) Snake模型的分割结果进行比较,结果表明该方法是有效的.     

一种基于广义梯度矢量流Snake模型的心脏MR图像分割方法

提出了一种基于广义梯度矢量流Snake模型的心脏核磁共振图像左心室内、外膜分割方法。首先构造了一种基于目标边缘的方向广义梯度矢量流(edge-based directional generalized gradient vector flow, EDGGVF) Snake模型,该模型在传统GGVF的基础上,结合目标边缘图梯度方向信息,将左心室内、外膜区分为正边缘和负边缘,从而实现左心室内外膜的全自动分割。其次,根据左心室近似为圆形的形状特点,引入了圆形能量约束,有利于克服由于图像灰度不均、乳突肌等引起的局部极小。实验结果表明,该方法可以高效准确地自动分割出左心室内、外膜。     

辅以区域力量的梯度矢量流测地线活动轮廓模型

梯度矢量流测地线活动轮廓模型作为对测地线活动轮廓模型的重要改进,不仅扩大了测地线活动轮廓模型的适用范围,而且改进了它的分割效果。但由于该模型中推动活动轮廓演化的外部力量来自于梯度矢量流,因此活动轮廓在演化过程中可能会由于弱边缘等因素的影响而陷于不希望的局部最小值。为尽量减少弱边缘对活动轮廓初始位置的限制及其对轮廓演化的不利影响,提出了一种新的辅以区域力量的梯度矢量流测地线活动轮廓模型,该模型首先将基于区域信息的力场与梯度矢量流力场相耦合,然后由以上两种力量构成的耦合力场,使活动轮廓模型能够有效地克服弱边缘的影响而收敛到所期望的边缘。实验结果表明,辅以区域力量的梯度矢量流测地线活动轮廓模型与梯度矢量流测地线活动轮廓模型相比,不仅可以更灵活地设置初始轮廓的位置,而且对弱边缘的干扰也有较好的适应性,并能有效地避免边缘泄漏。     

基于力场分析的多目标图像分割算法研究

提出基于力场分析的图像分割方法,为解决参数轮廓模型处理多对象的图像分割问题。首先分析梯度矢量流力场生成机理与力场的特点,建立真实轮廓处力场(零力场)充分条件,依据零力场充分条件生成离真实轮廓处3-5像素距离的初始轮廓线,逼近得到多对象的目标轮廓线;再引入扩散梯度矢量流力场,简化零力场识别与连接过程,降低时间复杂度。试验结果表明,方法对多目标图像分割可得到精确的结果,可以提高轮廓精确度与抗噪性,降低时间复杂度,用户可自主选择图像中感兴趣的对象,在医学图像处理、遥感测绘领域有一定应用价值。     

针对弱边缘信息的左心室图像分割算法

基于距离正则水平集模型（DRLSE）的左心室MR图像分割算法对梯度信息有很强的依赖性,在图像弱边缘区域容易陷入局部最优,且对初始轮廓的选取敏感。为降低算法对初始轮廓的敏感程度,提高其在左心室图像弱边缘的分割能力,提出一种适用于弱边缘信息的左心室分割算法。在DRLSE的基础上,该分割算法提出运用拟合方法计算基于变异系数分割模型（PSM）的新局部项,算法依靠梯度与图像局部信息驱动曲线演化,降低了DRLSE对初始轮廓的敏感度;引入形状约束力,克服DRLSE算法在左心室外膜弱边界处出现边界泄露的情况。为验证所提算法分割的准确性,基于多伦多市患病儿童医院影像科提供的数据库,利用DRLSE、保持凸性水平集模型（CPLSE）模型、U-Net网络以及提出的内膜算法对心内膜进行分割;利用DRLSE、引入外膜形状约束力的DRLSE模型（DRLSE-shape）、U-Net网络以及提出的外膜算法对心外膜进行分割。实验结果表明,针对左心室内、外膜,所提算法优于上述算法,能降低DRLSE对初始轮廓的敏感程度,提升对左心室弱边界MR图像分割的精确度。     

基于各向异性扩散活动轮廓模型的左心室MRI分割

结合各向异性扩散算法与梯度矢量流活动轮廓模型,提出了基于各向异性扩散活动轮廓模型并应用于心脏核磁共振图像分割;模型采用各向异性扩散方程构造活动轮廓模型的外部能量函数,得到边界更加清晰的分段平滑图像,运用梯度矢量流将边缘图梯度散射到平坦区域,可以有效抑制噪声,同时保持了目标边界;对左心室核磁共振图像的分割实验表明,该模型可以克服噪声和伪影的干扰,与原梯度矢量流模型相比具有更高的精确性和可靠性,有利于实现自动分割.     

基于HSV颜色模型的广义梯度矢量流图像分割方法

采用广义梯度矢量流（Generalized gradient vector flow, GGVF）作为势能函数的Snake算法在处理灰度图像分割时具有较好的性能.但当处理彩色图像时, GGVF模型往往将彩色图像转化为灰度图像进行处理,此过程造成颜色信息丢失,因而容易受到阴影等伪边界灰度变化的干扰.本文提出了基于HSV颜色模型的GGVF,充分利用了图像的色彩信息进行图像分割.实验结果表明,该方法能够有效地区分彩色图像的真实边界与由阴影产生的伪边界,并取得较好的分割结果.     

一种改进的多尺度梯度矢量流模型

梯度矢量流主动轮廓线模型是广泛应用于数字图像处理的一种目标轮廓跟踪算法,但易受干扰噪声及虚假边缘的影响,在结合多尺度图像分析的基础上,利用改进的梯度矢量流场,提出了一种改进的主动轮廓线模型。实验表明,该方法较好的限制了非目标边缘和噪声干扰的影响,提高了模型分割的精确性,具有较好的分割效果。     

基于改进的Snake模型的脑部MR图像分割方法*

为实现对脑部磁共振图像的分割,提出了一种改进Snake模型的图像分割方法。通过引入轮廓中心的概念,在贪婪Snake模型的能量函数中增加距离势能作为外部约束能量,增大了外能的吸引范围,使分割结果不依赖于初始轮廓;对各能量项进行归一化操作,并以归一化扩散方程各分量的梯度矢量流代替MR图像的梯度,提高了模型处理弱边界和深度凹陷区域的能力;对各能量函数的离散化和参数的选择进行了阐述。实验结果表明,该算法是一种有效的分割脑部MR图像的方法。     

Segmentation of the left ventricle in cardiac cine MRI using a shape-constrained snake model

Segmentation of the left ventricle (LV) is a hot topic in cardiac magnetic resonance (MR) images analysis. In this paper, we present an automatic LV myocardial boundary segmentation method using the parametric active contour model (or snake model). By convolving the gradient map of an image, a fast external force named gradient vector convolution (GVC) is presented for the snake model. A circle-based energy is incorporated into the GVC snake model to extract the endocardium. With this prior constraint, the snake contour can conquer the unexpected local minimum stemming from artifacts and papillary muscle, etc. After the endocardium is detected, the original edge map around and within the endocardium is directly set to zero. This modified edge map is used to generate a new GVC force filed, which automatically pushes the snake contour directly to the epicardium by employing the endocardium result as initialization. Meanwhile, a novel shape-similarity based energy is proposed to prevent the snake contour from being strapped in faulty edges and to preserve weak boundaries. Both qualitative and quantitative evaluations on our dataset and the publicly available database (e.g. MICCAI 2009) demonstrate the good performance of our algorithm.     

基于GVF和压力Snake模型的哑铃型目标提取

针对传统活动模型初始化曲线严格的位置选择问题和梯度矢量流场模型存在的哑铃型目标"临界点"问题,提出了梯度矢量流场构造出气球压力的活动轮廓改进模型.利用了梯度矢量流场决定形变点的压力方向,在该构造压力与图像外力及轮廓曲线内力的共同作用下模型完成目标提取.结合实例对改进模型和算法进行了试验分析,结果表明了模型的可行性和算法的有效性.     

Segmentation of cardiac tagged MR images using a snake model based on hybrid gradient vector flow

In the segmentation of cardiac tagging magnetic resonance (tMR) images, it is difficult to segment the left ventricle automatically by using the traditional segmentation model because of the interference caused by the tags. A new snake model based on hybrid gradient vector flow (HGVF) is proposed by us to improve this segmentation. Due to the different characteristics between endocardium and epicardium of the left ventricle (LV), several gradient vector flows (GVFs) with distinctive boundary information would be fused to segment these two sub regions individually. For segmentation of endocardium, we construct a new HGVF in snake model fused by three independent GVFs. These flows are respectively exported from the original cardiac tMR image, the tags-removed image and the local-filtered image. On the other hand, since the epicardium is with a nearly-circle shape, we construct the other HGVF which is composed of two different GVFs. One of them is derived from the tags-removed image either and the other one is derived from the ideal circle-shape image. Some experiments have been done to validate our new segmentation model. The average overlap of the endocardium segmentation is 89.67% (its mean absolute distance is 1.86 pixels), and the average overlap of the epicardium segmentation is 95.88% (its mean absolute distance is 1.64 pixels). Experimental results show that the proposed method improves the segmentation performance compared to some available methods effectively.     

MAC: magnetostatic active contour model

We propose an active contour model using an external force field that is based on magnetostatics and hypothesized magnetic interactions between the active contour and object boundaries. The major contribution of the method is that the interaction of its forces can greatly improve the active contour in capturing complex geometries and dealing with difficult initializations, weak edges and broken boundaries. The proposed method is shown to achieve significant improvements when compared against six well-known and state-of-the-art shape recovery methods, including the geodesic snake, the generalized version of GVF snake, the combined geodesic and GVF snake, and the charged particle model.     

Video object tracking based on improved gradient vector flow snake and intra-frame centroids tracking method

Accurately tracking the video object in video sequence is a crucial stage for video object processing which has wide applications in different fields. In this paper, a novel video object tracking algorithm based on the improved gradient vector flow (GVF) snake model and intra-frame centroids tracking algorithm is proposed. Unlike traditional gradient vector flow snake, the improved gradient vector flow snake adopts anisotropic diffusion and a four directions edge operator to solve the blurry boundary and edge shifting problem. Then the improved gradient vector flow snake is employed to extract the object contour in each frame of the video sequence. To set the initial contour of the gradient vector flow snake automatically, we design an intra-frame centroids tracking algorithm. Splitting the original video sequence into segments, for each segment, the initial contours of first two frames are set by change detection based on t-distribution significance test. Then, utilizing the redundancy between the consecutive frames, the subsequent frames’ initial contours are obtained by intra-frame motion vectors. Experimental results with several test video sequences indicate the validity and accuracy of the video object tracking.     

梯度矢量流Snake模型临界点剖析

作为经典Snake模型的一个变体,梯度矢量流(gradient vector flow,简称GVF)Snake在扩大Snake轮廓的捕捉范围和深度凹陷区域的收敛上具有卓越的性能.但GVF Snake在初始化时存在一个临界点问题:在目标内部的临界点必须在初始Snake轮廓的内部;在目标外部的临界点必须在Snake轮廓的外部.否则,Snake轮廓将不能收敛到正确的结果.对GVF Snake的临界点问题进行探讨,详细分析了临界点的影响因素,指出GVF场只有在合适的条件下才是有效的;证明了相关文献中从粘性流体力学