一种新的心脏核磁共振图像分割方法

心脏核磁共振图像分割一直是医学影像分析领域的研究热点和难点,文中提出了一种基于梯度矢量流Snake模型的左心室分割方法.作为对梯度矢量流(GVF)的改进,提出了退化最小曲面梯度矢量流(dmsGVF).该模型对弱边界泄漏有更好的鲁棒性;挖掘了左心室的形状特点,采用相应的形状约束,克服了由于图像灰度不均而导致的局部极小,也大大减弱了分割结果对初始轮廓的依赖;对于左室壁外膜的分割,挖掘了左室壁内、外膜的位置关系,通过重新组合梯度分量来构造新的外力场.这种外力场能够克服原始梯度矢量流的不足,使得室壁外膜边缘很弱时也能得到保持,以左室壁内膜分割结果作为初始化能够自动地分割出左室壁外膜.实验结果表明,该方法能高效准确地同时分割左室壁内、外膜.  

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。  

形状约束的三维人脸组件形变模型

传统的三维人脸形变模型是通过对大量的三维人脸数据进行学习，构建描述人脸三维形状和纹理的参数模型，通过模型优化完成对二维人脸图像的三维重构。但是，实际中大量的训练样本是很难获得的，这导致形变模型描述能力的不完善，制约了它的应用。如将整个人脸看成由若干个组件组合而成，则在样本数不变的情况下降低了描述空间的维数，提高了模型的描述能力。但是在重构人脸图像时必须解决组件间三维空间的重叠合并，并且随着组件数目的增加，模型参数也随之增加，所以对优化算法也提出了更高的要求。为了解决形变模型的这些困难，提出了一种全局模型和组件模型的折中算法，即在形状上保持全局约束而纹理上进行组件匹配，从而在算法性能和算法复杂度之间获得了一个有效的平衡。  

基于圆形保持水平集方法的虹膜分割研究

虹膜分割是虹膜识别系统中最重要的环节，其分割的好坏将影响虹膜识别的准确率，而虹膜识别也是最可靠的人体生物终身身份标志之一。因此，提出了基于水平集算法的虹膜分割算法。此算法是利用水平集隐式特点与圆形形状方程显式的特点相融合确保了演化曲线在演化过程中仍保持圆形，利用其思想分割内边缘。引入自适应面积项到形状约束的CV模型中用来约束外边缘。实验结果表明，尽管眼睛睁开有限、眼镜和睫毛及眼睑等遮挡以及成像设备形成图像的角度等问题，此模型仍能取得很好的分割效果。选用区域相互重叠度——DICE作为分割算法的评价指标，由实验数据可知，提出的算法对虹膜分割是有效的。  

Mammographic mass segmentation: Embedding multiple features in vector-valued level set in ambiguous regions

Mammographic mass segmentation plays an important role in computer-aided diagnosis systems. It is very challenging because masses are always of low contrast with ambiguous margins, connected with the normal tissues, and of various scales and complex shapes. To effectively detect true boundaries of mass regions, we propose a feature embedded vector-valued contour-based level set method with relaxed shape constraint.In particular, we initially use the contour-based level set method to obtain the initial boundaries on the smoothed mammogram as the shape constraint. To prevent the contour leaking and meanwhile preserve the radiative characteristics of specific malignant masses, afterward, we relax the obtained shape constraint by analyzing possible valid regions around the initial boundaries. The relaxed shape constraint is then used to design a novel stopping function for subsequent vector-valued level set method. Since texture maps, gradient maps, and the original intensity map can reflect different characteristics of the mammogram, we integrate them together to obtain more accurate segmentation by incorporating the new stopping function into the newly proposed feature embedded vector-valued contour-based level set method.The experimental results suggest that the proposed feature embedded vector-valued contour-based level set method with relaxed shape constraint can effectively find ambiguous margins of the mass regions. Comparing against existing active contours methods, the new scheme is more effective and robust in detecting complex masses.  

Semiparametric regression with shape-constrained penalized splines

In semiparametric regression models, penalized splines can be used to describe complex, non-linear relationships between the mean response and covariates. In some applications it is desirable to restrict the shape of the splines so as to enforce properties such as monotonicity or convexity on regression functions. We describe a method for imposing such shape constraints on penalized splines within a linear mixed model framework. We employ Markov chain Monte Carlo (MCMC) methods for model fitting, using a truncated prior distribution to impose the requisite shape restrictions. We develop a computationally efficient MCMC sampler by using a correspondingly truncated multivariate normal proposal distribution, which is a restricted version of the approximate sampling distribution of the model parameters in an unconstrained version of the model. We also describe a cheap approximation to this methodology that can be applied for shape-constrained scatterplot smoothing. Our methods are illustrated through two applications, the first involving the length of dugongs and the second concerned with growth curves for sitka spruce trees.  

利用形状估计的人脸特征点定位算法*

针对鲁棒级联姿势回归算法（RCPR，Robust Cascaded Pose Regression）缺乏形状约束，对复杂人脸图像和遮挡的定位精度差、成功率低的问题，提出一种利用形状估计的分块特征点定位算法。为提高定位成功率和准确度，对人脸特征点进行分块，对每一块进行形状估计作为约束；为保证形状估计的精度和连续性，在传统核回归的基础上，学习得到图像特征与目标形状间的联合概率分布函数，称作匹配函数，并求取最大值作为形状估计；为提高算法性能，只需对部分点的位置进行回归，减少了回归器的数量，并引入了形状索引特征的采样先验。实验表明，本文算法对复杂人脸图像和遮挡具有更高的定位准确度和鲁棒性，定位成功率可达86％，同时计算速度可以实现实时处理。