共查询到20条相似文献,搜索用时 15 毫秒
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Multi-Reference Shape Priors for Active Contours 总被引:1,自引:0,他引:1
Alban Foulonneau Pierre Charbonnier Fabrice Heitz 《International Journal of Computer Vision》2009,81(1):68-81
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提出了一种新颖的基于先验形状学习的混杂活动轮廓(SHAC)模型,该模型采用变分水平集方法,融合自适应区域信息与边界信息,运用主成分分析的方法从给定的含有目标物体轮廓的训练集学习得到最佳形状信息,并将其作为先验形状。将自适应区域特征和轮廓特征作为局部信息,先验形状作为全局信息,在迭代过程中结合全局和局部信息实现对演化曲线的形变进行指导和约束,达到分割目标物体的目的。通过定量和定性地分析低对比度的乳腺核磁共振图像中的乳腺轮廓的分割,以及具有复杂背景的自然图像中感兴趣区域的分割结果,验证了SHAC模型比传统活动轮廓模型具有更高的准确率,表明了该模型不仅提高了图像分割中对弱边界的识别度,减弱了非目标轮廓的干扰,而且具有良好的抗噪能力。 相似文献
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针对图像中灰度分布不均匀和弱边缘情况下已有的水平集模型不能正确分割,且现有基于先验形状的水平集模型都要利用大量样本来进行训练的不足,提出一种无需训练的血管先验形状水平集分割方法.首先通过机械应力张量的方法分析Hessian矩阵,并建立血管相似函数;然后根据血管相似函数临界值得到血管的先验形状,并用水平集符号距离隐式表达形状曲线;最后将先验血管形状模型作为约束加入到耦合最小方差和FLUX模型的能量函数中,采用变分水平集法求解能量函数的极值.由于曲线的演化不仅依赖图像的区域信息和梯度信息,还受到血管先验形状的约束,因此该模型不但能精确定位边缘,还能准确地提取出血管.实验结果表明,采用该方法分割严重灰度分布不均匀的血管造影图像,具有准确度好、精度高、鲁棒性好的优点. 相似文献
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提出了一种基于梯度向量场通量能量的水平集图像分割算法.通过加入约束符号距离函数的能量项,并极小化该能量函数得到的变分表达式主要具有4条优于传统主动轮廓模型的优点.一是可以克服分割弱边界目标的困难;二是水平集函数不但可以灵活初始化,而且可避免在演化过程中重新初始化为符号距离甬数;三是水平集函数数值化可采用简单的有限差分方法,计算效率得到了极大的提高;四是仅用一个初始轮廓就可以自动检测带孑L目标的内轮廓.对合成和真实图像的分割结果表明:对弱边界目标和灰度分布不均目标的分割效果分别优于测地线模型(GAC)和C-V主动轮廓模型. 相似文献
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一种新变分方法在图像分割中的应用 总被引:1,自引:1,他引:0
针对传统的水平集方法用于图像分割时速度较慢的现象,提出一种新的变分方法(PDE)。首先修改了CV模型的能量函数,然后用凸松弛方法将其转化为凸优化问题,并引入一个辅助变量,再采用高效和无条件稳定的AOS算法,测试实验获得了较好的分割效果。实验结果表明,所提出的变分方法(PDE)是可行有效的。 相似文献
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文章在图割理论的基础上,引入了一种新的方法将图割理论和改进的变分水平集模型结合起来,先利用图割理论对目标形成一个初始轮廓,并在得到的轮廓线上定义能量函数,通过能量函数的最小化,从而使得到的轮廓线最终收敛到目标边界,这样在保证分割精度的同时大大简化了计算量. 相似文献
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基于先验形状信息的水平集图像分割 总被引:1,自引:0,他引:1
针对现有水平集方法对于具有强噪声或弱边界的目标进行分割时存在的问题,提出了一种基于形状先验的图像分割方法.该模型采用变分水平集方法,融合了区域特征和边界轮廓特征,并通过相似性匹配选择最佳先验形状.该模型不仅对具有强噪声和弱边界的复杂图像具有较好的分割效果,而且有效地解决了曲线演化的初始轮廓的确定问题.与传统方法进行对比实验,结果表明,该方法具有较好的分割效果和较高的准确率. 相似文献
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提出一种结合超声前列腺图像的局部特征和前列腺的先验形状知识的分割方法。该方法在传统图像分割方法中引入了前列腺的先验形状约束,使得分割能够一定程度地避免由于超声图像中噪声、伪影、灰度分布不均匀等因素对前列腺分割所造成的影响。算法分为两个部分:先验形状模型的学习和先验形状约束的分割。在先验形状模型学习阶段,采用主成分分析方法对形状作特征提取,以高斯分布作为形变参数的估计;在先验形状约束分剖阶段,将基于局部高斯拟合特征的活动轮廓模型与形状模型相结合对前列腺图像分割。实验表明,所提出的方法在超声前列腺图像中取得了良好的分割效果,为临床诊断和治疗提供了定量分析的工具。 相似文献
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基于多尺度统计形状模型的Levelset分割方法 总被引:1,自引:0,他引:1
提出并建立了一种基于小波分析的多尺度统计模型,将该统计模型作为先验知识引入Mumford-Shah能量约束函数,从而指导水平集函数进行图像分割。实验表明,当对拓扑结构复杂的医学图像进行分割时,该方法具有明显的效果,同时分割速度和精度都得到了明显改善。 相似文献
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基于变宽邻域图割和活动轮廓的目标分割方法 总被引:1,自引:1,他引:1
基于图割的活动轮廓算法是一个结合图割优化工具和活动轮廓模型迭代变形思想的目标分割算法。针对算法在迭代过程中对已达目标边界的活动轮廓线所在邻域重复切割的不足,将活动轮廓线分为已达目标曲线段和未达目标曲线段,仅对未达目标曲线段进行膨胀得到可变宽度轮廓线邻域,从而减少了对邻域的切割时间。实验表明,改进算法效率提高为原来的2~3倍。 相似文献
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Daniel Cremers Stanley J. Osher Stefano Soatto 《International Journal of Computer Vision》2006,69(3):335-351
In this paper, we make two contributions to the field of level set based image segmentation. Firstly, we propose shape dissimilarity
measures on the space of level set functions which are analytically invariant under the action of certain transformation groups.
The invariance is obtained by an intrinsic registration of the evolving level set function. In contrast to existing approaches
to invariance in the level set framework, this closed-form solution removes the need to iteratively optimize explicit pose
parameters. The resulting shape gradient is more accurate in that it takes into account the effect of boundary variation on
the object’s pose.
Secondly, based on these invariant shape dissimilarity measures, we propose a statistical shape prior which allows to accurately
encode multiple fairly distinct training shapes. This prior constitutes an extension of kernel density estimators to the level
set domain. In contrast to the commonly employed Gaussian distribution, such nonparametric density estimators are suited to
model aribtrary distributions.
We demonstrate the advantages of this multi-modal shape prior applied to the segmentation and tracking of a partially occluded
walking person in a video sequence, and on the segmentation of the left ventricle in cardiac ultrasound images. We give quantitative
results on segmentation accuracy and on the dependency of segmentation results on the number of training shapes.
Electronic supplementary material Electronic supplementary material is available for this article at and accessible for authorised users. 相似文献
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In this article we develop a new method to segment high angular resolution diffusion imaging (HARDI) data. We first estimate
the orientation distribution function (ODF) using a fast and robust spherical harmonic (SH) method. Then, we use a region-based
statistical surface evolution on this image of ODFs to efficiently find coherent white matter fiber bundles. We show that
our method is appropriate to propagate through regions of fiber crossings and we show that our results outperform state-of-the-art
diffusion tensor (DT) imaging segmentation methods, inherently limited by the DT model. Results obtained on synthetic data,
on a biological phantom, on real datasets and on all 13 subjects of a public NMR database show that our method is reproducible,
automatic and brings a strong added value to diffusion MRI segmentation.
相似文献
Rachid DericheEmail: |
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Ariane Herbulot Stéphanie Jehan-Besson Stefan Duffner Michel Barlaud Gilles Aubert 《Journal of Mathematical Imaging and Vision》2006,25(3):365-386
In this paper, we propose to focus on the segmentation of vectorial features (e.g. vector fields or color intensity) using
region-based active contours. We search for a domain that minimizes a criterion based on homogeneity measures of the vectorial
features. We choose to evaluate, within each region to be segmented, the average quantity of information carried out by the
vectorial features, namely the joint entropy of vector components. We do not make any assumption on the underlying distribution
of joint probability density functions of vector components, and so we evaluate the entropy using non parametric probability
density functions. A local shape minimizer is then obtained through the evolution of a deformable domain in the direction
of the shape gradient.
The first contribution of this paper lies in the methodological approach used to differentiate such a criterion. This approach
is mainly based on shape optimization tools. The second one is the extension of this method to vectorial data. We apply this
segmentation method on color images for the segmentation of color homogeneous regions. We then focus on the segmentation of
synthetic vector fields and show interesting results where motion vector fields may be separated using both their length and
their direction. Then, optical flow is estimated in real video sequences and segmented using the proposed technique. This
leads to promising results for the segmentation of moving video objects.
Ariane Herbulot received the M. Engineering degree in computer science from the Ecole Superieure en Sciences Informatiques (ESSI), Sophia
Antipolis,France in 2001, and the M.S. degree in computer vision from the University of Nice-Sophia Antipolis (UNSA) in 2003.
She is currently a Ph.D. student in image processing with the I3S laboratory, CNRS-UNSA. Her research interests focus on nonparametric
methods for image and video segmentation.
Stéphanie Jehan-Besson received the engineering degree from Ecole Centrale Nantes and a Ph.D. in computer vision from the University of Nice Sophia
Antipolis. She is currently associate professor at ENSICAEN, engineering school of Caen. Her research interests include variational
methods for image segmentation, geometric PDEs (Partial Differential Equations), video object detection for MPEG-4/7, medical
image segmentation, motion estimation and tracking.
Stefan Duffner was born in Schorndorf, Germany in 1978. He received the Bachelor's degree in Computer Science from the University of Applied
Sciences Konstanz, Germany in 2002 and the Master's degree in Applied Computer Science from the University of Freiburg, Germany
in 2004. He's currently pursuing a Ph.D. degree in Computer Science at the Research Laboratory of France Telecom in Rennes,
France.
His research interests include machine learning, neural networks and their application to object detection and recognition
in images.
Michel Barlaud received his These d'Etat from the University of Paris XII and Agregation de Physique. He is currently a Professor of Image
Processing at the University of Nice-Sophia Antipolis, and the leader of the Image Processing group of I3S. His research topics
are: Image and Video coding using Wavelet Transform, Inverse problem using Half Quadratic Regularization and, Region Based
Image and Video Segmentation using Shape Gradient and Active Contours. He is a regular reviewer for several journals, a member
of the technical committees of several scientific conferences. He leads several national research and development projects
with French industries, and participates in several international academic collaborations: European Network of Excellence
SCHEMA and SIMILAR (Louvain La Neuve (Belgium), ITI Greece, Imperial College ...) and NSF-CNRS Funding (Universities of Stanford
and Boston). He is the author of a large number of publications in the area of image and video processing, and the Editor
of the book “Wavelets and Image Communication” Elsevier, 1994.
Gilles Aubert received the These d'Etat es-Sciences Mathematiques from the Univesity of Paris 6, France, in 1986. He is currently professor
of mathematics at the University of Nice-Sophia Antipolis and member of the J.A. Dieudonne Laboratory at Nice, France. His
research interests are calculus of variations, nonlinear partial differential equations. Fields of applications include image
processing and, in particular, restoration, segmentation, decomposition models and optical flow. 相似文献
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一种基于主动轮廓模型的心脏核磁共振图像分割方法 总被引:1,自引:0,他引:1
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. 相似文献