基于级联随机森林与活动轮廓的3D MR图像分割

针对医学磁共振（Magnetic resonance，MR）图像三维分割中随机森林（Random forest，RF）方法难以获得具有几何约束的结果以及活动轮廓模型（Active contour model，ACM）不能自动分割发生信号混叠的组织结构的问题，提出了一种整合了级联随机森林与活动轮廓模型的磁共振图像三维分割方法.该方法首先从多模态磁共振体数据中提取图像多尺度局部鲁棒统计信息，以此驱动级联随机森林对磁共振图像进行迭代的体素分类，从而获得对组织结构的初步分割结果，进一步将此结果作为初始轮廓与形状先验，整合进一个尺度可调的活动轮廓模型中，将独立的体素分类转化为轮廓曲线演化，最终得到具有几何约束的精确分割结果.在公开数据集上的实验结果表明，本文的自动化分割方法在分割精度和鲁棒性等方面，相比其他同类方法具有较大的性能提升.     

A Variational Model for Object Segmentation Using Boundary Information and Shape Prior Driven by the Mumford-Shah Functional

In this paper, we propose a new variational model to segment an object belonging to a given shape space using the active contour method, a geometric shape prior and the Mumford-Shah functional. The core of our model is an energy functional composed by three complementary terms. The first one is based on a shape model which constrains the active contour to get a shape of interest. The second term detects object boundaries from image gradients. And the third term drives globally the shape prior and the active contour towards a homogeneous intensity region. The segmentation of the object of interest is given by the minimum of our energy functional. This minimum is computed with the calculus of variations and the gradient descent method that provide a system of evolution equations solved with the well-known level set method. We also prove the existence of this minimum in the space of functions with bounded variation. Applications of the proposed model are presented on synthetic and medical images.     

Using Prior Shapes in Geometric Active Contours in a Variational Framework

In this paper, we report an active contour algorithm that is capable of using prior shapes. The energy functional of the contour is modified so that the energy depends on the image gradient as well as the prior shape. The model provides the segmentation and the transformation that maps the segmented contour to the prior shape. The active contour is able to find boundaries that are similar in shape to the prior, even when the entire boundary is not visible in the image (i.e., when the boundary has gaps). A level set formulation of the active contour is presented. The existence of the solution to the energy minimization is also established.We also report experimental results of the use of this contour on 2d synthetic images, ultrasound images and fMRI images. Classical active contours cannot be used in many of these images.     

几何活动轮廓模型中停止速度函数的尺度变换

近年来,通过水平集方法实现的几何活动轮廓模型（GAC）已成为图像处理和计算机视觉领域里十分流行的图像分割方法。几乎所有的GAC模型都依赖于停止速度函数,该函数通常是基于图像梯度定义的,其作用是使活动轮廓（演化曲线）停止在所希望的目标边界上。为了加快活动轮廓的演化速度,提出对停止速度函数进行尺度变换的方法。对4幅人工和自然图像的实验结果显示,所提出的方案能够大大减少分割时间,同时,对于凹陷边界和弱边界的分割取得了更好的效果。     

多尺度几何活动曲线及MR图像边界提取

活动曲线方法是８０年代末发展起来的基于模型的图像分割方法,主要有两大类,能量活动曲线方法和几何活动曲线方法,几何活动曲线言方法在数学上比较完备,较好地克服了能量的许多缺点,但是在医学图像分割中,尤其是结构性噪声比较严重的情况下,几何活动曲线向边界的演化会受到一定程度的影响,为了解决这个问题,作者利用基于小波变换的多尺度边缘检测算法,提出了多尺度几何活动曲线模型,在人体头部ＭＲ图像脑边界的提取中,多     

Statistical Model of Shape Moments with Active Contour Evolution for Shape Detection and Segmentation

This paper describes a novel method for shape representation and robust image segmentation. The proposed method combines two well known methodologies, namely, statistical shape models and active contours implemented in level set framework. The shape detection is achieved by maximizing a posterior function that consists of a prior shape probability model and image likelihood function conditioned on shapes. The statistical shape model is built as a result of a learning process based on nonparametric probability estimation in a PCA reduced feature space formed by the Legendre moments of training silhouette images. A greedy strategy is applied to optimize the proposed cost function by iteratively evolving an implicit active contour in the image space and subsequent constrained optimization of the evolved shape in the reduced shape feature space. Experimental results presented in the paper demonstrate that the proposed method, contrary to many other active contour segmentation methods, is highly resilient to severe random and structural noise that could be present in the data.     

Fast Global Minimization of the Active Contour/Snake Model

The active contour/snake model is one of the most successful variational models in image segmentation. It consists of evolving a contour in images toward the boundaries of objects. Its success is based on strong mathematical properties and efficient numerical schemes based on the level set method. The only drawback of this model is the existence of local minima in the active contour energy, which makes the initial guess critical to get satisfactory results. In this paper, we propose to solve this problem by determining a global minimum of the active contour model. Our approach is based on the unification of image segmentation and image denoising tasks into a global minimization framework. More precisely, we propose to unify three well-known image variational models, namely the snake model, the Rudin–Osher–Fatemi denoising model and the Mumford–Shah segmentation model. We will establish theorems with proofs to determine the existence of a global minimum of the active contour model. From a numerical point of view, we propose a new practical way to solve the active contour propagation problem toward object boundaries through a dual formulation of the minimization problem. The dual formulation, easy to implement, allows us a fast global minimization of the snake energy. It avoids the usual drawback in the level set approach that consists of initializing the active contour in a distance function and re-initializing it periodically during the evolution, which is time-consuming. We apply our segmentation algorithms on synthetic and real-world images, such as texture images and medical images, to emphasize the performances of our model compared with other segmentation models. Research supported by NIH U54RR021813, NSF DMS-0312222, NSF ACI-0321917 and NSF DMI-0327077.     

先验形状力场参数活动轮廓模型及其医学图像分割

先验形状参数活动轮廓模型是一种抗噪声干扰稳定的图像分割方法．它具有对弱边缘、凹区域进行分割的能力,同时有较大的边缘捕捉范围．通过引入一种非距离性的先验形状力场,构建一种新的能反映先验形状的参数活动轮廓模型．新的先验形状活动轮廓模型避免了曲线之间距离的计算,减少了模型的复杂性．新的方法可以较好地解决传统型参数活动轮廓模型的一些本质缺陷.实验对带噪声且为弱边缘的医学CT图像和超声图像进行分割能得到理想的边缘轮廓.     

基于颜色与纹理特征的局部主动轮廓模型

针对图像分割过程中前背景表面特征不均质问题,提出一种基于颜色纹理先验特征的多通道局部能量模型。对现有局部能量模型进行扩展,降低初始轮廓线位置对分割结果的影响,并引入8维HSV颜色模型和变换域结构张量纹理特征,实现前背景颜色特性相似的图像分割。实验结果表明,该模型具有较好的分割效果。     

基于多尺度变形模板的目标检测与识别

在分析现有模板匹配算法存在问题的基础上，提出一种基于多尺度变形模板的新方法，它在已有的Snake算法基础上，加入了形态约束，并利用小波变换的多尺度特性，使得匹配过程在由粗至精的尺度上进行，从而使运算速度大大提高，对噪声的敏感程度也相应下降，而轮廓初始化是在较粗的尺度上，利用Hausdorff距离初步匹配得到的，漏警概率较低。实验结果与理论分析相吻合，验证了算法对多类目标适用，具有速度快，精度高和对图像畸变，噪声与遮挡不敏感的优点。     

Moment-based alignment for shape prior with variational B-spline level set

This paper presents a new shape prior-based implicit active contour model for image segmentation. The paper proposes an energy functional including a data term and a shape prior term. The data term, inspired from the region-based active contour approach, evolves the contour based on the region information of the image to segment. The shape prior term, defined as the distance between the evolving shape and a reference shape, constraints the evolution of the contour with respect to the reference shape. Especially, in this paper, we present shapes via geometric moments, and utilize the shape normalization procedure, which takes into account the affine transformation, to align the evolving shape with the reference one. By this way, we could directly calculate the shape transformation, instead of solving a set of coupled partial differential equations as in the gradient descent approach. In addition, we represent the level-set function in the proposed energy functional as a linear combination of continuous basic functions expressed on a B-spline basic. This allows a fast convergence to the segmentation solution. Experiment results on synthetic, real, and medical images show that the proposed model is able to extract object boundaries even in the presence of clutter and occlusion.     

形状约束下活动轮廓模型冠脉血管图像多尺度分割

目的 由于计算机断层血管造影(CTA)图像的复杂性,临床诊断冠脉疾病往往需要经验丰富的医师对冠状动脉进行手动分割,快速、准确自动分割出冠状动脉对提高冠脉疾病诊断效率具有重要意义。针对双源CT图像特点以及传统单一基于区域或边界的活动轮廓模型的不足,研究了心脏冠脉3维分割算法,提出一种基于血管形状约束的活动轮廓模型分割方法。方法 首先,利用改进的FCM(fuzzy C-means)对心脏CT图像感兴趣区域初分割,其结果用于初始化C-V模型水平集演化曲线及控制参数,提取感兴趣区域轮廓。接着,由3维心脏图像数据获取多尺度梯度矢量信息构造边界型能量泛函,然后利用基于Hessian矩阵的多尺度血管函数对心脏感兴趣区域3维体数据增强滤波,获取血管先验形状信息用于约束能量泛函。最后融合边界、区域能量泛函并利用变分原理及水平集方法得到适合冠脉血管分割的水平集演化方程。结果 由于血管图像的灰度不均匀,血管末端区域更为细小,所以上述算法的实施是面向被划分多个子区域的血管,在缩小的范围内进行轮廓的演化。相比于传统的血管分割方法,该方法充分融合血管图像的先验信息及梯度场信息,能够从灰度及造影剂分布不均匀的冠脉血管图像中准确分割出冠状动脉,对于细小的血管结构亦能获得较好的分割效果。实验结果表明,该方法只需在给定初始轮廓前提下,有效提取3维冠脉血管。结论 对多组心脏CT图像进行分割,本文基于血管先验形状约束的活动轮廓模型可以准确分割出冠脉结构完整轮廓,并且人工交互简单。该方法在双源CT冠脉图像自动分割方面具有较好的正确率与优越性。     

Fast level set image and video segmentation using new evolution indicator operators

We propose an effective level set evolution method for robust object segmentation in real images. We construct an effective region indicator and an multiscale edge indicator, and use these two indicators to adaptively guide the evolution of the level set function. The multiscale edge indicator is defined in the gradient domain of the multiscale feature-preserving filtered image. The region indicator is built on the similarity map between image pixels and user specified interest regions, where the similarity map is computed using Gaussian Mixture Models (GMM). Then we combine these two methods to develop a new mixing edge stop function, which makes the level set method more robust to initial active contour setting, and forces the level set to evolve adaptively based on the image content. Furthermore, we apply an acceleration approach to speed up our evolution process, which yields real time segmentation performance. Finally, we extend the proposed approach to video segmentation for achieving effective target tracking results. As the results show, our approach is effective for image and video segmentation and works well to accurately detect the complex object boundaries in real-time.     

显著性驱动的局部相似拟合模型分割算法研究

灰度不均匀和噪声图像的分割是计算机视觉中的难点。现有的活动轮廓模型尽管能够取得较好的分割效果,但仍然对噪声图像分割效果不理想,初始轮廓曲线的选取敏感,优化易陷入局部极小导致演化速度慢等问题。针对该问题,首先使用局部区域灰度的均值和方差拟合高斯分布,构建新的能量泛函,均值和方差随着能量的最小化过程而变化,从而增强了灰度不均匀和噪声图像的分割能力。此外,结合视觉显著性检测算法获取待分割目标的先验形状信息,并自适应地创建水平集函数,从而降低了初始轮廓位置敏感性及计算时间复杂度,实现全自动的图像分割。实验结果证明,提出的算法可以用于灰度不均匀和噪声图像分割,并取得了较好的分割性能,消除了算法对初始轮廓位置敏感性,减少了迭代次数。     

基于改进自仿射映射系统与参数活动轮廓的医学图像分割算法

Snake模型分割图像时要求初始化轮廓线位于目标图像特征附近,且处理弱边界与深度凹陷区域的能力较弱,对此提出一种基于改进自仿射映射系统与参数活动轮廓的医学图像分割算法。首先,使用高斯滤波器对给定图像进行平滑处理并计算其小波系数;然后,在每个小波尺度的子矩阵中定义一些自仿射映射;之后,将不同小波尺度对应的子力叠加以获得自仿射力;最终,基于动态力公式引导snake模型变形。基于医学图像的仿真实验结果表明,本算法对于医学图像的分割性能较好,有效地提高了snake模型对弱边界与深度凹陷区域的处理能力。     

结合改进分水岭和GVF的三维肝脏分割方法

三维肝脏分割是当前医学图像处理的热点问题,如何准确快速地从腹部CT序列中分割出肝脏是肝部病变诊断的基础。针对传统活动轮廓模型对轮廓线敏感、运算量大的问题,改进了传统轮廓线设置方法,并把算法扩展到三维。首先,在一幅腹部CT图片中采用改进的分水岭算法,按照灰度和纹理的相似性原则从一个种子块开始生长出整个肝脏,再用其边缘作为相邻CT序列的起始轮廓,用GVF算法从序列图片中分割出肝脏,重复该过程,直至分割出整个腹部序列图像的切片,进行三维重建。     

An active contour model with shape regulation scheme

This paper presents an active method for locating target objects in images, which is aimed at improving the performance of detecting object boundaries by enhancing the behavioral characteristics of an active contour. The proposed active contour model simulates a mechanical system consisting of two main parts: the first is a rigid fixture, called the 'core', specifying the expected shape of target boundaries, while the second is an elastic rod attached to the rigid fixture. The elastic rod deforms or moves relative to the rigid core according to the classical laws of the mechanical system. When the initial contour is applied to an image data, it is attracted near the dominant image features, but tries to keep its home shape and simultaneously make the deformation smooth if a deformation is more natural for force equilibrium. This mechanism significantly improves the performance of detecting object boundaries in the presence of some disturbing image features. The active contour is scale invariant, thereby significantly relieving the difficulty in selecting proper values for the model parameters. The values for the model parameters can be selected to make the contour have the desired behaviors around the equilibrium position through the analysis of the vibration mode of the mechanical system. The performance of the proposed method is validated through a series of experiments, which include detection of heavily degraded objects, tracking of objects under non-rigid motion and comparisons with the original snake models.     

Elastic shapes models for improving segmentation of object boundaries in synthetic aperture sonar images

We present a variational framework for naturally incorporating prior shape knowledge in guidance of active contours for boundary extraction in images. This framework is especially suitable for images collected outside the visible spectrum, where boundary estimation is difficult due to low contrast, low resolution, and presence of noise and clutter. Accordingly, we illustrate this approach using the segmentation of various objects in synthetic aperture sonar (SAS) images of underwater terrains. We use elastic shape analysis of planar curves in which the shapes are considered as elements of a quotient space of an infinite dimensional, non-linear Riemannian manifold. Using geodesic paths under the elastic Riemannian metric, one computes sample mean and covariances of training shapes in each classes and derives statistical models for capturing class-specific shape variability. These models are then used as shape priors in a variational setting to solve for Bayesian estimation of desired contours as follows. In traditional active contour models curves are driven towards minimum of an energy composed of image and smoothing terms. We introduce an additional shape term based on shape models of relevant shape classes. The minimization of this total energy, using iterated gradient-based updates of curves, leads to an improved segmentation of object boundaries. This is demonstrated using a number of shape classes in two large SAS image datasets.     

结合显著性的主动轮廓图像分割

传统的主动轮廓方法无法突出分割区域的显著性,同时在由显著性检测算法所得到的显著图中目标具有较高的信噪比,因此提出结合显著性的主动轮廓图像分割。通过线性光谱聚类分割得到超像素,以超像素为处理单位利用基于图论的流形排序算法获得较好的显著图;将高斯混合模型引入到主动轮廓的曲线演化过程中,计算曲线内外的平均灰度值,从而通过高斯混合模型和显著性信息得到了新的主动轮廓能量方程,并运用水平集方法指导分割,获得最终的分割结果。实验结果表明,提出的图像分割方法可以对图像进行快速和有效的分割。     

一种文档图像多尺度分割算法

1.研究背景随着数字图书馆和电子办公应用的日益普及,面对大量纸质原始文件及资料的数字化,直接扫描、存储和传输文档的需求越来越多,为了解决扫描文档图像庞大信息量和存储容量、传输带宽之间的矛盾,迫切需要专门针对文档图像研究新的高性能压缩算法。根据扫描文挡页面的内容特征,提高压缩比最有挖掘潜力的方法是对文档图像进行图文分割后再分别