Remote sensing image registration via active contour model

Image registration is the process by which we determine a transformation that provides the most accurate match between two images. The search for the matching transformation can be automated with the use of a suitable metric, but it can be very time-consuming and tedious. In this paper, we introduce a registration algorithm that combines active contour segmentation together with mutual information. Our approach starts with a segmentation procedure. It is formed by a novel geometric active contour, which incorporates edge knowledge, namely Edgeflow, into active contour model. Two edgemap images filled with closed contours are obtained. After ruling out mismatched curves, we use mutual information (MI) as a similarity measure to register two edgemap images. Experimental results are provided to illustrate the performance of the proposed registration algorithm using both synthetic and multisensor images. Quantitative error analysis is also provided and several images are shown for subjective evaluation.     

Multigrid geometric active contour models.

Geometric active contour models are very popular partial differential equation-based tools in image analysis and computer vision. We present a new multigrid algorithm for the fast evolution of level-set-based geometric active contours and compare it with other established numerical schemes. We overcome the main bottleneck associated with most numerical implementations of geometric active contours, namely the need for very small time steps to avoid instability, by employing a very stable fully 2-D implicit-explicit time integration numerical scheme. The proposed scheme is more accurate and has improved rotational invariance properties compared with alternative split schemes, particularly when big time steps are utilized. We then apply properly designed multigrid methods to efficiently solve the occurring sparse linear system. The combined algorithm allows for the rapid evolution of the contour and convergence to its final configuration after very few iterations. Image segmentation experiments demonstrate the efficiency and accuracy of the method.     

基于主动轮廓模型的红外图像轮廓提取算法

针对主动轮廓模型存在的对初始轮廓位置敏感、凹性目标轮廓无法正确收敛等问题,本文将自适应边缘检测和主动轮廓模型相融合,提出一种改进的红外图像目标轮廓自动提取算法。首先,采用最大类间方差法计算红外图像边缘检测算法的自适应阈值,获取目标初次边缘,降低对初始轮廓位置的敏感性;对初次边缘分别进行横向与纵向填充,填充图像相与运算,对得到目标区域提取二次边缘,将其作为主动轮廓模型的初始轮廓,保证目标凹陷区域轮廓的有效收敛。最后,通过仿真分析验证了该方法能够实现红外目标轮廓的精确自动收敛。     

A fast minimal path active contour model

A new minimal path active contour model for boundary extraction is presented. Implementing the new approach requires four steps (1) users place some initial end points on or near the desired boundary through an interactive interface; (2) a potential searching window is defined between two end points; (3) a graph search method based on conic curves is used to search the boundary; and (4) a “wriggling” procedure is used to calibrate the contour and reduce sensitivity of the search results on the selected initial end points. The last three steps are performed automatically. In the proposed approach, the potential window systematically provides a new node connection for the later graph search, which is different from the row-by-row and column-by-column methods used in the classical graph search. Furthermore, this graph search also suggests ways to design a “wriggling” procedure to evolve the contour in the direction nearly perpendicular to itself by creating a list of displacement vectors in the potential window. The proposed minimal path active contour model speeds up the search and reduces the “metrication error” frequently encountered in the classical graph search methods e.g., the dynamic programming minimal path (DPMP) method     

Towards multi-stage texture-based active contour image segmentation

In this paper, we present a three-stage approach to incorporation of texture analysis into a two-dimensional active contour segmentation framework. This approach allows to utilise texture information alongside other image features. The proposed method starts with an initial unsupervised feature computation and selection, then moves to a fast contour evolution process and ends with a final refinement stage. The algorithm is designed to be general in its nature and not restricted to any particular texture feature extraction method. In this paper, the initial stage generates a set of feature maps consisting of grey-level co-occurrence matrix and Gabor features. The implementation makes an extensive use of hardware acceleration for efficient calculation of a relatively large number of features. The performance of the method was tested on various synthetic and natural images and compared with results of other algorithms.     

A multistage, optimal active contour model

Energy-minimizing active contour models or snakes can be used in many applications such as edge detection, motion tracking, image matching, computer vision, and three-dimensional (3-D) reconstruction. We present a novel snake that is superior both in accuracy and convergence speed over previous snake algorithms. High performance is achieved by using spline representation and dividing the energy-minimization process into multiple stages. The first stage is designed to optimize the convergence speed in order to allow the snake to quickly approach the minimum-energy state. The second stage is devoted to snake refinement and to local minimization of energy, thereby driving the snake to a quasiminimum-energy state. The third stage uses the Bellman (1957) optimality principle to fine-tune the snake to the global minimum-energy state. This three-stage scheme is optimized for both accuracy and speed.     

Vascular active contour for vessel tree segmentation

In this paper, a novel active contour model is proposed for vessel tree segmentation. First, we introduce a region competition-based active contour model exploiting the gaussian mixture model, which mainly segments thick vessels. Second, we define a vascular vector field to evolve the active contour along its center line into the thin and weak vessels. The vector field is derived from the eigenanalysis of the Hessian matrix of the image intensity in a multiscale framework. Finally, a dual curvature strategy, which uses a vesselness measure-dependent function selecting between a minimal principal curvature and a mean curvature criterion, is added to smoothen the surface of the vessel without changing its shape. The developed model is used to extract the liver and lung vessel tree as well as the coronary artery from high-resolution volumetric computed tomography images. Comparisons are made with several classical active contour models and manual extraction. The experiments show that our model is more accurate and robust than these classical models and is, therefore, more suited for automatic vessel tree extraction.     

An Algorithm to Compute Averages on Matrix Lie Groups

Averaging is a common way to alleviate errors and random fluctuations in measurements and to smooth out data. Averaging also provides a way to merge structured data in a smooth manner. The present paper describes an algorithm to compute averages on matrix Lie groups. In particular, we discuss the case of averaging over the special orthogonal group of matrices, the unitary group of matrices and the group of symmetric positive-definite matrices.      

An active contour model for mapping the cortex

A new active contour model for finding and mapping the outer cortex in brain images is developed. A cross-section of the brain cortex is modeled as a ribbon, and a constant speed mapping of its spine is sought. A variational formulation, an associated force balance condition, and a numerical approach are proposed to achieve this goal. The primary difference between this formulation and that of snakes is in the specification of the external force acting on the active contour. A study of the uniqueness and fidelity of solutions is made through convexity and frequency domain analyses, and a criterion for selection of the regularization coefficient is developed. Examples demonstrating the performance of this method on simulated and real data are provided.     

Auto-Regressive Moving Average Models on Complex-Valued Matrix Lie Groups

The present contribution aims at extending the classical scalar autoregressive moving average (ARMA) model to generate random (as well as deterministic) paths on complex-valued matrix Lie groups. The numerical properties of the developed ARMA model are studied by recurring to a tailored version of the Z-transform on Lie groups and to statistical indicators tailored to Lie groups, such as correlation functions on tangent bundles. The numerical behavior of the devised ARMA model is also illustrated by numerical simulations.     

Automated analysis of nerve-cell images using active contour models

The number of nerve fibers (axons) in a nerve, the axon size, and shape can all be important neuroanatomical features in understanding different aspects of nerves in the brain. However, the number of axons in a nerve is typically in the order of tens of thousands and a study of a particular aspect of the nerve often involves many nerves. Potentially meaningful studies are often prohibited by the huge number involved when manual measurements have to be employed. A method that automates the analysis of axons from electron-micrographic images is presented. It begins with a rough identification of all the axon centers by use of an elliptical Hough transform procedure. Boundaries of each axons are then extracted based on active contour model, or snakes, approach where physical properties of the axons and the given image data are used in an optimization scheme to guide the snakes to converge to axon boundaries for accurate sheath measurement. However, false axon detection is still common due to poor image quality and the presence of other irrelevant cell features, thus a conflict resolution scheme is developed to eliminate false axons to further improve the performance of detection. The developed method has been tested on a number of nerve images and its results are presented.     

A neural network-based stochastic active contour model (NNS-SNAKE)for contour finding of distinct features

Contour finding of distinct features in 2-D/3-D images is essential for image analysis and computer vision. To overcome the potential problems associated with existing contour finding algorithms, we propose a framework, called the neural network-based stochastic active contour model (NNS-SNAKE), which integrates a neural network classifier for systematic knowledge building, an active contour model (also known as the "Snake") for automated contour finding using energy functions, and the Gibbs sampler to help the snake to find the most probable contour using a stochastic decision mechanism. Successful application of the NNS-SNAKE to extraction of several types of contours on magnetic resonance (MR) images is presented.     

基于相位一致性改进的活动轮廓分割模型

为了实现甲状腺超声图像中结节组织的快速准确分割,克服图像灰度分布不均匀和边缘模糊对分割结果造成的影响,采用了基于相位一致性改进的活动轮廓分割模型。首先,利用相位一致性边缘检测原理构造一种新的速度函数,不仅弥补了梯度算子边缘检测中由于滤波处理造成边缘损坏的缺陷,而且可以灵活地控制曲线演化速率;然后,将该速度函数乘入到无边缘主动轮廓模型的能量项中,避免了线性组合中的权重分配问题,同时具有全局分割能力。通过理论分析和实验验证,改进模型的相对差异度均小于1%,运行时间均低于对比模型。结果表明,新模型实现了灰度分布不均匀图像的精确分割,同时分割效率也有所提高。     

Local morphology fitting active contour for automatic vascular segmentation

In this paper, we propose an active contour model using local morphology fitting for automatic vascular segmentation on 2-D angiogram. The vessel and background are fitted to fuzzy morphology maximum and minimum opening, separately, using linear structuring element with adaptive scale and orientation. The minimization of the energy associated with the active contour model is implemented within a level set framework. As in the current local model, fitting the image to local region information makes the model robust against the inhomogeneous background. Moreover, selective local estimations for fitting that are precomputed instead of updated in each contour evolution makes the evolution of level set robust again initial location compared to the current local model. The results on synthetic image and real angiogram compared with other methods are presented. It is shown that the proposed method can achieve automatic and accurate segmentation of vascular angiogram.     

改进边界指示函数的水平集活动轮廓模型

由于受成像原理的限制,导致超声图像对比度低、边界模糊,因此基于边界的水平集分割效果很不理想。为了提高超声图像的分割精度和分割效率,提出了一种梯度信息与区域信息相结合的水平集分割算法。首先对基于边界的距离正则化水平集演化(DRLSE)模型进行改进,将区域信息引入到边界指示函数中,并用改进后的边界指示函数代替DRLSE模型中的边界指示函数,最后,得到一个梯度与区域信息相结合的水平集演化模型。结果表明,本文中的模型能准确分割甲状腺肿瘤超声图像,且在分割效率和分割精确度方面均比DRLSE模型有所提高。     

Region-based active contour driven by global intensity fitting energy

In this paper, we present a novel region-based active contour model based on global intensity fitting energy in a variational level set framework. Meanwhile, an internal energy term is introduced, and it forces the level set function to be close to a signed distance function. Image global information utilized efficiently makes the proposed model insensitive to noise, and the introduced penalty term can avoid the costly re-initialization for the evolving level set function, which not only speeds up the contour evolvement, but also improves accuracy of the final contour. Comparisons with other classical region-based models, such as Chan-Vese model and Region-Scalable Fitting (RSF) model, show the advantages of our model in terms of efficiency and accuracy. Moreover, the model is robust to noise.     

虚拟距离窄带活动轮廓模型

针对活动轮廓模型计算量大、演化收敛缓慢、效率低下的问题,提出了一种新颖的活动轮廓模型.该模型采用虚拟的符号距离函数代替真实的符号距离函数,依靠待检测目标物内外均值来驱动活动轮廓的演化,利用虚拟距离函数的梯度形成一个窄带,活动轮廓在窄带内做简单的加减运算演化.其演化具有计算简单、分割效率高、能自由改变拓扑、全局性的优点,初始化曲线也无须非常接近待检测物体的边缘.符号距离函数重新初始化也只需在窄带内使用高斯函数规则化后,对其取符号运算即可.最后给出了活动轮廓在窄带内收敛的一个简单条件,能方便地判断待检测目标是否被检测出来.     

Image segmentation using fuzzy energy-based active contour with shape prior

This paper presents a fuzzy energy-based active contour model with shape prior for image segmentation. The paper proposes a fuzzy energy functional including a data term and a shape prior term. The data term, inspired from the region-based active contour approach proposed by Chan and Vese, evolves the contour relied on image information. The shape term inspired from Chan and Zhu’s work, defined as the distance between the evolving shape and a reference one, constrains the evolving contour with respect to the reference shape. To align the shapes, we exploit the shape normalization procedure which takes into account the affine transformation. In addition, to minimize the energy functional, we utilize a direct method to calculate the energy alterations. The proposed model therefore can deal with images with background clutter and object occlusion, improves the computational speed, and avoids difficulties associated with time step selection issue in gradient descent-based approaches.