利用局部离散度活动轮廓模型的强度非均匀图像分割

强度非均匀现象在真实图像中普遍存在,采用常规基于强度的分割算法会导致严重的误分割。针对强度非均匀图像分割,提出了基于局部离散度的活动轮廓模型分割算法。首先定义基于类内类间距离的离散度,然后利用核函数提取局部区域信息,同时加入边缘指示函数加权的轮廓线长度项能量,建立基于局部离散度的活动轮廓模型。最后引入水平集函数惩罚项,避免水平集方法在演化求解时需要不断初始化的问题。合成图像和真实图像实验结果证明本文算法性能稳定,适应于强度非均匀图像的分割。      

Improving image segmentation by using energy function based on mixture of Gaussian pre-processing

In this paper, by proposing a two-stage segmentation method based on active contour model, we improve the procedure of former image segmentation methods. The first stage of our method is computing weights, means and variances of image by utilizing Mixture of Gaussian distribution which parameters are obtained from EM-algorithm. Once they are obtained, in the second stage, by incorporating level set method for minimizing energy function, the segmentation is achieved. We use an adaptive direction function to make the curve evolution robust against the curves initial position and a nonlinear adaptive velocity to speed up the process of curve evolution and also a probability-weighted edge and region indicator function to implement a robust segmentation for objects with weak boundaries. The paper consists of minimizing a functional containing a penalty term in an attempt to maintain the signed distance property in the entire domain and an external energy term such that it achieves a minimum when the zero level set of the function is located at desired position.     

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。     

Edge location in SAR images: performance of the likelihood ratiofilter and accuracy improvement with an active contour approach

The likelihood ratio edge detector is an efficient filter for the segmentation of synthetic aperture radar (SAR) images. We show that this filter provides biased location of the edge, when the window does not have the same orientation as the edge. A phenomenological model is proposed to characterize this bias. We then introduce an efficient technique to refine edge location: the statistical active contour. The combination of these two methods permits to achieve accurate and regularized edge location.     

结合边界和区域的水平集超声图像分割算法

针对距离正则化的水平集演化（DRLSE）模型难以处理弱边缘图像、初始轮廓敏感以及曲线演化方向单一等问题,提出一种结合边缘和区域信息的变分水平集超声图像分割模型。该模型采用改进的四阶偏微分方程进行滤波,实现在去除噪声的同时保护图像边缘信息;构造了自适应加权系数,实现曲线自适应地向内或者向外演化;引入CV模型的外部能量项,将图像的边缘信息和区域信息相结合,提高了全局分割能力。实验结果表明：该方法在分割超声图像时,具有演化结果稳定,边缘定位准确的特点,可以较好地提取超声图像中的目标。     

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。     

Simultaneous vector-valued image segmentation and intensity nonuniformity correction using variational level set combined with Markov random field modeling

Object quantification requires an image segmentation to make measurements about size, material composition and morphology of the object. In vector-valued or multispectral images, each image channel has its signal characteristics and provides special information that may improve the results of image segmentation method. This paper presents a region-based active contour model for vector-valued image segmentation with a variational level set formulation. In this model, the local image intensities are characterized using Gaussian distributions with different means and variances. Furthermore, by utilizing Markov random field, the spatial correlation between neighboring pixels and voxels is modeled. With incorporation of intensity nonuniformity model, our method is able to deal with brain tissue segmentation from multispectral magnetic resonance (MR) images. Our experiments on synthetic images and multispectral cerebral MR images with different noise and bias level show the advantages of the proposed method.     

一种鲁棒的非均匀灰度图像分割算法

针对非均匀灰度图像分割困难及分割效率低下的问题,该文提出了一种基于活动轮廓模型的高效图像分割算法。不同于传统水平集方法中仅用单一信息定义的能量泛函,该算法结合图像的边缘信息和区域统计信息定义了一个新的能量泛函。边缘信息的利用便于演化轮廓线快速精确地定位至物体边缘;区域统计信息由局部统计信息和全局统计信息构成,一方面,局部统计信息的利用能够有效处理图像的灰度分布不均匀现象,另一方面,全局统计信息的利用避免了轮廓线陷入局部极小值。最后,在轮廓线演化过程中,通过高斯卷积核实现快速规则化,避免了传统模型计算代价高昂的重新初始化或规则化。合成图像和真实图像的实验结果表明,该文算法不仅能够快速有效分割灰度分布不均匀的弱边缘物体,而且对于多灰阶复杂结构物体也能够精确分割;同时,该算法对噪声和初始轮廓线具有较好的鲁棒性。     

一种自适应初始轮廓的水平集演化方法的研究

距离规则水平集存在对噪声、初始轮廓敏感、收敛速度慢以及容易从弱边缘处泄露等不稳定问题.结合待分割目标灰度统计信息和图像梯度信息,提出了一种自适应初始轮廓的水平集演化方法,利用图像信息构成的自带符号目标信息函数代替面积项中的边缘指示函数,解决水平集方法对初始轮廓敏感问题.另外,还设计一个自我调整的面积项系数解决水平集方法对收敛速度慢以及弱边缘处泄露问题.实验结果表明：本文方法不仅可以减少图像分割时间,提高了分割质量,同时能够解决对初始轮廓敏感问题.     

Automatic segmentation of coronary arteries in CT imaging in the presence of kissing vessel artifacts

In this paper, we present a novel two-step algorithm for segmentation of coronary arteries in computed tomography images based on the framework of active contours. In the proposed method, both global and local intensity information is utilized in the energy calculation. The global term is defined as a normalized cumulative distribution function, which contributes to the overall active contour energy in an adaptive fashion based on image histograms, to deform the active contour away from local stationary points. Possible outliers, such as kissing vessel artifacts, are removed in the postprocessing stage by a slice-by-slice correction scheme based on multiregion competition, where both arteries and kissing vessels are identified and tracked through the slices. The efficiency and the accuracy of the proposed technique are demonstrated on both synthetic and real datasets. The results on clinical datasets show that the method is able to extract the major branches of arteries with an average distance of 0.73 voxels to the manually delineated ground truth data. In the presence of kissing vessel artifacts, the outer surface of the entire coronary tree, extracted by the proposed algorithm, is smooth and contains fewer erroneous regions, originating in kissing vessel artifacts, as compared to the initial segmentation.     

Minimization of region-scalable fitting energy for image segmentation

Intensity inhomogeneities often occur in real-world images and may cause considerable difficulties in image segmentation. In order to overcome the difficulties caused by intensity inhomogeneities, we propose a region-based active contour model that draws upon intensity information in local regions at a controllable scale. A data fitting energy is defined in terms of a contour and two fitting functions that locally approximate the image intensities on the two sides of the contour. This energy is then incorporated into a variational level set formulation with a level set regularization term, from which a curve evolution equation is derived for energy minimization. Due to a kernel function in the data fitting term, intensity information in local regions is extracted to guide the motion of the contour, which thereby enables our model to cope with intensity inhomogeneity. In addition, the regularity of the level set function is intrinsically preserved by the level set regularization term to ensure accurate computation and avoids expensive reinitialization of the evolving level set function. Experimental results for synthetic and real images show desirable performances of our method.     

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.     

Tracking nonparameterized object contours in video

We propose a new method for contour tracking in video. The inverted distance transform of the edge map is used as an edge indicator function for contour detection. Using the concept of topographical distance, the watershed segmentation can be formulated as a minimization. This new viewpoint gives a way to combine the results of the watershed algorithm on different surfaces. In particular, our algorithm determines the contour as a combination of the current edge map and the contour, predicted from the tracking result in the previous frame. We also show that the problem of background clutter can be relaxed by taking the object motion into account. The compensation with object motion allows to detect and remove spurious edges in background. The experimental results confirm the expected advantages of the proposed method over the existing approaches.     

Weighted local variance-based edge detection and its application to vascular segmentation in magnetic resonance angiography

Accurate detection of vessel boundaries is particularly important for a precise extraction of vasculatures in magnetic resonance angiography (MRA). In this paper, we propose the use of weighted local variance (WLV)-based edge detection scheme for vessel boundary detection in MRA. The proposed method is robust against changes of intensity contrast of edges and capable of giving high detection responses on low contrast edges. These robustness and capabilities are essential for detecting the boundaries of vessels in low contrast regions of images, which can contain intensity inhomogeneity, such as bias field, interferences induced from other tissues, or fluctuation of the speed related vessel intensity. The performance of the WLV-based edge detection scheme is studied and shown to be able to return strong and consistent detection responses on low contrast edges in the experiments. The proposed edge detection scheme can be embedded naturally in the active contour models for vascular segmentation. The WLV-based vascular segmentation method is tested using MRA image volumes. It is experimentally shown that the WLV-based edge detection approach can achieve high-quality segmentation of vasculatures in MRA images.     

A Biased Vector Field Convolution External Force for Snakes

The vector field convolution (VFC) is an effective external force for active contour models. However, it always comes across premature convergence in extracting complex geometries, especially narrow and deep concavity when the initial contour is set outside of the object boundary. In this letter, a biased vector field convolution (BVFC) external force is proposed. In BVFC, an indicator function with respect to the contour and a narrow band are introduced to biasedly utilize the edges gradient information of a concave region. On the other hand, a feature map which better describes the principal curvatures and equally emphasizes both corners and edges is also introduced. Experimental results demonstrate that the BVFC snake improves the performance in extracting object boundary and shows the ability to converge to concavity compared with several state-of-art active contour models.     

Boundary detection in medical images using edge following algorithm based on intensity gradient and texture gradient features

Finding the correct boundary in noisy images is still a difficult task. This paper introduces a new edge following technique for boundary detection in noisy images. Utilization of the proposed technique is exhibited via its application to various types of medical images. Our proposed technique can detect the boundaries of objects in noisy images using the information from the intensity gradient via the vector image model and the texture gradient via the edge map. The performance and robustness of the technique have been tested to segment objects in synthetic noisy images and medical images including prostates in ultrasound images, left ventricles in cardiac magnetic resonance (MR) images, aortas in cardiovascular MR images, and knee joints in computerized tomography images. We compare the proposed segmentation technique with the active contour models (ACM), geodesic active contour models, active contours without edges, gradient vector flow snake models, and ACMs based on vector field convolution, by using the skilled doctors' opinions as the ground truths. The results show that our technique performs very well and yields better performance than the classical contour models. The proposed method is robust and applicable on various kinds of noisy images without prior knowledge of noise properties.     

基于C-V模型的红外图像自动分割方法研究

为解决红外图像分割中背景噪声及边界轮廓的影响,引入了基于曲线演化理论、水平集方法和M-S分割函数的C-V模型。通过将图像表达为分段常量函数来建立适当的能量函数模型,引入水平集的表示方法,在整个图像域中依据最小化分割寻找全局极小值,可令活动轮廓最终到达目标边缘。由MATLAB实现的仿真结果表明采用C-V模型对红外图像进行自动分割不受边界轮廓线连续性限制,对初始轮廓线位置不敏感,对图像噪声具有很强的鲁棒性,对均匀灰度目标分割效果良好。     

Adaptive Energy Weight Based Active Contour Model for Robust Medical Image Segmentation

The performance of the traditional active contour model is subject to the energy weight parameters and initial level set functions, which significantly affect the accuracy of segmentation results. This paper proposes a new robust active contour model to eliminate the above limitations for both 2D single-object and multi-object medical image segmentation. The optimal values of energy weight parameters are defined with adaptive energy weight functions to adjust the contribution of each external energy term dynamically. Thus, the energy functional will not be controlled by the large external energy terms, otherwise boundaries leakage would occur. The initial level set functions are optimized with the coarse results obtained by fuzzy C-means clustering method. The evolution of level set functions starts from the locations near the true boundaries. Therefore, the deformable curves could converge to the true boundaries robustly. The proposed algorithm is verified using both synthetic images and medical images from different modalities. The experimental results demonstrate that the proposed algorithm could realize accurate and robust segmentation for medical images even in the presence of noises and weak boundaries.     

A fuzzy energy-based active contour model with adaptive contrast constraint for local segmentation

Image segmentation is to divide an image into different parts or extract some interested objects. Active contour model and fuzzy clustering are two widely used segmentation methods, which have been integrated into an effective model in recent years. Local segmentation is often needful in medical image processing. In view of local segmentation on inhomogeneous images, a new average fuzzy energy-based active contour model is proposed in this paper, in which the total fuzzy energy integrates the approximate weighted average and arithmetic average variances of the image. And an adaptive contrast constraint condition is introduced to prevent the curve from falling into local minimum, which further improves the robustness of the segmentation model to initial contour. Experimental results on synthetic and medical images demonstrate that the proposed model has considerable improvements in terms of segmentation accuracy and robustness compared to several existing local segmentation models.     

结合局部能量与改进的符号距离正则项的图像目标分割算法

针对传统C-V模型对颜色不均匀图像分割失败并且对初始轮廓和位置敏感问题,以及现有符号距离正则项存在周期性振荡和局部极值问题。该文提出结合局部能量信息和改进的符号距离正则项的图像目标分割算法。首先,将全局图像信息扩展到HSV空间,并使用局部能量项信息分析每个像素及其领域内的统计特性,从而在较少的迭代次数内有效分割颜色分布不均匀图像。其次,改进现有符号距离正则项,改进后的符号距离正则项在避免水平集函数的重新初始化的同时,提高了计算效率,保证了水平集函数演化过程的稳定性。然后,定义阈值判断法的水平集函数演化的终止准则,使曲线准确演化到目标轮廓。该算法与同类模型的对比实验表明该模型具有较高的分割精度和对初始轮廓的鲁棒性。