结合全局信息的局部图像灰度拟合模型

针对局部图像拟合（LIF）模型对初始轮廓大小、形状和位置敏感的问题,提出一个结合全局信息的局部图像灰度拟合模型。首先,构造了一个基于全局图像信息的全局项;其次,将该全局项与LIF模型中的局部项线性组合;最后,得到了一个以偏微分方程形式存在的图像分割模型。数值实现采用有限差分法,同时采用高斯滤波器正则化水平集函数以确保水平集函数的光滑作用。在分割实验中,当选取不同的初始轮廓时,该模型均能得到正确的分割结果,且分割时间仅为LIF模型的20%到50%。实验结果表明,所提模型既对演化曲线初始轮廓的大小、形状和位置都不敏感,又能够有效地分割灰度不均图像,且分割速度较快。此外,在无初始轮廓的情形下,该模型能快速分割一些真实图像和人造图像。     

pSnakes: A new radial active contour model and its application in the segmentation of the left ventricle from echocardiographic images

Active contours are image segmentation methods that minimize the total energy of the contour to be segmented. Among the active contour methods, the radial methods have lower computational complexity and can be applied in real time. This work aims to present a new radial active contour technique, called pSnakes, using the 1D Hilbert transform as external energy. The pSnakes method is based on the fact that the beams in ultrasound equipment diverge from a single point of the probe, thus enabling the use of polar coordinates in the segmentation. The control points or nodes of the active contour are obtained in pairs and are called twin nodes. The internal energies as well as the external one, Hilbertian energy, are redefined. The results showed that pSnakes can be used in image segmentation of short-axis echocardiogram images and that they were effective in image segmentation of the left ventricle. The echo-cardiologist's golden standard showed that the pSnakes was the best method when compared with other methods. The main contributions of this work are the use of pSnakes and Hilbertian energy, as the external energy, in image segmentation. The Hilbertian energy is calculated by the 1D Hilbert transform. Compared with traditional methods, the pSnakes method is more suitable for ultrasound images because it is not affected by variations in image contrast, such as noise. The experimental results obtained by the left ventricle segmentation of echocardiographic images demonstrated the advantages of the proposed model. The results presented in this paper are justified due to an improved performance of the Hilbert energy in the presence of speckle noise.     

A disk expansion segmentation method for ultrasonic breast lesions

Automatically extracting lesion boundaries in ultrasound images is difficult due to the variance in shape and interference from speckle noise. An effective scheme of removing speckle noise can facilitate the segmentation of ultrasonic breast lesions, which can be performed with an iterative disk expansion method. In this study, a disk expansion segmentation method is proposed to semi-automatically find lesion contours in ultrasonic breast image. To evaluate the performance of the proposed method, the simulations with seven types of cysts, three in vitro phantom images and 10 clinical breast images are introduced. The mean normalized true positive area overlap between simulated contours and contours obtained by the proposed method is over 85% in simulation results. A strong correlation exists between physicians’ manual delineations and detected contours in clinical breast images. In addition, the method is also verified to be able to simultaneously contour multiple lesions in a single image. In comparison with the conventional active contour model, our proposed method does not require any initial seed within a lesion and thus, it is more convenient and applicable.     

基于角点检测的建筑物轮廓矢量化方法

针对高分辨率航空影像的特点,提出了基于角点检测的建筑物轮廓矢量化方法:首先,对航空影像进行多尺度面积形态学分割,获取建筑物的二值图像;其次,利用边缘跟踪技术,记录边缘点,在边界曲线上利用高斯函数计算其曲率,提取候选角点集,并由自适应支持区域确定的角点角度和一个动态曲率阔值代替固定的阈值筛选出正确角点;最后,以直角作为约...     

多级水脊线算法在二维医学超声图像中的应用

以肝囊肿超声图像为例,介绍一种新的医学超声图像的分割方法：多级水脊线分割算法在医学二维超声图像分割中的应用。由于斑点噪声和超声衰减产生的假象,二维和三维超声成像中的图像分割问题一直是公认的一大难点。本文采用由若干数学形态学辅助的水脊线方法提取超声图像中的肝囊肿边缘,取得了相当令人满意的结果,这为进一步的图像处理和化疗等治疗手段奠定了必要的基础。该方法自动选择并分割图像中的主要目标对象,从而克服了传统水脊线算法中常见的过分割和改进水脊线算法中半自动的缺陷。     

Segmentation of blurred objects by classification of isolabel contours

Segmentation of objects with blurred boundaries is an important and challenging problem, especially in the field of medical image analysis. A new approach to segmentation of homogeneous blurred objects in grayscale images is described in this paper. The proposed algorithm is based on building of an isolabel-contour map of the image and classification of closed isolabel contours by the SVM. Each closed isolabel contour is described by the feature vector that can include intensity-based features of the image area enclosed by the contour, as well as geometrical features of the contour shape. The image labeling procedure for construction of the training base becomes very fast and convenient because it is reduced to clicking on isolabel contours delineating the objects of interest on the isolabel-contour map. The proposed algorithm was applied to the problem of brain lesion segmentation in MRI and demonstrated performance figures above 98% on real data, both in sensitivity and in specificity.     

Dynamic programming for detecting, tracking, and matchingdeformable contours

The problem of segmenting an image into separate regions and tracking them over time is one of the most significant problems in vision. Terzopoulos et al. (1987) proposed an approach to detect the contour regions of complex shapes, assuming a user selected initial contour not very far from the desired solution. We propose to further explore the information provided by the user's selected points and apply an optimal method to detect contours which allows a segmentation of the image. The method is based on dynamic programming (DP), and applies to a wide variety of shapes. It is exact and not iterative. We also consider a multiscale approach capable of speeding up the algorithm by a factor of 20, although at the expense of losing the guaranteed optimality characteristic. The problem of tracking and matching these contours is addressed. For tracking, the final contour obtained at one frame is sampled and used as initial points for the next frame. Then, the same DP process is applied. For matching, a novel strategy is proposed where the solution is a smooth displacement field in which unmatched regions are allowed while cross vectors are not. The algorithm is again based on DP and the optimal solution is guaranteed. We have demonstrated the algorithms on natural objects in a large spectrum of applications, including interactive segmentation and automatic tracking of the regions of interest in medical images     

基于轮廓与特征点的医学图像弹性配准方法

针对单一特征引导图像配准的准确度有限性,提出了一种同时使用轮廓与特征点的医学图像弹性配准方法。半自动的特征点提取方法既可以保证提取的精确性又能够避免繁琐的特征点对应关系建立过程。对于提取的轮廓,在保证外形的基础之上,通过轮廓直线化操作减少提取轮廓中关键点的数量,以提高计算效率。以两幅待配准图像中的特征点对间距离与轮廓对间距离累加和作为图像配准测度函数,选择ICP算法框架迭代地求解最优配准变换函数。通过与其他测度函数进行比较和真实图像实验结果对比,其结果表明,该算法由于采用轮廓与特征点同时引导图像配准,其配准效果好于单独使用特征点或者轮廓的图像配准算法。该算法既能匹配图像的整体结构信息(轮廓)又能对齐图像中感兴趣的生理解剖位置(特征点),更加准确地反映图像间差异情况,是一种快速、精确的医学图像配准方法。     

局部区域拟合的快速水平集图像分割方法

目的 由于灰度不均匀图像在不同目标区域的灰度分布存在严重的重叠,对其进行分割仍然是一个难题;同时,图像中的噪声严重降低了图像分割的准确性。因此,传统水平集方法无法鲁棒、精确、快速地对具有灰度不均匀性和噪声的图像进行分割。针对这一问题,提出一种基于局部区域信息的快速水平集图像分割方法。方法 灰度不均匀图像通常被描述为一个分段常数图像乘以一个缓慢变化的偏移场。首先,通过一个经过微调的多尺度均值滤波器来估计图像的偏移场,并对图像进行预处理以减轻图像的不均匀性;然后,利用基于偏移场校正的方法和基于局部区域信息拟合的方法分别构建能量项,并利用演化曲线轮廓内外图像灰度分布的重叠程度,构建权重函数自适应调整两个能量项之间的权重;最后,引入全方差规则项对水平集进行约束,增强了数值计算的稳定性和对噪声的鲁棒性,并通过加性算子分裂策略实现水平集快速演化。结果 在具有不同灰度不均匀性和噪声图像上的分割结果表明,所提方法不但对初始轮廓的位置、灰度不均匀性和各种噪声具有较强的鲁棒性,而且具有高达94.5%的分割精度和较高的分割效率,与传统水平集方法相比分割精度至少提高了20.6%,分割效率是LIC（local intensity clustering）模型的9倍;结论 本文提出一种基于局部区域信息的快速水平集图像分割方法。实验结果表明,与传统水平集方法相比具有较高的分割精度和分割效率,可以很好地应用于具有灰度不均匀和噪声的医学、红外和自然图像等的分割。     

改进K-means活动轮廓模型

目的 通过对C-V模型能量泛函的Euler-Lagrange方程进行变形,建立其与K-means方法的等价关系,提出一种新的基于水平集函数的改进K-means活动轮廓模型。方法 该模型包含局部自适应权重矩阵函数,它根据像素点所在邻域的局部统计信息自适应地确定各个像素点的分割阈值,排除灰度非同质对分割目标的影响,进而实现对灰度非同质图像的精确分割。结果 通过分析对合成以及自然图像的分割结果,与传统及最新经典的活动轮廓模型相比,新模型不仅能较准确地分割灰度非同质图像,而且降低了对初始曲线选取的敏感度。结论 提出了包含权重矩阵函数的新活动轮廓模型,根据分割目的和分割图像性质,制定不同的权重函数,该模型具有广泛的适用性。文中给出的一种具有局部统计特性的权重函数,对灰度非同质图像的效果较好,且对初始曲线位置具有稳定性。     

An active contour model driven by anisotropic region fitting energy for image segmentation

A novel region active contour model (ACM) for image segmentation is proposed in this paper. In order to perform an accurate segmentation of images with non-homogeneous intensity, the original region fitting energy in the general region-based ACMs is improved by an anisotropic region fitting energy to evolve the contour. Using the local image information described by the structure tensor, this new region fitting energy is defined in terms of two anisotropic fitting functions that approximate the image intensity along the principal directions of variation of the intensity. Therefore, the anisotropic fitting functions extract intensity information more precisely, which enable our model to cope with the boundaries with low-contrast and complicated structures. It is incorporated into a variational formula with a total variation (TV) regularization term with respect to level set function, from which the segmentation process is performed by minimizing this variational energy functional. Experiments on the vessel and brain magnetic resonance images demonstrate the advantages of the proposed method over Chan–Vese (CV) active contours and local binary active contours (LBF) in terms of both efficiency and accuracy.     

Object density-based image segmentation and its applications in biomedical image analysis

In many applications of medical image analysis, the density of an object is the most important feature for isolating an area of interest (image segmentation). In this research, an object density-based image segmentation methodology is developed, which incorporates intensity-based, edge-based and texture-based segmentation techniques. The proposed method consists of three main stages: preprocessing, object segmentation and final segmentation. Image enhancement, noise reduction and layer-of-interest extraction are several subtasks of preprocessing. Object segmentation utilizes a marker-controlled watershed technique to identify each object of interest (OI) from the background. A marker estimation method is proposed to minimize over-segmentation resulting from the watershed algorithm. Object segmentation provides an accurate density estimation of OI which is used to guide the subsequent segmentation steps. The final stage converts the distribution of OI into textural energy by using fractal dimension analysis. An energy-driven active contour procedure is designed to delineate the area with desired object density. Experimental results show that the proposed method is 98% accurate in segmenting synthetic images. Segmentation of microscopic images and ultrasound images shows the potential utility of the proposed method in different applications of medical image processing.     

结合全局与局部信息活动轮廓的非同质图像分割

目的 通过对现有基于区域的活动轮廓模型能量泛函的Euler-Lagrange方程进行变形,建立其与K-means方法的等价关系,提出一种新的基于K-means活动轮廓模型,该模型能有效分割灰度非同质图像。方法 结合图像全局和局部信息,根据交互熵的特性,提出新的局部自适应权重,它根据像素点所在邻域的局部统计信息自适应地确定各个像素点的分割阈值,排除灰度非同质分割目标的影响。结果 采用Jaccard相似系数-JS(Jaccard similarity)和Dice相似系数-DSC(Dice similarity coefficient)两个指标对自然以及合成图像的分割结果进行定量分析,与传统及最新经典的活动轮廓模型相比,新模型JS和DSC的值最接近1,且迭代次数不多于50次。提出的模型具有较高的计算效率和准确率。结论 通过大量实验发现,新模型结合图像全局和局部信息,利用交互熵特性得到自适应权重,对初始曲线位置具有稳定性,且对灰度非同质图像具有较好地分割效果。本文算法主要适用于分割含有噪声及灰度非同质的医学图像,而且分割结果对初始轮廓具有鲁棒性。     

Automated Segmentation of Left Ventricle Using Local and Global Intensity Based Active Contour and Dynamic Programming

The aim of this work is to develop an improved region based active contour and dynamic programming based method for accurate segmentation of left ventricle (LV) from multi-slice cine short axis cardiac magnetic resonance (MR) images. Intensity inhomogeneity and weak object boundaries present in MR images hinder the segmentation accuracy. The proposed active contour model driven by a local Gaussian distribution fitting (LGDF) energy and an auxiliary global intensity fitting energy improves the accuracy of endocardial boundary detection. The weightage of the global energy fitting term is dynamically adjusted using a spatially varying weight function. Dynamic programming scheme proposed for the segmentation of epicardium considers the myocardium probability map and a distance weighted edge map in the cost matrix. Radial distance weighted technique and conical geometry are employed for segmenting the basal slices with left ventricle outflow tract (LVOT) and most apical slices. The proposed method is validated on a public dataset comprising 45 subjects from medical image computing and computer assisted interventions (MICCAI) 2009 segmentation challenge. The average percentage of good endocardial and epicardial contours detected is about 99%, average perpendicular distance of the detected good contours from the manual reference contours is 1.95 mm, and the dice similarity coefficient between the detected contours and the reference contours is 0.91. Correlation coefficient and the coefficient of determination between the ejection fraction measurements from manual segmentation and the automated method are respectively 0.978 1 and 0.956 7, for LV mass these values are 0.924 9 and 0.855 4. Statistical analysis of the results reveals a good agreement between the clinical parameters determined manually and those estimated using the automated method.     

Lip contour segmentation and tracking compliant with lip-reading application constraints

We propose to use both active contours and parametric models for lip contour extraction and tracking. In the first image, jumping snakes are used to detect outer and inner contour key points. These points initialize a lip parametric model composed of several cubic curves that are appropriate to the mouth deformations. According to a combined luminance and chrominance gradient, the initial model is optimized and precisely locked onto the lip contours. On subsequent images, the segmentation is based on the mouth bounding box and key point tracking. Quantitative and qualitative evaluations show the effectiveness of the algorithm for lip-reading applications.     

融合区域和测地线的活动轮廓模型与图割相结合的自然图像分割

针对活动轮廓模型利用水平集函数演化来分割图像时,只能分割灰度均匀的图像 问题以及容易陷入能量泛函局部极小值的缺点,提出一种新的图像分割模型。模型将区域中的 局部和全局信息融合的活动轮廓模型与边界模型相结合,然后利用图切割进行优化。实验表明, 该方法对初始曲线不敏感,能分割灰度不均的自然图像,避免陷入局部极小,并能有效提高图 像分割的速度和精度。     

融合凹点检测与仿射变换的活动轮廓模型

目的 针对基于矢量场的活动轮廓模型,如经典的梯度矢量流（GVF）模型、矢量场卷积（VFC）模型等,在提取凹形物体时矢量场常出现平衡点,不能较好地收敛到凹陷区域、尤其是深而窄的凹形及复杂凹陷区域的问题。提出一种融合凹点检测与仿射变换的活动轮廓模型。方法 首先利用活动轮廓模型进行曲线演化,得到演化后轮廓曲线上各点的坐标并求出各点的法线方向;然后基于凹点检测的方法,判断各点的凹凸性,利用梯度判断法,检测出未收敛到目标边界的凹点;其次对各凹点进行法向方向的仿射变换。在接近且不越过目标边界的情况下求出可变换的最大距离,变换后的点穿越了平衡点区域,让变换后的点代替原来的点形成新的轮廓曲线;最后为保证提取边界的精确性,将变换后的轮廓曲线再次演化并最终收敛到目标边界。结果 通过对具有凹陷区域的合成图像进行分割,计算提出模型分割结果的平均Jaccard相似系数（JS）值为95.51%,相比目前先进的GVF模型,VFC模型和自适应扩散流（ADF）模型分别提高了15.08%,12.09%和10.70%,整体效果上优于几种先进的模型。然后又对单/多目标真实图像及含噪的图像进行分割,证实提出模型分割性能的鲁棒性。结论 提出的模型有效地避免了凹形区域内的平衡点问题,可以对深凹形及复杂凹形图像进行有效分割,并且提高了分割精度。此外,该模型能融合到任何基于矢量场的活动轮廓模型中,具有广泛的普适性。     

Contour matching using epipolar geometry

Matching features computed in images is an important process in multiview image analysis. When the motion between two images is large, the matching problem becomes very difficult. In this paper, we propose a contour matching algorithm based on geometric constraints. With the assumption that the contours are obtained from images taken from a moving camera with static scenes, we apply the epipolar constraint between two sets of contours and compute the corresponding points on the contours. From the initial epipolar constraints obtained from corner point matching, candidate contours are selected according to the epipolar geometry, contour end point constraints, and contour distance measures. In order to reduce the possibility of false matches, the number of match points on a contour is also used as a selection measure. The initial epipolar constraint is refined from the matched sets of contours. The algorithm can be applied to a pair or two pairs of images. All of the processes are fully automatic and successfully implemented and tested with various real images     

Edge enhancement nucleus and cytoplast contour detector of cervical smear images.

This paper presents an edge enhancement nucleus and cytoplast contour (EENCC) detector to enable cutting the nucleus and cytoplast from a cervical smear cell image. To clean up noises from an image, this paper proposes a trim-meaning filter that can effectively remove impulse and Gaussian noises but still preserves the sharpness of object boundaries. In addition, a bigroup enhancer is proposed to make a clear-cut separation of the pixels lying in-between two objects. A mean vector difference enhancer is presented to suppress the gradients of noises and also to brighten the gradients of object contours. What is more, a relative-distance-error measure is put forward to evaluate the segmentation error between the extracted and target object contours. The experimental results show that all the aforementioned techniques proposed have performed impressively. Other than for cervical smear images, these proposed techniques can also be utilized in object segmentation of other images.     

基于改进符号压力函数的变分水平集图像分割算法

为了更好地解决含有弱边界、灰度不均匀的图像在分割时出现的轮廓线错误移动而导致分割结果错误的问题,结合图像的统计信息,构造出一种新的符号压力(SPF)函数,提出了一种基于改进的压力符号函数的变分水平集图像分割算法。首先,利用新的压力符号函数代替边缘函数,构造了新的活动轮廓模型;其次,该算法保持了测地线活动轮廓(GAC)模型和chan-vese(C-V) 模型的优点,使水平集函数演化到目标的边界上;最后,对一些弱边界、灰度不均匀的图像进行仿真实验,结果表明提出的算法能够精准地分割目标,并且具有一定的抗噪性。