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     

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.     

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.     

Segmentation of volumetric tissue images using constrained active contour models

In this article we describe an application of active contour model for the segmentation of 3D histo-pathological images. The 3D images of a thick tissue specimen are obtained as a stack of optical sections using confocal laser beam scanning microscope (CLSM). We have applied noise reduction and feature enhancement methods so that a smooth and slowly varying potential surface is obtained for proper convergence. To increase the capture range of the potential surface, we use a combination of distance potential and the diffused gradient potential as external forces. It has been shown that the region-based information obtained from low-level segmentation can be applied to reduce the adverse influence of the neighbouring nucleus having a strong boundary feature. We have also shown that, by increasing the axial resolution of the image stack, we can automatically propagate the optimum active contour of one image slice to its neighbouring image slices as an appropriate initial model. Results on images of prostate tissue section are presented.     

Probability density difference-based active contour for ultrasound image segmentation

Because of its low signal/noise ratio, low contrast and blurry boundaries, ultrasound (US) image segmentation is a difficult task. In this paper, a novel level set-based active contour model is proposed for breast ultrasound (BUS) image segmentation. At first, an energy function is formulated according to the differences between the actual and estimated probability densities of the intensities in different regions. The actual probability densities are calculated directly. For calculating the estimated probability densities, the probability density estimation method and background knowledge are utilized. The energy function is formulated with level set approach, and a partial differential equation is derived for finding the minimum of the energy function. For performing numerical computation, the derived partial differential equation is approximated by the central difference and non-re-initialization approach. The proposed method was operated on both the synthetic images and clinical BUS images for studying its characteristics and evaluating its performance. The experimental results demonstrate that the proposed method can model the BUS images well, be robust to noise, and segment the BUS images accurately and reliably.     

一种具有拓扑自适应性的图象两步分割方法

为了准确提取出感兴趣区域的边界，研究出一种具有拓扑自适应性的图象两步分割方法，即基于棱边检测算子的B样条活动围道分割方法，该方法首先是进行图象的底层分割，即用基于图象局部特性（像元邻域）操作的棱边检测算子来检测图象的棱边点，然后进行图象的高层分割，即用基于图象全局统计特性的B样条活动围道分割方法来求取对象的准确边界，另外，还提出了基于区域欧拉数的拓扑自适应处理方案，该两图像分割方法具有人为干预少，对初始条件不敏感，拓扑自适应性强等优点，试验结果证明了该方法的有效性。     

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。     

一种心脏核磁共振图像左室壁内、外膜分割方法

为了充分利用心脏核磁共振图像(magnetic resonance image,简称MRI)中关于左心室的解剖和功能信息,必须先分割左室壁内、外膜.提出一种基于Snake模型的左室壁内、外膜分割方法.首先提出了Snake模型的卷积虚拟静电场外力模型CONVEF(convolutional virtual electric field),该外力场捕捉范围大、抗噪能力强、在C形凹陷区域等问题上性能突出,而且基于卷积运算,采用快速Fourier变换可以实时计算.就左室壁内膜的分割而言,考虑到左室壁的形状近似为圆形,引入基于圆形约束的能量项.对于左室壁外膜的分割,充分挖掘了左室壁内、外膜形状上的相似性和位置上的相关性,构造了形状相似性内能和一个新的边缘图,该边缘图用来计算新的外力场.基于所有这些策略并采用内膜的分割结果初始化,可以自动、准确地分割外膜.通过对一套活体心脏MR(magnetic resonance)图像进行分割并和手工分割结果和GGVF(generalized gradient vector flow) Snake模型的分割结果进行比较,结果表明该方法是有效的.     

结合全局概率密度差异与局部灰度拟合的超声图像分割

由于超声图像具有高噪声、低对比度、边缘模糊不清等特点, 超声图像的分割成为图像处理领域中一个难度较高、亟待解决的问题. 本文提出了一种结合全局概率密度差异与局部灰度拟合的主动轮廓模型对超声图像进行分割的方法. 该方法分别在原始超声图像与预处理图像上利用了图像的全局和局部信息. 在原始图像上, 利用各区域的灰度分布, 并结合超声图像的背景知识对图像的全局信息建模. 为了考虑图像的局部信息, 首先对图像进行预处理, 在预处理图像上, 利用局部灰度拟合模型对图像中的局部信息进行建模. 通过分别在不同图像上对全局和局部信息建模的方式, 本方法将利用Speckle噪声与去除Speckle噪声的分割思想结合在一起. 本文提出的方法分别在模拟和临床超声图像上进行了实验. 实验结果证明, 该方法对图像中的噪声具有较好的适应性, 并对初始条件不敏感, 可以准确地对超声图像进行分割.     

Unsupervised 2D gel electrophoresis image segmentation based on active contours

This work introduces a novel active contour-based scheme for unsupervised segmentation of protein spots in two-dimensional gel electrophoresis (2D-GE) images. The proposed segmentation scheme is the first to exploit the attractive properties of the active contour formulation in order to cope with crucial issues in 2D-GE image analysis, including the presence of noise, streaks, multiplets and faint spots. In addition, it is unsupervised, providing an alternate to the laborious, error-prone process of manual editing, which is required in state-of-the-art 2D-GE image analysis software packages. It is based on the formation of a spot-targeted level-set surface, as well as of morphologically-derived active contour energy terms, used to guide active contour initialization and evolution, respectively. The experimental results on real and synthetic 2D-GE images demonstrate that the proposed scheme results in more plausible spot boundaries and outperforms all commercial software packages in terms of segmentation quality.     

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.     

Adaptive active contour model driven by image data field for image segmentation with flexible initialization

In this paper, a novel adaptive active contour model based on image data field for image segmentation with robust and flexible initializations is proposed. We firstly construct a new external energy term deduced from the image data field that drives the level set function to move in the opposite direction along the boundaries of object and an adaptive length regularization term based on the image local entropy. The designed external energy and length regularization term are then incorporated into a variationlevel set framework with an additional penalizing energy term. Due to the adaptive sign–changing property of the external energy and the adaptive length regularization term, the proposed model can tackle images with clutter background and noise, the level set function can be initialized as any bounded functions (e.g., constant function), which implies the proposed model is robust to initialization of contours. Experimental results on both synthetic and real images from different modalities confirm the effectiveness and competivive performance of the proposed method compared with other representative models.

     

Contour segmentation in 2D ultrasound medical images with particle filtering

Object segmentation in medical images is an actively investigated research area. Segmentation techniques are a valuable tool in medical diagnostics for cancer tumours and cysts, for planning surgery operations and other medical treatment. In this paper, a Monte Carlo algorithm for extracting lesion contours in ultrasound medical images is proposed. An efficient multiple model particle filter for progressive contour growing (tracking) from a starting point is developed, accounting for convex, non-circular forms of delineated contour areas. The driving idea of the proposed particle filter consists in the incorporation of different image intensity inside and outside the contour into the filter likelihood function. The filter employs image intensity gradients as measurements and requires information about four manually selected points: a seed point, a starting point, arbitrarily selected on the contour, and two additional points, bounding the measurement formation area around the contour. The filter performance is studied by segmenting contours from a number of real and simulated ultrasound medical images. Accurate contour segmentation is achieved with the proposed approach in ultrasound images with a high level of speckle noise.     

一种基于主动轮廓模型的心脏核磁共振图像分割方法

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.     

基于方向气球力活动轮廓模型的图像分割

针对传统参数活动轮廓模型存在对轮廓线初始位置敏感的缺点,提出了方向气球力活动轮廓模型并应用于MRI图像分割。该模型利用底层图像分割的结果确定外力的方向,使气球力方向始终指向目标边界,引导轮廓线变形。当轮廓线运动到目标边界附近时,在高斯势力作用下继续变形,完成图像高层分割。实验结果表明,该模型与轮廓线初始位置无关,能实现MRI图像的自动分割。     

基于区域信息主动轮廓模型的图像分割

图像分割是对图像进行后续处理的关键步骤之一,传统主动轮廓模型在目标图像背景较为复杂的情况下很难精确地进行图像分割。为了精确且快速地进行图像分割,以便更加有利地进行后续相关图像处理操作,在对传统主动轮廓模型进行相关研究的基础之上,提出一种基于区域信息主动轮廓模型的图像分割方法。将图像区域信息融入主动轮廓模型的能量函数中去,减弱了模型对图像区域信息突变所造成的图像误分割;改进该模型能量函数内外曲线的拟合中心,以此减少图像噪声点对拟合中心准确性的影响;利用信息熵改进曲线内外能量函数权重,以此提高曲线的演化速度。实验结果表明,与传统CV（Chan_Vese）模型等四种模型相比,该方法所分割的图像更加精确,且在算法分割效率上具有较明显的优势。     

先验形状约束的超声前列腺图像分割方法

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。     

Unsupervised active contours driven by density distance and local fitting energy with applications to medical image segmentation

This study presents an efficient variational region-based active contour model for segmenting images without priori knowledge about the object or background. In order to handle intensity inhomogeneities and noise, we propose to integrate into the region-based local intensity model a global density distance inspired by the Bhattacharyya flow. The local term based on local information of segmented image allows the model to deal with bias field artifact, which arises in data acquisition processes. The global term, which is based on the density distance between the probability distribution functions of image intensity inside and outside the active contour, provides information for accurate segmentation, keeps the curve from spilling, and addresses noise in the image. Intensive 2D and 3D experiments on many imaging modalities of medical fields such as computed tomography, magnetic resonance imaging, and ultrasound images demonstrate the effectiveness of the model when dealing with images with blurred object boundary, intensity inhomogeneities, and noise.     

基于Snake模型的图像分割新算法

针对目前基于Snake模型的图像分割算法普遍存在噪声鲁棒性差、适用范围受限、易发生弱边缘泄露以及轮廓曲线难以收敛到细小深凹边界的缺陷,提出了一种基于Snake模型的图像分割新算法。首先,选取新的扩散项代替具有各向同性光滑作用的拉普拉斯算子;其次,引入p-拉普拉斯泛函到平滑能量项中强化法线方向外力;最后,利用边缘保护项使外力场方向与边缘方向一致,以防止弱边缘泄漏并促使轮廓线收敛到细小深凹边界。实验结果表明,所提模型不仅克服了现有基于Snake模型的图像分割算法的缺陷,具有更好的分割效果,明显提高了抗噪性能和角点定位精度,而且耗时更少,适用于噪声图像、医学图像以及含有很多弱边缘的自然图像分割。     

多描述子活动轮廓模型的医学图像分割

目的 医学图像分割结果可帮助医生进行预测、诊断及制定治疗方案。医学图像在采集过程中受多种因素影响,同一组织往往具有不同灰度,且伴有强噪声。现有的针对医学图像的分割方法,对图像的灰度分布描述不够充分,不足以为精确的分割图像信息,且抗噪性较差。为实现医学图像的精确分割,提出一种多描述子的活动轮廓（MDAC）模型。方法 首先,引入图像的熵,结合图像的局部均值和方差共同描述图像的灰度分布。其次,在贝叶斯框架下,引入灰度偏移因子,建立活动轮廓模型的能量泛函。最后,利用梯度下降流法得到水平集演化公式,演化的最后在完成分割的同时实现偏移场的矫正。结果 利用合成图像和心脏、血管和脑等医学图像进行了仿真实验。利用MDAC模型对加噪的灰度不均图像进行分割,结果显示,在完成精确分割的同时实现了纠偏。通过对比分割前后图像的灰度直方图,纠偏图像只包含对应两相的两个峰,且界限更加清晰;与经典分割算法进行对比,MDAC在视觉效果和定量分析中,分割效果最好,比LIC的分割精度提高了30%多。结论 实验结果表明,利用均值、方差和局部熵共同描述图像灰度分布,保证了算法的精度。局部熵的引入,在保证算法精度的同时,提高了算法的抗噪性。能泛中嵌入偏移因子,保证算法精确分割的同时实现偏移场纠正,进一步提高分割精度。