基于高斯混合分布和区域竞争主动轮廓的医学目标分割

提出了一种基于高斯混合分布和区域竞争主动轮廓的医学目标提取模型。这一模型,把主动轮廓的能量函数表示为像素属于目标或背景的子类的最大概率的区域积分,在水平集合框架下使能量函数最小化,得到在高斯子类区域间竞争演化的分割迭代方程。同时,附加的速度约束项使主动轮廓在越过目标边缘时速度降低,提高了分割的收敛性。通过大量肝脏CT图像的分割实验以及与几种经典模型和手工提取的比较,表明该模型在医学图像分割中具有较好的健壮性、准确性和灵活性均较好。     

Dynamic contour: A texture approach and contour operations

The large morphometric variability in biomedical organs requires an accurate fitting method for a pregenerated contour model. We propose a physically based approach to fitting 2D shapes using texture feature vectors and contour operations that allow even automatic contour splitting. To support shrinkage of the contour and obtain a better fit for the concave parts an area force is introduced. When two parts of the active contour approach each other, it divides. The contour undergoing elastic deformation is considered as a set of masses linked by springs with their natural lengths set to zero. We also propose a method for automatic estimation of some model parameters based on a histogram of image forces along a contour.     

Snake模型综述

基于轮廓线模型的目标分割、物体跟踪方法是近十几年来图像和视频领域研究的热点.因为轮廓线模型可以将待处理问题的先验知识与各种图像处理算法有效地融合在一起,所以比以往的计算视觉理论具有更强的实用性.虽然很多模型算法已经和最初的Snakes大相径庭,但它们的指导思想却是一脉相承的.该以Snakes模型为基础,系统地介绍了轮廓线模型的发展脉络和各种最新成果.     

窄带主动轮廓模型及在医学和纹理图像局部分割中的应用

提出一种新的基于二值水平集的窄带主动轮廓模型用于局部分割.通过对二值水平集进行形态学膨胀和腐蚀操作, 提出一种稳定灵活可控的窄带控制方案,该方案可使得曲线进化精度从一个像素宽度灵活变化到任意大小. 考虑到局部分割一般要求初始轮廓置于待分割目标内部并不断膨胀进化直至目标边缘,本文提出用形态学闭运算作为新的曲线平滑方案. 与传统的高斯平滑和曲率平滑方案相比,形态学闭运算不仅能够更好地促进曲线的膨胀进化,而且有利于保持水平集函数的二值性. 此外,本文提出的方法是一种通用的自然框架,可以根据不同的需求设计不同的速度函数. 为了证明所提出的局部分割框架的有效性和鲁棒性,本文以医学图像和纹理图像为例分别设计了两个速度函数: 一个是融合了磁共振脑图像的非严格对称信息的速度函数用于大脑皮质下结构的局部分割;另一个是融合了局部熵和局部梯 度算子的速度函数用于纹理图像的局部分割. 在合成图像、医学图像和纹理图像上的实验证明了本文方法在局部分割中的有效性和鲁棒性.     

WaterBalloons: A hybrid watershed Balloon Snake segmentation

In this paper, a new image segmentation technique called WaterBalloons is introduced. It combines both watershed segmentation and the active contour model known as Balloon Snake. The watershed transform has a major problem of over-segmentation. Solutions like region merging, use of markers, use of multi-scales have been proposed. These approaches led to other problems such as under-segmentation. The Balloon Snake in an innovative approach that detects salient objects in an image. But in general snakes are very sensitive to initialization and need user interactions and a priori knowledge of objects to segment. WaterBalloons provide the advantage of reducing watershed over-segmentation problems while preventing under-segmentation and ensure automatic initialization of traditional snakes. In addition, a method for parameter optimization of the proposed hybrid snake is introduced based on energy transitions tracking.     

Attractable snakes based on the greedy algorithm for contour extraction

While most improved snakes were built under the original variational scheme, this paper presents an attractable snake based on the greedy snake (Williams and Shah, CVGIP: Image Understanding 55(1) (1992) 14-26). By use of a direct feedback mechanism that is seamlessly consistent with the search strategy of the greedy algorithm, the proposed approach is capable of inheriting the simplicity and efficiency of that algorithm and performing competitively with related snakes. To avoid undesirable local minima, an overall optimal edge detector is designed. A suitable synthetic convergent criterion is proposed which enables snakes to converge normally or oscillatingly on target objects. An adaptive interpolation scheme that encourages snakes to accurately sense the details of object shapes is also described. This model is applied to extract contours from various images with encouraging results.     

管状特性和主动轮廓的3维血管自动提取

针对血管树结构的复杂性,提出了基于管状特性和主动轮廓的3维血管的自动提取模型.该模型充分利用管状特性,包括血管的先验灰度分布、多尺度血管矢量场和血管几何曲率特征,把这些信息表示为主动轮廓模型的能量项并最小化,得到包括3个主要速度项的迭代方程:基于区域竞争和先验灰度的主动轮廓、血管矢量场和多曲率策略.基于区域竞争和先验灰度的主动轮廓可以准确健壮地提取大的血管;由Hessian矩阵主元分析得到的血管矢量场,可以驱使主动轮廓演化到细小血管内部;最小主曲率和平均曲率的多曲率策略,可以降噪平滑血管的同时,充分保持血管的几何形状.通过对肝脏、冠状动脉和肺部血管的分割,表明该模型可以自动地对整个血管树进行提取,不需要太多的预处理和后处理,是一种有效的血管自动提取模型.     

A genetically optimized level set approach to segmentation of thyroid ultrasound images

This paper presents a novel framework for thyroid ultrasound image segmentation that aims to accurately delineate thyroid nodules. This framework, named GA-VBAC incorporates a level set approach named Variable Background Active Contour model (VBAC) that utilizes variable background regions, to reduce the effects of the intensity inhomogeneity in the thyroid ultrasound images. Moreover, a parameter tuning mechanism based on Genetic Algorithms (GA) has been considered to search for the optimal VBAC parameters automatically, without requiring technical skills. Experiments were conducted over a range of ultrasound images displaying thyroid nodules. The results show that the proposed GA-VBAC framework provides an efficient, effective and highly objective system for the delineation of thyroid nodules.     

Using the Shape Gradient for Active Contour Segmentation: from the Continuous to the Discrete Formulation

A variational approach to image or video segmentation consists in defining an energy depending on local or global image characteristics, the minimum of which being reached for objects of interest. This study focuses on energies written as an integral on a domain of a function which can depend on this domain. The derivative of the energy with respect to the domain, the so-called shape derivative, is a function of a velocity field applied to the domain boundary. For a given, non-optimal domain, the velocity should be chosen such that the shape derivative is negative, thus indicating a way to deform the domain in order to decrease its energy. Minimizing the energy through an iterative deformation process is known as the active contour method. In the continuous framework, setting the velocity to the opposite of the gradient associated with the L ² inner product is a common practice. In this paper, it is noted that the negativity of the shape derivative is not preserved, in general, by the discretization of this velocity required by implementation. In order to guarantee that the negativity condition holds in the discrete framework, it is proposed to choose the velocity as a linear combination of pre-defined velocities. This approach also gives more flexibility to the active contour process by allowing to introduce some a priori knowledge about the optimal domain. Some experimental results illustrate the differences between the classical and the proposed approach.

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.     

Automated segmentation of optic disc region on retinal fundus photographs: Comparison of contour modeling and pixel classification methods

The automatic determination of the optic disc area in retinal fundus images can be useful for calculation of the cup-to-disc (CD) ratio in the glaucoma screening. We compared three different methods that employed active contour model (ACM), fuzzy c-mean (FCM) clustering, and artificial neural network (ANN) for the segmentation of the optic disc regions. The results of these methods were evaluated using new databases that included the images captured by different camera systems. The average measures of overlap between the disc regions determined by an ophthalmologist and by using the ACM (0.88 and 0.87 for two test datasets) and ANN (0.88 and 0.89) methods were slightly higher than that by using FCM (0.86 and 0.86) method. These results on the unknown datasets were comparable with those of the resubstitution test; this indicates the generalizability of these methods. The differences in the vertical diameters, which are often used for CD ratio calculation, determined by the proposed methods and based on the ophthalmologist's outlines were even smaller than those in the case of the measure of overlap. The proposed methods can be useful for automatic determination of CD ratios.     

基于GVF Snake的图像轮廓检测

主动轮廓模型(snake)是计算机视觉领域的重要研究方向,受到越来越多的研究者的青睐.由于传统snake具有不能收敛到凹形区域和收敛速度较慢等缺点,通过改进外部能量,引入了梯度矢量流主动轮廓模型(GVF Snake).实验表明,该方法能更快、更好的收敛于图像轮廓.     

基于GVF Snake的图像轮廓检测

主动轮廓模型（snake）是计算机视觉领域的重要研究方向,受到越来越多的研究者的青睐。由于传统snake具有不能收敛到凹形区域和收敛速度较慢等缺点,通过改进外部能量,引入了梯度矢量流主动轮廓模型（GVF Snake）。实验表明,该方法能更快、更好的收敛于图像轮廓。     

基于活动轮廓技术的边缘检测方法研究

通过对气囊边界检测的分析,研究了基于活动轮廓模型的边缘检测方法,并且对现有的活动轮廓模型进行了改进,完善了模型的内容,提高了其在实际应用中的准确性和强壮性。     

序列B超图像的快速轮廓提取

提出了一种序列B超图像的轮廓提取算法,该算法通过构造边缘轮廓模板来进行B超图像中感兴趣轮廓的提取,实验证明该算法能够较快速地对序列B超图像进行轮廓提取。     

一种新的肺结节检测算法

为了能够在肺癌早期或者例行体检时及时发现肺部结节灶,临床上要求能够对肺结节进行全面迅速的检测。提出了一种新的肺结节检测算法——TMH（引入了Hessian矩阵的模版匹配）算法。该算法首先获取肺实质;然后在二维层面上采用模版匹配方法（Template-Matching,TM）对结节进行检测;最后引入Hessian矩阵,有效降低了原TM算法的假阳率,实验证明该算法能够快速检测出肺结节,且假阳率较小。     

Edge Contour Extraction in MR Image Using Edgeflow Contour Model

This paper presents a new model for edge extraction of MR images, based on curve evolution and edgeflow techniques. At first the model for curve evolution is constructed, which automatically detect boundaries, and change of topology in terms of the edgeflow fields, and then the numerical approximation of the model is introduced, which is based on semi-implicit scheme to speed up the proposed approach. Finally, the numerical implementation is present and the experimental results show that the proposed model successfully extracts the edge contours, regardless of the heavy noise.     

活动轮廓模型在医学图像分割中的应用及发展

图像分割是医学三维重建、医学可视化等的基础,对疾病的诊断和治疗有着重要的临床意义,目前,用于医学图像分割的算法很多,而活动轮廓模型（Active Contour Model）的提出则是这个领域的一个重大突破。介绍活动轮廓模型从参数活动轮廓模型到几何活动轮廓模型的发展过程及发展现状,提出活动轮廓模型的研究和发展方向。     

基于鞍点搜索的粘连对象图像分割方法

针对图像中不规则形状物体接触的分割问题,提出一种采用Hessian矩阵来判断分割点（即鞍点）进行粘连对象分割的方法。根据对图像进行腐蚀操作得到的三维地貌图,结合鞍点的数学特性,运用Hessian矩阵的特征值对鞍点进行搜索定位,沿经过鞍点的最大梯度路径分割粘连对象。与目前已有的几种分割算法相比,该方法具有算法简捷、分割效果好以及易于实现的特点。