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.     

基于水平集的医学图像分割改进算法

医学影像分割是图像分割中的难点,具有重要的应用价值。针对医学影像的特点和图像分割算法的性能差异,提出了一种水平集医学图像分割改进算法。首先通过曲线演化仿真,得出水平集算法核心-速度函数;其次选定速度函数实现对图像的粗略分割,将灰度值较大的区域设置成灰度值较小的值,通过仿真演化准确找到图像中目标区域;最后利用选定的速度函数通过初始算法,经过一定次数的迭代操作后实现了医学影像的准确分割。实验结果表明:该算法可以精确地找到肿瘤所在区域,具有较好的分割性能和一定的鲁棒性。
     

A narrow band graph partitioning method for skin lesion segmentation

Accurate skin lesion segmentation is critical for automated early skin cancer detection and diagnosis. In this paper, we present a novel multi-modal skin lesion segmentation method based on region fusion and narrow band energy graph partitioning. The proposed method can handle challenging characteristics of skin lesions, such as topological changes, weak or false edges, and asymmetry. Extensive testing demonstrated that in this method complex contours are detected correctly while topological changes of evolving curves are managed naturally. The accuracy of the method was quantified using a lesion similarity measure and lesion segmentation error ratio, Our results were validated using a large set of epiluminescence microscopy (ELM) images acquired using cross-polarization ELM and side-transillumination ELM. Our findings demonstrate that the new method can achieve improved robustness and better overall performance compared to other state-of-the-art segmentation methods.     

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.     

A novel active contour model for image segmentation using distance regularization term

In this paper, a novel active contour model (R-DRLSE model) based on level set method is proposed for image segmentation. The R-DRLSE model is a variational level set approach that utilizes the region information to find image contours by minimizing the presented energy functional. To avoid the time-consuming re-initialization step, the distance regularization term is used to penalize the deviation of the level set function from a signed distance function. The numerical implementation scheme of the model can significantly reduce the iteration number and computation time. The results of experiments performed on some synthetic and real images show that the R-DRLSE model is effective and efficient. In particular, our method has been applied to MR kidney image segmentation with desirable results.     

Human facial feature extraction for face interpretation and recognition

Facial features' extraction algorithms which can be used for automated visual interpretation and recognition of human faces are presented. Here, we can capture the contours of the eye and mouth by a deformable template model because of their analytically describable shapes. However, the shapes of the eyebrow, nostril and face are difficult to model using a deformable template. We extract them by using an active contour model (snake). In the experiments, 12 models are photographed, and the feature contours are extracted for each portrait.     

一种全局LBF主动轮廓模型

LBF模型的能量函数对于水平集函数是非凸的,从而导致应用LBF模型分割的最终结果对水平集函数的初始化非常敏感。通过凸化LBF模型的能量函数,提出一种全局的LBF模型(GLBF)。该模型针对水平集函数是凸的,从而可以通过任意初始化水平集函数得到全局最优解。此外,该模型不必重新初始化水平集函数为符号距离函数,从而极大地提高运算效率。对灰度不均匀医学图像的分割结果表明,GLBF模型对水平集函数的初始化不敏感,优于传统的LBF模型以及目前具有代表性的LIF模型。     

一种新的Snake模型及其在癌细胞分割中的应用

提出了一种新的主动轮廓线模型——P-snake(Pokarized Snake)。该模型选取目标中心一点作为原点建立极坐标,用等夹角抽样的n个点描述目标轮廓,通过迭代在图像中找出使得能量最小的轮廓点,将P-snake应用于医学癌变细胞图像的分割中,取得了满意的效果,实验结果表明,P-snake能有效跟踪目标轮廓,具有较强的抗噪能力。     

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.     

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。     

Segmentation of medical image sequence under constraints: application to non-invasive assessment of pulmonary arterial hypertension

In this article, we propose a new segmentation model including geometric constraints, namely interpolation conditions, to detect objects in a given image sequence. We propose to apply the deformable models to an explicit function to avoid the problem of parameterization (see Gout, C. and Vieira-Testé, S. (2003). An algorithm for segmentation under interpolation conditions using deformable models. Int. J. Comput. Math., 80(1), 47–54.). A problem of energy minimization on a closed subspace of a Hilbert space is defined, and introducing Lagrange multipliers enables us to formulate the corresponding variational problem with interpolation conditions. We apply this method in order to ouline the cross-sectional area (CSA) of a great thoracic vessel, namely the main pulmonary artery, in order to non-invasively assess pulmonary arterial hypertension (see Laffon, E., Vallet, C., Bernard, V., Montaudon, M., Ducassou, D., Laurent, F. and Marthan, R. (2003). A computed method for non-invasive MRI assessment of pulmonary arterial hypertension. J. Appl. Physiol. (in press); Laffon, E., Laurent, F., Bernard, V., De Boucaud, L., Ducassou, D. and Marthan, R. (2001). Noninvasive assessment of pulmonary arterial hypertension by MR phase-mapping method. J. Appl. Physiol., 90(6), 2197–2202; Laffon, E., Bernard, V., Montaudon, M., Marthan, R., Barat, J. L. and Laurent, F. (2001). Tuning of pulmonary arterial circulation evidenced by MR phase mapping in healthy volunteers. J. Appl. Physiol., 90(2), 469–474, for more details).     

基于先验形状信息的水平集图像分割

针对现有水平集方法对于具有强噪声或弱边界的目标进行分割时存在的问题,提出了一种基于形状先验的图像分割方法.该模型采用变分水平集方法,融合了区域特征和边界轮廓特征,并通过相似性匹配选择最佳先验形状.该模型不仅对具有强噪声和弱边界的复杂图像具有较好的分割效果,而且有效地解决了曲线演化的初始轮廓的确定问题.与传统方法进行对比实验,结果表明,该方法具有较好的分割效果和较高的准确率.     

结合均值聚类的窄带水平集曲线演化

水平集方法是目前常用的一种图像分割方法,但它在构造速度函数时仅使用了图像的梯度信息,对于MRI这类含有强噪音、弱边界等现象的图像很难取得理想的分割结果.针对这一问题,将图像的区域信息和梯度信息相结合,构造新的基于K-均值聚类的水平集速度函数,该速度函数有较强的抗噪性能,并且能够处理含有弱边界、低对比度的图像.对左心室MR图像的分割实验表明该方法具有良好的分割效果.     

主动模型研究

首先对主动视觉理论及主动模型的发展作了简单的回顾,然后阐述了主动模型的研究、方法及应用,从几何表示的角度把主动模型分为参数主动模型、几何主动模型及离散主动模型等三类,介绍了各类模型中有代表性的方法。     

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.     

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。     

基于可变模型的腹主动脉瘤分割算法

提出了一种基于可变模型的腹主动脉瘤分割算法。它利用相邻两个切片间的相关性以及医学先验知识,可以快速地分割腹主动脉瘤CTA图像序列,并且结果与专家人工分割结果接近。本文将像素点分为三类：边界内,边界点,边界外,这样就直接决定了模型形变方向,大大提高了分割效率。     

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。     

An Active Contour Model for Selective Segmentation

In this paper, we develop a new active contour model for image selective segmentation. The model adopts cascade anisotropic diffusion prcprocessing and a selective term in level set function. Cascade anisotropic diffusion filtering is powerful and tlexible to enhance image for various segmentation tasks. The selective term in level set function can evolve a single curve to capture a selective segmentation region which we are interested in. This is useful for intentional segmentatioa tasks. We ,：an al, so realize the multi-region segmentation by varying selecting term conditions. Furthermore, we obviously speed the process of the new algorithm by using AOS scheme in cascade anisotropic diffusion filtering aad discarding mean curvature motiou in level set function. We illustrate the performance of our segmentation method on images generated by different modalities.