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。     

基于图割优化的能量最小化模型图像分割方法

针对主动轮廓模型中利用梯度下降法求解能量函数容易陷入局部极小的不足,设计了一个离散化最小能量函数模型。该模型以Chan-Vese模型为基础,利用图割方法优化能量泛函,实现能量的全局最优解。新模型首先将图像映射为图,将基于像素的能量泛函转换为可用图表示的离散化能量函数,通过计算节点及其邻域关系权值,迭代求解最小化能量并将其作用于形变轮廓曲线,直至达到稳定状态。新模型改进了主动轮廓模型对弱边界图像初始轮廓敏感的问题,提高了分割精度和运行速度。     

基于全局最小化活动轮廓的多目标检测跟踪

为了在噪声干扰以及目标和背景颜色相近情况下实现多目标跟踪,提出一种基于快速全局最小化的活动轮廓模型的目标检测跟踪算法。该算法结合了基于边缘的活动轮廓模型和基于区域的活动轮廓模型,对能量泛函进行全局最小化来检测目标活动轮廓,用卡尔曼滤波预测目标下一帧的特征信息,然后用改进的最近邻法进行多目标跟踪。对图像序列的实验结果表明该算法能有效地对运动背景下多目标进行跟踪。     

Interactively multiphase image segmentation based on variational formulation and graph cuts

In this paper the multiple piecewise constant (MPC) active contour model is extended to deal with multiphase case. This proposed multiphase model can be effectively optimized by solving the minimum cuts problem of a specially devised multilayer graph. Based on the proposed energy functional and its graph cuts optimization, an interactively multiphase partition method for image segmentation is presented. The user places some scribbles with different colors on the image according to the practical application demand and each group of scribbles with the same color corresponds to a potential image region. The distribution of each region can be learned from the input scribbles with some particular color. Then the corresponding multilayer graph can be constructed and its minimum cuts can be computed to determine the segmentation result of the image. Numerical experiments show that the proposed interactively multiphase segmentation method can accurately segment the image into different regions according to the input scribbles with different color.     

基于格式塔心理学原理的几何活动轮廓模型

基于格式塔心理学原理提出了一种几何活动轮廓模型,并将其应用于图像分割。当轮廓曲线远离目标边界时,应用格式塔心理学目标-背景原则,其能量函数主要由区域间差异性组成;当轮廓曲线位于目标边界附近时,应用格式塔心理学接近性原则,其能量函数主要由区域内一致性组成。该模型符合知觉特性,是几何活动轮廓模型的一般形式,且融合图像区域信息和边界信息。通过侧脑室和肿瘤医学图像分割实验,其结果表明,该模型对模糊边界图像的自动分割具有一定的普适性,能达到满意的分割效果。将该模型应用到多目标的免疫细胞图像分割中,能一次性完成将细胞质从细胞核和体液两种不同背景中分割出来的任务。     

基于颜色与纹理特征的局部主动轮廓模型

针对图像分割过程中前背景表面特征不均质问题,提出一种基于颜色纹理先验特征的多通道局部能量模型。对现有局部能量模型进行扩展,降低初始轮廓线位置对分割结果的影响,并引入8维HSV颜色模型和变换域结构张量纹理特征,实现前背景颜色特性相似的图像分割。实验结果表明,该模型具有较好的分割效果。     

基于区域混合活动轮廓模型的医学图像分割

针对变分水平集算法在图像分割过程中计算量较大且收敛速度慢的现象, 在一些基于区域的活动轮廓模型基础上提出了一种新的基于区域混合模型的非凸正则化活动轮廓模型。该模型构造了一个新的能量泛函,该能量泛函结合了考虑图像局部聚类性质的LBF模型和测地线模型,增加了非凸正则化项,加快了轮廓曲线的收敛速度,可以很好地保持区域形状并能防止边缘过平滑,然后通过经典有限差分法求得能量泛函的极小值。最后,在合成图像和医学图像上做了仿真实验,结果表明,该算法具有较快的收敛速度 和很好的鲁棒性,分割结果也较准确。     

C-V活动轮廓模型的一个注记

Chan-Vese提出的“无边活动轮廓”模型（C-V模型）是一个著名的基于区域的图像分割模型,它是基于Mumford-Shah泛函和二值PC函数（目标区域取一个值,背景区域取另一个值）解决图像分割问题的。在C-V模型中,定义能量泛函的面积项的系数被要求为非负值,这个要求限制了模型适用的范围。实验研究表明：面积项系数取负值时,C-V模型能够分割某些原来不适用的图像。     

Novel fuzzy active contour model with kernel metric for image segmentation

In this paper, a novel region-based fuzzy active contour model with kernel metric is proposed for a robust and stable image segmentation. This model can detect the boundaries precisely and work well with images in the presence of noise, outliers and low contrast. It segments an image into two regions – the object and the background by the minimization of a predefined energy function. Due to the kernel metric incorporated in the energy and the fuzziness of the energy, the active contour evolves very stably without the reinitialization for the level set function during the evolution. Here the fuzziness provides the model with a strong ability to reject local minima and the kernel metric is employed to construct a nonlinear version of energy function based on a level set framework. This new fuzzy and nonlinear version of energy function makes the updating of region centers more robust against the noise and outliers in an image. Theoretical analysis and experimental results show that the proposed model achieves a much better balance between accuracy and efficiency compared with other active contour models.     

深度图辅助的主动轮廓分割算法

针对由前后背景相似而导致从图像中很难分割出精确图像的问题,以主动轮廓分割方法为基础,结合深度图为辅助,提出了一种新的深度图辅助的基于区域的主动轮廓图像分割方法。首先,使用滤波算法对深度图进行修补,得到较为完善的深度图;然后使用混合高斯模型计算得到彩色图和深度图的置信图;最后使用置信图,计算给定区域中颜色与深度的权重,从而指导分割过程。提出的算法能够正确使用彩色信息和深度信息来指导分割,更准确地实现前后背景的分离。实验结果表明,该方法得到的分割结果较为接近真实情况,提高了图像分割的准确率。     

区域信息和水平集方法的图像分割

随着图像处理技术不断发展,图像分割技术也在不断的走向成熟,但是目前比较成熟的分割方法都存在一定的局限性,传统的分割方法一般都难以实现全局分割,而且对目标边缘比较模糊的物体难以实现有效的精确的分割;基于区域信息和水平集方法的图像分割算法弥补了这些缺陷,该算法是在传统的动态轮廓GAC模型和C_V模型的基础上进行改善;通过实验分析,首先,该算法极大提高了图像分割的精确性,使得轮廓线能够在要分割目标的边缘附近停止演化,即使目标的边缘是模糊不清的图像,该算法也能实现精确地分割;其次,该算法还克服了传统动态轮廓分割算     

An integrated two-stage approach for image segmentation via active contours

A novel integrated two-stage approach is proposed for image segmentation, where the edge, global and local region information of images are in turn incorporated to define the intensity fitting energy. In the first stage, the Chan-Vese model flexibly assimilates the edge indicator function in the beginning, and then the Laplace operator is introduced to regularize the level set function when minimizing the energy functional. As an edge-based and global region-based active contour, it can be inclined to rapidly produce a coarse segmentation result. In the second stage, we further segment the image by absorbing the local region fitting energy, where its initialization is acquired by the final active contour of the first stage. In addition, we present a generalized level set regularization term, which efficiently eliminates the periodically re-initialization procedure of traditional level set methods and maintains the corresponding signed distance property. Compared with the first stage, the local object details are accurately segmented in the second stage, which can acquire an accurate segmentation result. Qualitative and quantitative experimental results demonstrate the accuracy, robustness and efficiency of our approach with applications to some synthetical and real-world images.

     

红外图像活动轮廓分割算法

红外图像通常存在边缘模糊、对比度低、背景复杂等特点,传统的活动轮廓模型难以达到理想的分割效果。针对这种情况,提出了一种能够用于红外图像分割的边缘活动轮廓模型。该模型采用了结合图像局部熵信息定义的边缘停止函数,同时提出了一种针对红外图像的自动选取初始轮廓的方法,进一步提高了算法的效率。实验结果表明,采用该模型能够有效分割红外图像。     

结合显著性的主动轮廓图像分割

传统的主动轮廓方法无法突出分割区域的显著性,同时在由显著性检测算法所得到的显著图中目标具有较高的信噪比,因此提出结合显著性的主动轮廓图像分割。通过线性光谱聚类分割得到超像素,以超像素为处理单位利用基于图论的流形排序算法获得较好的显著图;将高斯混合模型引入到主动轮廓的曲线演化过程中,计算曲线内外的平均灰度值,从而通过高斯混合模型和显著性信息得到了新的主动轮廓能量方程,并运用水平集方法指导分割,获得最终的分割结果。实验结果表明,提出的图像分割方法可以对图像进行快速和有效的分割。     

基于各向异性扩散活动轮廓模型的左心室MRI分割

结合各向异性扩散算法与梯度矢量流活动轮廓模型,提出了基于各向异性扩散活动轮廓模型并应用于心脏核磁共振图像分割;模型采用各向异性扩散方程构造活动轮廓模型的外部能量函数,得到边界更加清晰的分段平滑图像,运用梯度矢量流将边缘图梯度散射到平坦区域,可以有效抑制噪声,同时保持了目标边界;对左心室核磁共振图像的分割实验表明,该模型可以克服噪声和伪影的干扰,与原梯度矢量流模型相比具有更高的精确性和可靠性,有利于实现自动分割.     

一种基于边缘与区域信息的先验水平集图像分割方法

传统的水平集图像分割方法仅考虑了图像的数据信息,因此对被遮盖的目标以及与背景灰度相近的目标无法达到理想的分割效果.针对这个问题,提出了一种基于边缘和区域信息的先验水平集图像分割方法.该方法首先将图像的区域信息融入基于边缘的水平集方法,然后将其与形状先验结合.对比实验表明该文方法由于综合考虑了多种信息,能够更好地完成被遮盖目标的分割,对于与背景灰度相近的目标也能达到更好的效果.     

Small object detection in cluttered image using a correlation based active contour model

This paper presents a novel energy function for active contour models based on autocorrelation function, which is capable of detecting small objects against a cluttered background. In the proposed method, image features are calculated using a combination of short-term autocorrelations (STA) computed from the image pixels to represent region information. The obtained features are exploited to define an energy function for the localized region-based active contour model called normalized accumulated short-term autocorrelation (NASTA). Minimizing this energy function, we can accurately detect small objects in images containing cluttered and textured backgrounds. Moreover, the proposed method provides high robustness against random noise and can precisely locate small objects in noisy backgrounds, difficult to be detected with naked eye. Experimental results indicate remarkable advantages of our approach comparing to existing methods.     

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。     

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.