Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure

Fuzzy c-means clustering (FCM) with spatial constraints (FCM_S) is an effective algorithm suitable for image segmentation. Its effectiveness contributes not only to the introduction of fuzziness for belongingness of each pixel but also to exploitation of spatial contextual information. Although the contextual information can raise its insensitivity to noise to some extent, FCM_S still lacks enough robustness to noise and outliers and is not suitable for revealing non-Euclidean structure of the input data due to the use of Euclidean distance (L2 norm). In this paper, to overcome the above problems, we first propose two variants, FCM_S1 and FCM_S2, of FCM_S to aim at simplifying its computation and then extend them, including FCM_S, to corresponding robust kernelized versions KFCM_S, KFCM_S1 and KFCM_S2 by the kernel methods. Our main motives of using the kernel methods consist in: inducing a class of robust non-Euclidean distance measures for the original data space to derive new objective functions and thus clustering the non-Euclidean structures in data; enhancing robustness of the original clustering algorithms to noise and outliers, and still retaining computational simplicity. The experiments on the artificial and real-world datasets show that our proposed algorithms, especially with spatial constraints, are more effective.     

Robust interactive image segmentation using structure-aware labeling

Interactive image segmentation has remained an active research topic in image processing and graphics, since the user intention can be incorporated to enhance the performance. It can be employed to mobile devices which now allow user interaction as an input, enabling various applications. Most interactive segmentation methods assume that the initial labels are correctly and carefully assigned to some parts of regions to segment. Inaccurate labels, such as foreground labels in background regions for example, lead to incorrect segments, even by a small number of inaccurate labels, which is not appropriate for practical usage such as mobile application. In this paper, we present an interactive segmentation method that is robust to inaccurate initial labels (scribbles). To address this problem, we propose a structure-aware labeling method using occurrence and co-occurrence probability (OCP) of color values for each initial label in a unified framework. Occurrence probability captures a global distribution of all color values within each label, while co-occurrence one encodes a local distribution of color values around the label. We show that nonlocal regularization together with the OCP enables robust image segmentation to inaccurately assigned labels and alleviates a small-cut problem. We analyze theoretic relations of our approach to other segmentation methods. Intensive experiments with synthetic and manual labels show that our approach outperforms the state of the art.     

Robust fuzzy clustering-based image segmentation

The fuzzy clustering algorithm fuzzy c-means (FCM) is often used for image segmentation. When noisy image segmentation is required, FCM should be modified such that it can be less sensitive to noise in an image. In this correspondence, a robust fuzzy clustering-based segmentation method for noisy images is developed. The contribution of the study here is twofold: (1) we derive a robust modified FCM in the sense of a novel objective function. The proposed modified FCM here is proved to be equivalent to the modified FCM given by Hoppner and Klawonn [F. Hoppner, F. Klawonn, Improved fuzzy partitions for fuzzy regression models, Int. J. Approx. Reason. 32 (2) (2003) 85–102]. (2) We explore the very applicability of the proposed modified FCM for noisy image segmentation. Our experimental results indicate that the proposed modified FCM here is very suitable for noisy image segmentation.     

结合形状统计的水平集图像分割

给出了一种结合先验形状统计信息的Mumford-Shah模型的水平集实现方法。结合形状统计的水平集图像分割主要包括先验形状模型的构造和形状能量项的构造,针对这两个主要方面做了如下两点工作：（1）提出了一种简单可行的先验形状模型构造方法;（2）重新构造了形状能量项,它综合考虑了全局和局部形状信息,且不含形状姿态参量,使曲面演化稳定可靠。带标记线左心室核磁共振（MR）长轴图像的实验结果和合成图像的分割结果证明了该方法的有效性。     

形状约束下活动轮廓模型冠脉血管图像多尺度分割

目的 由于计算机断层血管造影(CTA)图像的复杂性,临床诊断冠脉疾病往往需要经验丰富的医师对冠状动脉进行手动分割,快速、准确自动分割出冠状动脉对提高冠脉疾病诊断效率具有重要意义。针对双源CT图像特点以及传统单一基于区域或边界的活动轮廓模型的不足,研究了心脏冠脉3维分割算法,提出一种基于血管形状约束的活动轮廓模型分割方法。方法 首先,利用改进的FCM(fuzzy C-means)对心脏CT图像感兴趣区域初分割,其结果用于初始化C-V模型水平集演化曲线及控制参数,提取感兴趣区域轮廓。接着,由3维心脏图像数据获取多尺度梯度矢量信息构造边界型能量泛函,然后利用基于Hessian矩阵的多尺度血管函数对心脏感兴趣区域3维体数据增强滤波,获取血管先验形状信息用于约束能量泛函。最后融合边界、区域能量泛函并利用变分原理及水平集方法得到适合冠脉血管分割的水平集演化方程。结果 由于血管图像的灰度不均匀,血管末端区域更为细小,所以上述算法的实施是面向被划分多个子区域的血管,在缩小的范围内进行轮廓的演化。相比于传统的血管分割方法,该方法充分融合血管图像的先验信息及梯度场信息,能够从灰度及造影剂分布不均匀的冠脉血管图像中准确分割出冠状动脉,对于细小的血管结构亦能获得较好的分割效果。实验结果表明,该方法只需在给定初始轮廓前提下,有效提取3维冠脉血管。结论 对多组心脏CT图像进行分割,本文基于血管先验形状约束的活动轮廓模型可以准确分割出冠脉结构完整轮廓,并且人工交互简单。该方法在双源CT冠脉图像自动分割方面具有较好的正确率与优越性。     

Robust color image segmentation using fuzzy c-means with weighted hue and intensity

In this paper we propose a novel method for color image segmentation. This method uses only hue and intensity components (which are chosen rationally) of image and combines those by adaptive tuned weights in a specially defined fuzzy c-means cost function. The tuned weights indicate how informative every color component (hue and intensity) is. Obtaining tuned weights begins with finding peaks of hue and intensity's histograms and continues by obtaining the table of the frequencies of hue and intensity values and computing entropy and contrast of every color component. Also this method specifies proper initial values for cluster centers with the aim of reducing the overall number of iterations and avoiding converging of FCM to wrong centroids. Experimental results demonstrate that our algorithm achieves better segmentation performance and also runs faster than similar methods.     

重要性图约束的均值漂移图像分割

针对已有算法结果分割区域过多问题,提出采用边缘正交场构造重要性图,通过边缘特征稳定性约束分割区域,从而有效地提高分割质量。构造边缘正交场,通过高斯积分提高边缘线的连续性和稳定性。采用边缘特征进行距离变换,生成图像的重要性图。采用均值漂移进行图像预分割,根据相邻区域边界上的重要性强度对分割区域结果进行合并。实验结果表明,和原有分割方法相比较,算法在保持原始图像重要区域的同时,对细节区域进行有效合并,明显提高分割质量。     

Robust estimation for range image segmentation and reconstruction

This correspondence presents a segmentation and fitting method using a new robust estimation technique. We present a robust estimation method with high breakdown point which can tolerate more than 80% of outliers. The method randomly samples appropriate range image points in the current processing region and solves equations determined by these points for parameters of selected primitive type. From K samples, we choose one set of sample points that determines a best-fit equation for the largest homogeneous surface patch in the region. This choice is made by measuring a residual consensus (RESC), using a compressed histogram method which is effective at various noise levels. After we get the best-fit surface parameters, the surface patch can be segmented from the region and the process is repeated until no pixel left. The method segments the range image into planar and quadratic surfaces. The RESC method is a substantial improvement over the least median squares method by using histogram approach to inferring residual consensus. A genetic algorithm is also incorporated to accelerate the random search     

Robust image segmentation against complex color distribution

Color distribution is the most effective cue that is widely adopted in previous interactive image segmentation methods. However, it also may introduce additional errors in some situations, for example, when the foreground and background have similar colors. To address this problem, this paper proposes a novel method to learn the segmentation likelihoods. The proposed method is designed for high reliability, for which purpose it may choose to discard some unreliable likelihoods that may cause segmentation error. The reliability of likelihoods is estimated in a few Expectation–Maximization iterations. In each iteration, a novel multi-class transductive learning algorithm, namely, the Constrained Mapping, is proposed to learn likelihoods and identify unreliable likelihoods simultaneously. The resulting likelihoods then can be used as the input of any segmentation methods to improve their robustness. Experiments show that the proposed method is an effective way to improve both segmentation quality and efficiency, especially when the input image has complex color distribution.     

Model-based detection,segmentation, and classification for image analysis using on-line shape learning

Detection, segmentation, and classification of specific objects are the key building blocks of a computer vision system for image analysis. This paper presents a unified model-based approach to these three tasks. It is based on using unsupervised learning to find a set of templates specific to the objects being outlined by the user. The templates are formed by averaging the shapes that belong to a particular cluster, and are used to guide a probabilistic search through the space of possible objects. The main difference from previously reported methods is the use of on-line learning, ideal for highly repetitive tasks. This results in faster and more accurate object detection, as system performance improves with continued use. Further, the information gained through clustering and user feedback is used to classify the objects for problems in which shape is relevant to the classification. The effectiveness of the resulting system is demonstrated in two applications: a medical diagnosis task using cytological images, and a vehicle recognition task. Received: 5 November 2000 / Accepted: 29 June 2001 Correspondence to: K.-M. Lee     

Robust interactive image segmentation via graph-based manifold ranking

Computational Visual Media - Interactive image segmentation aims at classifying the image pixels into foreground and background classes given some foreground and background markers. In this paper,...     

融合Kernel PCA形状先验信息的变分图像分割模型

目的 基于能量最小化的变分图像分割方法已经受到研究人员的广泛重视,取得了丰硕成果。但是,针对图像中存在的噪音污染、目标被遮挡等情况,则难以正确分割。引入先验形状信息是解决该问题的一个重要方向,但是随之而带来的姿态变化问题是一个难点。传统的做法是在每步迭代过程中单独计算姿态变换参数,导致计算量大。方法 在基于Kernel PCA(KPCA)的形状先验模型基础上,提出一种具有内在的姿态不变性的KPCA形状先验模型,并将之融合到C-V变分图像分割模型中。结果 提出模型无须在每步迭代中显式地单独计算姿态变换参数,相对于C-V模型分割正确率能够提高7.47%。同时,针对KPCA模型中计算高斯核函数的参数σ取值问题,也给出一种自适应的计算方法。结论 理论分析及实验表明该模型能较好地解决先验形状与目标间存在的仿射变化问题,以及噪音、目标被遮挡等问题。     

Exposing image resampling forgery by using linear parametric model

Resampling forgery generally refers to as the technique that utilizes interpolation algorithm to maliciously geometrically transform a digital image or a portion of an image. This paper investigates the problem of image resampling detection based on the linear parametric model. First, we expose the periodic artifact of one-dimensional 1-D) resampled signal. After dealing with the nuisance parameters, together with Bayes’ rule, the detector is designed based on the probability of residual noise extracted from resampled signal using linear parametric model. Subsequently, we mainly study the characteristic of a resampled image. Meanwhile, it is proposed to estimate the probability of pixels’ noise and establish a practical Likelihood Ratio Test (LRT). Comparison with the state-of-the-art tests, numerical experiments show the relevance of our proposed algorithm with detecting uncompressed/compressed resampled images.     

基于形状先验和图割的彩色图像分割

基于图割理论的图像分割方法在二值标号问题中可以获取全局最优解,而在多标号问题中可以获取带有很强特征的局部最优解。但对于含有噪声或遮挡物等复杂的图像,分割结果不完整,效果并不令人满意,提出了一种基于形状先验和图割的图像分割方法。以图割算法为基础,加入形状先验知识,使该算法包含更多约束信息,从而限制感兴趣区域的搜寻空间,能够更好地分割出完整的目标,增加了算法的精确度。针对形状的仿射变换,运用特征匹配算法进行处理,使算法更加具有灵活性,能够应对不同类型的情况。实验表明了该算法的有效性。     

Graphical Gaussian shape models and their application to image segmentation

This paper presents a novel approach to shape modeling and a model-based image segmentation procedure tailor-made for the proposed shape model. A common way to represent shape is based on so-called key points and leads to shape variables, which are invariant with respect to similarity transformations. We propose a graphical shape model, which relies on a certain conditional independence structure among the shape variables. Most often, it is sufficient to use a sparse underlying graph reflecting both nearby and long-distance key point interactions. Graphical shape models allow for specific shape modeling, since, e.g., for the subclass of decomposable graphical Gaussian models both model selection procedures and explicit parameter estimates are available. A further prerequisite to a successful application of graphical shape models in image analysis is provided by the "toolbox" of Markov chain Monte Carlo methods offering highly flexible and effective methods for the exploration of a specified distribution. For Bayesian image segmentation based on a graphical Gaussian shape model, we suggest applying a hybrid approach composed of the well-known Gibbs sampler and the more recent slice sampler. Shape modeling as well as image analysis are demonstrated for the segmentation of vertebrae from two-dimensional slices of computer tomography images.     

鲁棒空间约束的模糊聚类图像分割

目的 为进一步提高分割精度,在模糊聚类的基础上引入统计信息,提出一种鲁棒型空间约束的模糊聚类分割算法。方法 基于局部空间信息的先验概率与后验概率,提出一种新型空间约束项,并通过卷积操作提高运行效率;进而引入负对数联合概率作为测度函数,进一步提高算法对于各像素点所属类别的甄别能力;同时将测度函数与空间约束项整合至目标函数中,通过迭代更新各参数达到最小化目标函数的目的。结果 对于合成图像的实验结果表明,本文算法对于噪声类型和噪声强度具有较强的鲁棒性;对于彩色图像的实验结果表明,在适当的特征描述符的辅助下,本文算法也能够获得令人满意的分割结果和较高的分割精度。结论 本文算法克服了现有算法的缺陷,进一步提升了图像的分割精度。其适用于分割带噪声图像,且在适当纹理特征的辅助下分割彩色图像,与同类算法的比较实验结果验证了本文算法的有效性。     

Watershed segmentation using prior shape and appearance knowledge

Watershed transformation is a common technique for image segmentation. However, its use for automatic medical image segmentation has been limited particularly due to oversegmentation and sensitivity to noise. Employing prior shape knowledge has demonstrated robust improvements to medical image segmentation algorithms. We propose a novel method for enhancing watershed segmentation by utilizing prior shape and appearance knowledge. Our method iteratively aligns a shape histogram with the result of an improved k-means clustering algorithm of the watershed segments. Quantitative validation of magnetic resonance imaging segmentation results supports the robust nature of our method.     

Robust skin segmentation using color space switching

Skin detection is very popular and has vast applications among researchers in computer vision and human computer interaction. The skin-color changes beyond comparable limits with considerable change in the nature of the light source. Different properties are taken into account when the colors are represented in different color spaces. However, a unique color space has not been found yet to adjust the needs of all illumination changes that can occur to practically similar objects. Therefore a dynamic skin color model must be constructed for robust skin pixel detection, which can cope with natural changes in illumination. This paper purposes that skin detection in a digital color image can be significantly improved by employing automated color space switching. A system with three robust algorithms has been built based on different color spaces towards automatic skin classification in a 2D image. These algorithms are based on the statistical mean of value of the skin pixels in the image. We also take Bayesian approaches to discriminate between skin-alike and non-skin pixels to avoid noise. This work is tested on a set of images which was captured in varying light conditions from highly illuminated to almost dark.