一种基于Ｃ均值的模糊核聚类图像分割算法

模糊核聚类算法是一种结合无监督聚类和模糊集合概念的图像分割技术,已广泛应用于图像分割领域,但其算法对初值敏感,很大程度上依赖初始聚类中心的选择,并且容易收敛于局部极小值,用于图像分割时,隶属度的计算只考虑了图像中当前的像素探值,而未考虑邻域像素探间的相互关系,故对分割含有噪声图像不理想。故提出了一种改进的模糊核聚类图像分割算法,先通过数据约简,不损失数据聚类结构的前提下对数据进行挖掘,然后在模糊核聚类算法中引入特性核函数,将约简后的数据映射到高维非线性特征空间进行划分,最后再利用表征邻域像素的参数来修正当前空间像素的隶属度。实验结果表明,提出的算法较好地解决了模糊核聚类算法在局部极值处收敛和在迭代过程中出现停滞等问题,最终得到最佳全局聚类,迭代次数降低明显,并具有高鲁棒性、对噪声不敏感的特点。     

基于模糊C均值聚类与空间信息相结合的图像分割新算法

针对传统的模糊C均值聚类（FCM）图像分割方法未考虑图像的空间信息,对噪声十分敏感的问题,本文提出了一种结合空间信息的模糊C均值聚类分割新算法;该算法将图像的二维直方图引入传统的模糊C均值聚类算法中,并对隶属函数做了改进;依据平方误差和最小准则,来确定模糊分类矩阵及聚类中心;最后,依据最大隶属度原则,划分图像像素的类别归属,以改善传统的PCM算法的分割质量。实验结果表明,该算法显示了较好的分割效果和较强的抗噪性能。     

基于改进模糊均值聚类算法的医学图像分割

为了提高医学图像分割性能,针对传统模糊聚类算法存在的缺陷,提出了一种改进模糊均值聚类算法的医学图像分割方法。首先采用粒子群算法选择模糊均值聚类算法的聚类中心,然后利用空间邻域信息设定聚类样本空间,最后采用具体的医学图像数据进行仿真实验,测试其有效性。仿真结果表明,相对于传统模糊聚类算法,本文算法不仅提高了医学图像分割精度,而且提高了医学图像分割效率。     

一种二型模糊可能性聚类红外图像分割算法

提出了一种新的基于二型模糊可能性聚类的红外图像分割算法。针对受概率约束的模糊聚类算法和不受概率约束的可能性聚类算法在红外图像分割时存在的问题,采用二型模糊系统融合两种分割算法的隶属度函数,将隶属度函数看作一个区间型分布,而不是单独采用两种算法输出的确定模糊值。这种处理方式不但能有效抑制噪声及野值,而且能有效防止红外图像的过分割。实验仿真结果表明,该算法较传统聚类算法能获得更好的分割效果,可有效抑制噪声对目标区域分割的干扰。     

基于并行小生境技术优化的智能模糊聚类图像分割算法

利用PBM模糊聚类有效性函数以图像特征空间为搜索空间,实现有效性函数的全局寻优,用并行小生境技术解决粒子群(PSO)算法的早收敛问题,优化聚类的全局收敛性能,实现有效聚类数目与聚类中心的并行寻优。通过对遥感图像分割的实验证明,与传统粒子优化群算法的分割结果相比,本文算法拥有更高的有效性且分割效果更优。     

一种基于DFC聚类算法的图像分割方法

图像分割是一种重要的图像技术,在理论研究和实际应用中都得到了人们的广泛重视。图像分割的方法和种类很多,有些分割运算可直接应用于任何图像,而另一些只能适用于特殊类别的图像。目前,图像分割的方法层出不穷。其中,最具代表性的图像分割算法是基于FCM聚类算法的图像分割方法。然而FCM聚类算法从理论上来说存在着聚类数目无法自动确定及运算的开销太大的缺点,因而限制了这种方法的应用。针对其不足,本文将FCM聚类算法引入到图像分割方法中。数值实验结果显示：新方法分割图像的效果是良好的。     

变长度微粒群优化模糊聚类的自适应图像分割方法

模糊C均值聚类(FCM.fuzzy c-means)图像分割方法,对初值选取较敏感,并且需要事先确定聚类数目.为此,提出了一种基于变长度微粒群算法(PSO,particle swarm optimization)优化PBMF模糊聚类的自适应图像分割方法.PBMF指标函数考虑了聚类数目和聚类中心,通过设计变长度PSO算法来实现PBMF指标函数的优化过程,并利用统计直方图将图像从像素窄间映射到灰度直方图特征空间,从而快速地获得图像的最佳聚类数日和聚类中心.对遥感图像的分割实验表明,该自适应分割策略具有全局搜索图像最佳聚类数月和聚类中心的能力,以及较强的抗噪能力.     

马尔可夫化的多尺度FCM在影像分割中的应用

为了同时处理影像分割问题中的随机性与模糊性,提出了一种多尺度(MR,multi-resolu-tion,马尔可夫随机场(MRF,markov random field)模型下的模糊C均值(FCM,fuzzy C-means)聚类分割算法(MR-MRF-FCM)。利用FCM算法能够处理影像模糊性的优点、MRF模型描述空间关系的长处以及小波的多尺度分析的优点,先对影像进行多尺度小波分解,并对小波系数建立MRF,进而用MR-MRF中的条件概率矩阵代替FCM算法的隶属度矩阵。实验结果从视觉效果和定量指标两方面表明,本文方法优于经典的MRF、多尺度MRF、FCM和核FCM等方法。     

Level set formulation for automatic medical image segmentation based on fuzzy clustering

The level set method is widely used in medical image segmentation, in which the performance is seriously subject to the initialization and parameters configuration. An automatic segmentation method was proposed in this paper, which integrates fuzzy clustering with level set method through a dynamic constrained term in the new energy functional. It is able to use the results of fuzzy clustering directly, which can control the level set evolution. Moreover, the added constrained term is changing continuously until getting the final results. Such algorithm eliminates the manual operation a lot and leads to more robust segmentation results. With the split Bregman method, the minimization of the new energy functional is fast. The proposed algorithm was tested on some medical images and also compared with other level set models and the state-of-the-art method such as U-Net. The quantitative and qualitative experimental results show its effectiveness and obvious improvement for medical image segmentation.     

一种新的结合稀疏编码的红外图像聚类分割算法

聚类作为一种重要的图像分割方法得到了大量研究,提出了一种新的结合稀疏编码的红外图像聚类分割算法,扩展了传统的基于K-means聚类的图像分割方法。结合稀疏编码的聚类算法能有效融合图像的局部信息,而且易于利用像素之间的内在相关性,但是对于分割会出现过分割和像素难以归类的问题。为此,在字典的学习过程中,将原子的聚类算法引入其中,有助于缩减字典中原子所属类别的数目防止出现过分割;同时将稀疏编码系数同原子对聚类中心的隶属程度相结合来判断像素所属的类别。这种处理方式能更好地实现利用像素的内在相关性进行聚类分割,并在其中自然引入了局部空间信息,达到更好分离目标区域和背景区域的目的。实验结果表明,结合稀疏编码的K-means聚类分割算法能更好的实现复杂背景下红外图像重要区域的准确分割提取。     

Infrared image segmentation algorithm based on distribution information intuitionistic fuzzy c-means clustering

Due to the sensitivity of the traditional intuitionistic fuzzy c-means (IFCM) clustering algorithm to the clustering center in image segmentation,which resulted in the low clustering precision,poor retention of details,and large time complexity,an intuitionistic fuzzy c-means clustering algorithm was proposed based on spatial distribution information suitable for infrared image segmentation of power equipment.The non-target objects with high intensity and the non-uniformity of image intensity in the infrared image had strong interference to the image segmentation,which could be effectively suppressed by the proposed algorithm.Firstly,the Gaussian model was introduced into the global spatial distribution information of power equipment to improve the IFCM algorithm.Secondly,the membership function was optimized by local spatial operator to solve the problem of edge blur and image intensity inhomogeneity.The experiments conducted on Terravic motion IR database and the data set containing 300 infrared images of power equipment show that,the relative region error rate is about 10% and is less affected by the change of fuzzy factor m.The effectiveness and applicability of the proposed algorithm are superior to other comparison algorithms.     

多分辨率图像锥的FCM聚类图像分割算法

模糊C-均值（FCM）聚类算法是一种基于像素分类的图像分割方法，在分割的过程中，仅仅利用了像素点的灰度信息，但在灰度密度丰富变化和图像的对比度不明显的情况下，物体和背景的分布将相互重叠而密不可分，往往得不到满意的分割效果。为了解决上述问题，现提出了一种基于多分辨率图像锥的模糊C-均值聚类图像分割算法。该方法利用多分辨技术产生多分辨率图像锥，将图像从空间信息引入，考虑图像的局部特性，使分割算法局限于图像的子图像中，物体和背景比单纯运用FCM更容易区分，且算法稳定性高，速度快。     

区间二型模糊C均值聚类在图像分割中的应用

聚类分析是非监督模式识别的重要分支,模糊C均值聚类算法(FCM)是其中的一类经典算法,然而该算法以一型模糊集为基础,无法处理数据集以及算法中的不确定性,为此引入区间二型模糊C均值聚类算法(IT2FCM)。二型模糊集处理不确定性的能力强于一型模糊集,基于二型模糊集的IT2FCM在处理不确定性时效果优于FCM算法。文章以图像分割为应用对象,比较IT2FCM和FCM算法的分割效果,实验证明IT2FCM较传统FCM有更好的抗噪性。     

基于面积约束和自适应梯度修正的分水岭图像分割

图像中的噪声或非规则细节干扰易导致形态学 分水岭产生较严重的过分割,为了在消除过分割的同时尽可能 保持图像目标边界的准确定位,提出了一种基于面积约束和自适应梯度修正的分水岭图像分 割方法。首先对图像进行梯 度变换,采用区域面积约束滤除狭小高梯度尖峰对应的噪声和非规则细节;然后建立梯度级 与结构元素大小之间的函数 关系,并以相对应的结构元素对梯度图像进行粘性形态学(VM)闭运算,消除低梯度噪声及非 规则细节,实现梯度图像的自适 应修正,由于VM闭运算对梯度图像进行修正时,对目标仅作轻度或不作修正,因 而能够最大限度的保持目标轮 廓的准确定位,而对噪声和非规则细节则采用较大尺寸的结构元素进行较大幅度修正,从而 消除产生过分割的因素;最 后对修正图像进行分水岭分割。实验结果表明,本文方法能够在消除过分割的同时,保持目 标轮廓的准确定位。     

Brain MR image segmentation based on local Gaussian mixture model and nonlocal spatial regularization

Brain Magnetic Resonance (MR) images often suffer from the inhomogeneous intensities caused by the bias field and heavy noise. The most widely used image segmentation algorithms, which typically rely on the homogeneity of image intensities in different regions, often fail to provide accurate segmentation results due to the existence of bias field and heavy noise. This paper proposes a novel variational approach for brain image segmentation with simultaneous bias correction. We define an energy functional with a local data fitting term and a nonlocal spatial regularization term. The local data fitting term is based on the idea of local Gaussian mixture model (LGMM), which locally models the distribution of each tissue by a linear combination of Gaussian function. By the LGMM, the bias field function in an additive form is embedded to the energy functional, which is helpful for eliminating the influence of the intensity inhomogeneity. For reducing the influence of noise and getting a smooth segmentation, the nonlocal spatial regularization is drawn upon, which is good at preserving fine structures in brain images. Experiments performed on simulated as well as real MR brain data and comparisons with other related methods are given to demonstrate the effectiveness of the proposed method.     

改进的模糊熵与图像局部信息相结合的遥感图像分割新算法

针对传统的一维最大模糊熵图像分割算法没有考虑图像的局部信息而对噪声十分敏感的这一不足,本文提出了结合图像局部信息的一维模糊熵图像分割算法。该算法将图像的空间信息和像素信息引入到一维模糊熵图像分割算法中,并运用微正则退火算法对一维最大模糊熵进行改进,从而提高了传统的一维最大模糊熵分割精度。实验结果表明,该算法显示了很好的分割效果和较强的抗噪性能。     

基于超顺磁聚类的复杂红外图像分割算法

针对复杂红外图像分割问题,将非均匀Potts模型的热力学聚集运动看作是数据聚类,提出了基于超顺磁聚类的分割新算法.算法首先要指定控制系统的哈密尔顿函数,然后通过测量磁化率随温度变化的曲线来识别系统的不同相位,最后在超顺磁相位测量相邻自旋子的相关函数来将图像分割成子类.结合SW算法和Metropolis算法给出了一种新的产生马尔科夫过程的方法,该过程能够快速收敛于Boltzmann分布,从而降低超顺磁聚类方法的计算量.在复杂红外图像上的分割实验表明,新算法在收敛速度和分割效果方面都明显优于经典的SW算法.     

The image segmentation based on optimized spatial feature of superpixel

This paper proposes a method of image segmentation based on superpixels. The method is applied to achieve the segmentation of synthetic aperture radar (SAR) image. Firstly, the superpixels are extracted based on multi-scale features. Then, the fuzzy c-means (FCM) clustering based on superpixels is implemented, in which the influence of neighboring and similar superpixels is incorporated into FCM and the influential degree is optimized to improve segmentation performance. Experimental results show that the proposed method can achieve an impressive accuracy of SAR segmentation. For application extension, when we extract corresponding feature from several types of specific images, the proposed method is able to achieve better segmentation performance.     

A human visual system-driven image segmentation algorithm

This paper presents a novel image segmentation algorithm driven by human visual system (HVS) properties. Segmentation quality metrics, based on perceptual properties of HVS with respect to segmentation, are integrated into an energy function. The energy function encodes the HVS properties from both region-based and boundary-based perspectives, where the just-noticeable difference (JND) model is employed when calculating the difference between the image contents. Extensive experiments are carried out to compare the performances of three variations of the presented algorithm and several representative segmentation and clustering algorithms available in the literature. The results show superior performance of our approach.     

Phishing web image segmentation based on improving spectral clustering

This paper proposes a novel phishing web image segmentation algorithm which based on improving spectral clustering. Firstly, we construct a set of points which are composed of spatial lo-cation pixels and gray levels from a given image. Secondly, the data is clustered in spectral space of the similar matrix of the set points, in order to avoid the drawbacks of K-means algorithm in the con-ventional spectral clustering method that is sensitive to initial clustering centroids and convergence to local optimal solution, we introduce the clone operator, Cauthy mutation to enlarge the scale of clustering centers, quantum-inspired evolutionary algorithm to find the global optimal clustering centroids. Compared with phishing web image segmentation based on K-means, experimental results show that the segmentation performance of our method gains much improvement. Moreover, our method can convergence to global optimal solution and is better in accuracy of phishing web seg-mentation.