多发性硬化症MR图像分割新算法研究

提出了一种针对多发性硬化症病灶T2加权脑部磁共振（MR）图像的分割算法。根据多发性硬化症病灶和脑脊液在T2加权像上同表现为高亮度信号的特点,把模糊C均值分割算法与形态学方法相结合,提出了基于核模糊C均值的多发性硬化症病灶分割算法。该算法首先用改进的核模糊C均值算法做基础分割,再用形态学方法提取出多发性硬化症病灶得到最终分割结果。通过对多发性硬化症模拟脑部MR图像的分割结果表明,算法能够比较准确地分割多发性硬化症病灶。     

基于多目标规划的模糊C均值聚类算法

模糊C均值聚类算法(FCM)是一种非常经典的非监督聚类技术,已被广泛地应用到医学图像分割。由于传统的FCM聚类算法在分割图像时仅利用了图像的灰度信息,未利用图像的空间信息,在分割叠加了噪声的磁共振(MR)图像时分割效果不理想。考虑到脑部MR图像真实的灰度值具有分片为常数的特性,按照合理利用图像空间信息的原则,对传统的FCM聚类算法进行了改进,引入多目标规划的概念,提出了一种新的,更加合理的应用图像空间信息的聚类算法。实验结果表明,应用该算法可以有效地分割含有噪声的图像。     

融合加窗色调直方图的彩色图像FCM分割算法

针对传统FCM算法计算量大、对噪声敏感的缺点,提出了一种融合加窗色调直方图的快速FCM算法（MHFCM）。对彩色图像的色调直方图加窗得到背景色调,对去除背景信息后的有效信息进行FCM聚类分割。实验结果表明,MHFCM算法提高了图像的分割速度,并且克服了图像中背景噪声对分割效果的影响,使分割更加准确。     

基于证据理论的脑部图像分割算法

针对在马尔可夫随机场和模糊聚类二维直方图方法中存在的像素点分割结果不一致的现象,提出一种基于Dempster-Shafer(D-S)证据理论的图像融合分割算法。利用模拟退火算法恢复出脑部轮廓,根据该轮廓从原图中提取出脑核部分,采用马尔可夫随机场和模糊聚类二维直方图方法分别对脑核部分进行分割,通过D-S证据理论将分类不确定的争议像素划归到似真区间,并进行融合分割。实验结果表明,该算法能解决争议点的归属问题,有效滤除噪声,稳健性较好。     

结合非局部均值的快速FCM算法分割MR图像研究

针对FCM算法分割医学MR图像存在的运算速度慢、对初始值敏感以及难以处理MR图像中固有Rician噪声等缺陷,提出了一种结合非局部均值的快速FCM算法。该算法的核心是首先针对MR图像中存在的Rician噪声,利用非局部均值算法对图像进行去噪处理,消除噪声对分割结果的影响;然后根据所提出的新的自动获取聚类中心的规则得到初始聚类中心;最后将得到的聚类中心作为快速FCM算法的初始聚类中心用于去噪后的图像分割,解决了随机选择初始聚类中心造成的搜索速度慢和容易陷入局部极值的问题。实验表明,该算法能够快速有效地分割图像,并且具有较好的抗噪能力。     

基于隶属度光滑约束的模糊C均值聚类算法

传统的FCM聚类算法未利用图像的空间信息,在分割叠加了噪声的MR图像时分割效果不理想。本文考虑到脑部MR图像真实的灰度值具有分片为常数的特性,按照合理利用图像空间信息的原则,对传统的FCM聚类算法进行了改进,增加了使隶属度趋向于分片光滑的约束项,得到了新的聚类算法。通过对模拟脑部MR图像和临床脑部MR图像的分割实验结果表明,本文提出的新算法比传统的FCM算法等多种图像分割算法有更精确的图像分割能力,并且运算简单、运算速度快、稳健性好。     

基于MS-FCM算法的MR图像分割方法

针对传统模糊C-均值(FCM)聚类算法在分割低信噪比图像时准确性较差的问题,提出一种用于MR图像分割的改进算法MS-FCM。针对脑部MR图像相邻像素属于同一分类的模糊隶属度相近的特性,在迭代过程中对隶属度数据集进行滤波,以降低噪声对聚类精度的影响。模拟脑部MR图像和临床脑部MR图像的分割实验证明,该算法可以提高图像分割精度。     

基于小波图像融合算法和改进FCM聚类的MR脑部图像分割算法

针对很多基于模糊C均值(FCM)的图像分割算法存在对噪声敏感和分割轮廓不清晰等问题,提出一种基于小波变换图像融合算法和FCM聚类算法的MR医学图像分割算法。在图像分割系统的第一阶段,利用Haar小波多分辨率特性保持像素间的空间信息;第二阶段,利用小波图像融合算法对得到的多分辨率图像和原始图像进行融合,进而增强被处理图像的清晰度并降低噪声;第三阶段,利用改进型FCM技术对所处理的图像进行分割。在BrainWeb数据集上进行实验,与现有相关算法相比,提出的算法具有较高的分割精度,且对噪声的鲁棒性比较强,处理时间也没有明显增加。     

分割多发性硬化症白质病灶的新方法

多发性硬化症(MS)是一种慢性的中枢神经系统疾病,其病灶可由常规脑部核磁共振成像(cMRI)进行检测。为提高图像处理的效率,提出了一种自动分割cMRI图像中的MS白质病灶(WML)的新方法。首先将模糊核聚类（KFCM)用于预处理后的T1加权像,得到白质图像;然后利用一个种子点的区域生长处理白质图像,提取出一个二值模板。该模板与对应的T2加权像进行乘积,得到一幅仅包含白质、病灶及背景的图像;最后再次利用KFCM分割图像,得到病灶的核心部分。实验结果表明,所提出的方法能快速、有效地分割出低噪声仿真图像中的WML,且Dice相似性系数平均值在80%以上。     

基于小波分解和模糊聚类的图像分割方法

传统的FCM图像聚类法由于需要大量先验知识和聚类速度的原因,大大限制其在图像分割领域的应用.提出一种基于小波分解和模糊聚类相结合的图像分割算法,首先对图像进行小波变换,对于L空间得到的灰度图像利用小波多尺度分解的性质得到特征图像,利用此特征图像的一维灰度信息采用模糊C均值聚类(FCM)算法,并自动确定FCM算法聚类数和聚类中心从而完成聚类的无监督化,实现对经小波分解后的特征图像的高效快速分割.     

非局部降噪快速模糊C-均值聚类算法

传统的模糊C-均值聚类算法未利用图像的空间信息,在分割迭加了噪声的MR图像时分割精度较差。采用了既能有效去除噪声又能较好地保持图像边缘特征的非局部降噪方法,结合基于图像灰度直方图聚类分析的快速模糊C-均值聚类算法,得到了一种具有较高分割精度的图像快速分割算法。通过对模拟图像、仿真脑部MR图像和临床脑部MR图像的分割实验,表明提出的新算法比已有的快速模糊C-均值聚类算法有更精确的图像分割能力。     

A study on fuzzy clustering for magnetic resonance brain image segmentation using soft computing approaches

This paper presents a novel idea of intracranial segmentation of magnetic resonance (MR) brain image using pixel intensity values by optimum boundary point detection (OBPD) method. The newly proposed (OBPD) method consists of three steps. Firstly, the brain only portion is extracted from the whole MR brain image. The brain only portion mainly contains three regions–gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). We need two boundary points to divide the brain pixels into three regions on the basis of their intensity. Secondly, the optimum boundary points are obtained using the newly proposed hybrid GA–BFO algorithm to compute final cluster centres of FCM method. For a comparison, other soft computing techniques GA, PSO and BFO are also used. Finally, FCM algorithm is executed only once to obtain the membership matrix. The brain image is then segmented using this final membership matrix. The key to our success is that we have proposed a technique where the final cluster centres for FCM are obtained using OBPD method. In addition, reformulated objective function for optimization is used. Initial values of boundary points are constrained to be in a range determined from the brain dataset. The boundary points violating imposed constraints are repaired. This method is validated by using simulated T1-weighted MR brain images from IBSR database with manual segmentation results. Further, we have used MR brain images from the Brainweb database with additional noise levels to validate the robustness of our proposed method. It is observed that our proposed method significantly improves segmentation results as compared to other methods.     

利用空间信息的核模糊C均值聚类算法

模糊聚类,特别是模糊C均值聚类算法（FCM）广泛地运用到图像的分割中。但是传统的算法未对数据对特征进行优化,亦未考虑图像的空间信息,对噪声图像分割不理想。在FCM目标函数中引入核函数,用内核引导距离代替传统的欧式距离,同时考虑到邻近象素的影响,增加了空间约束项,提出了利用空间信息的核FCM算法。通过对模拟图和仿真脑部MR图像的分割实验证明,该算法可以有效的分割含有噪声图像。     

Fuzzy c-means clustering with non local spatial information for noisy image segmentation

As an effective image segmentation method, the standard fuzzy c-means (FCM) clustering algorithm is very sensitive to noise in images. Several modified FCM algorithms, using local spatial information, can overcome this problem to some degree. However, when the noise level in the image is high, these algorithms still cannot obtain satisfactory segmentation performance. In this paper, we introduce a non local spatial constraint term into the objective function of FCM and propose a fuzzy cmeans clustering algorithm with non local spatial information (FCM_NLS). FCM_NLS can deal more effectively with the image noise and preserve geometrical edges in the image. Performance evaluation experiments on synthetic and real images, especially magnetic resonance (MR) images, show that FCM_NLS is more robust than both the standard FCM and the modified FCM algorithms using local spatial information for noisy image segmentation.     

Fuzzy c-means clustering with weighted image patch for image segmentation

Fuzzy c-means (FCM) clustering has been widely used in image segmentation. However, in spite of its computational efficiency and wide-spread prevalence, the FCM algorithm does not take the spatial information of pixels into consideration, and hence may result in low robustness to noise and less accurate segmentation. In this paper, we propose the weighted image patch-based FCM (WIPFCM) algorithm for image segmentation. In this algorithm, we use image patches to replace pixels in the fuzzy clustering, and construct a weighting scheme to able the pixels in each image patch to have anisotropic weights. Thus, the proposed algorithm incorporates local spatial information embedded in the image into the segmentation process, and hence improve its robustness to noise. We compared the novel algorithm to several state-of-the-art segmentation approaches in synthetic images and clinical brain MR studies. Our results show that the proposed WIPFCM algorithm can effectively overcome the impact of noise and substantially improve the accuracy of image segmentations.     

Automatic MR brain image segmentation using a multiseed based multiobjective clustering approach

In this paper, the automatic segmentation of a multispectral magnetic resonance image of the brain is posed as a clustering problem in the intensity space. The automatic clustering problem is thereafter modelled as solving a multiobjective optimization (MOO) problem, optimizing a set of cluster validity indices simultaneously. A multiobjective clustering technique, named MCMOClust, is used to solve this problem. MCMOClust utilizes a recently developed simulated annealing based multiobjective optimization method as the underlying optimization strategy. Each cluster is divided into several small hyperspherical subclusters and the centers of all these small sub-clusters are encoded in a string to represent the whole clustering. For assigning points to different clusters, these local sub-clusters are considered individually. For the purpose of objective function evaluation, these sub-clusters are merged appropriately to form a variable number of global clusters. Two cluster validity indices, one based on the Euclidean distance, XB-index, and another recently developed point symmetry distance based cluster validity index, Sym-index, are optimized simultaneously to automatically evolve the appropriate number of clusters present in MR brain images. A semi-supervised method is used to select a single solution from the final Pareto optimal front of MCMOClust. The present method is applied on several simulated T1-weighted, T2-weighted and proton density normal and MS lesion magnetic resonance brain images. Superiority of the present method over Fuzzy C-means, Expectation Maximization clustering algorithms and a newly developed symmetry based fuzzy genetic clustering technique (Fuzzy-VGAPS), are demonstrated quantitatively. The automatic segmentation obtained by multiseed based multiobjective clustering technique (MCMOClust) is also compared with the available ground truth information.     

Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation

A traditional approach to segmentation of magnetic resonance (MR) images is the fuzzy c-means (FCM) clustering algorithm. The efficacy of FCM algorithm considerably reduces in the case of noisy data. In order to improve the performance of FCM algorithm, researchers have introduced a neighborhood attraction, which is dependent on the relative location and features of neighboring pixels. However, determination of degree of attraction is a challenging task which can considerably affect the segmentation results.This paper presents a study investigating the potential of genetic algorithms (GAs) and particle swarm optimization (PSO) to determine the optimum value of degree of attraction. The GAs are best at reaching a near optimal solution but have trouble finding an exact solution, while PSO’s-group interactions enhances the search for an optimal solution. Therefore, significant improvements are expected using a hybrid method combining the strengths of PSO with GAs, simultaneously. In this context, a hybrid GAs/PSO (breeding swarms) method is employed for determination of optimum degree of attraction. The quantitative and qualitative comparisons performed on simulated and real brain MR images with different noise levels demonstrate unprecedented improvements in segmentation results compared to other FCM-based methods.