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《计算机辅助设计与图形学学报》2014,(9)
核磁共振图像技术可用于对疾病的辅助诊断,然而受成像机制的影响往往图像中含有噪声以及偏移场,使得传统的模糊C均值(FCM)算法很难得到较好的分割结果.为此,提出一种基于FCM算法的分割与偏移场恢复耦合模型.首先将偏移场耦合到模型中,以降低灰度不均匀对分割的影响;其次将非局部信息融入模型中,使其在降低噪声影响的同时还能保持细长拓扑结构区域信息;最后引入隶属度正则项,以降低隶属度在过渡区域的影响,改善模型的分割效果.实验结果证明,文中模型对噪声具有较好的鲁棒性,并且在分割过程中能较好地恢复图像偏移场,得到较理想的分割结果及偏移场估计. 相似文献
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由于磁共振图像(magnetic resonance images,MRI)常含有偏移场而影响后继图像分割,针对这种图像的分割,采用Legendre多项式基函数来拟合偏移场,可以去除偏移场对图像分割的影响。当使得恢复图像的信息熵达到最小时,则求得的偏移场最优。在求偏移场的过程中,需要求解基函数的参数,由于传统的梯度下降法易陷入局部最优,为解决此问题,提出将遗传算法引入到参数求解过程中,然而传统的遗传算法不仅时间复杂度高,且易陷入局部最优,为此需对遗传算法进行改进,使得不仅更容易得到全局最优解,且时间复杂度较低。实验证明,该改进算法可以得到精确的偏移场,并可得到准确的分割结果。 相似文献
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结合非局部信息的脑MR图像分割与偏移场恢复耦合模型 总被引:1,自引:0,他引:1
磁共振图像由于成像机制的影响往往导致图像中含有噪声和偏移场,使得传统方法很难得到较好的分割结果.为此,在模糊C均值模型的基础上提出一种分割与偏移场恢复耦合模型.首先构建基于非局部信息的邻域正则项,使得在降低噪声影响的同时能有效地保留图像结构信息;其次在模型求解时引入人工蜂群算法,使得模型能快速逼近凸优解.实验结果表明,该模型对噪声和偏移场均具有较好的鲁棒性,可得到较准确的分割和偏移场矫正结果. 相似文献
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核磁共振图像受成像机制的影响往往导致图像中含有噪声以及偏移场,使得传统的图像分割方法很难得到较好的分割结果.为此,提出一种基于局部熵的分割与偏移场恢复耦合模型,首先在小邻域内构建基于模糊C均值(FCM)聚类模型的局部统计项并将偏移场信息耦合到模型中,以恢复图像偏移场;其次采用非局部信息来构建邻域正则项,使得模型在降低噪声影响的同时能有效地保留图像结构信息;最后在对局部能量项进行全局积分时引入局部熵信息,使得模型具有各向异性,从而对噪声和偏移场影响更具鲁棒性.实验结果表明,本文方法可以得到较准确的分割和偏移场矫正结果. 相似文献
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由于磁共振图像(Magnetic Resonance Images,MRI)常含有偏移场,影响后继图像分割。采用Legendre多项式基函数来拟合偏移场,以去除偏移场对图像分割的影响。当使得恢复图像的信息熵达到最小时,求得的偏移场最优。求偏移场的过程中需要求解基函数的参数,由于传统的梯度下降法易陷入局部最优,将遗传算法引入到参数求解过程中,然而传统的遗传算法时间复杂度高,易陷入局部最优,对遗传算法进行了改进,更容易得到全局最优解且时间复杂度较低。实验证明该算法可以得到精确的偏移场,得到准确的分割结果。 相似文献
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针对偏置环境下图像分割问题,提出了一种基于偏置场估计的模糊聚类算法。通过建立依赖于偏置场的模糊聚类目标函数,提出了模糊聚类隶属函数和偏置场估计的迭代算法。该方法较好地处理了传统模糊聚类在偏置场存在的情况下图像分割精度下降问题。实验结果表明,该算法能有效分割具有偏置噪声的图像,其分割精度优于传统模糊聚类法。 相似文献
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生物医学图像分析可以辅助医生诊断疾病,然而,图像中常含有噪声以及灰度不均匀现象,使得传统的图像分割方法不能得到满意的结果。针对这些问题,构造一种基于图像区域信息的偏移场恢复耦合模型,使得模型可以在分割的同时恢复出图像偏移场。为了得到全局最优解并提高算法效率,将该模型改进成1范数下的凸函数,并使用基于Split-Bregman方法对该耦合模型进行快速求解。实验结果表明,本文方法可以降低噪声和灰度不均匀的影响,得到较准确的分割结果和偏移场信息,而且大大地降低了计算复杂度。 相似文献
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针对脑部MR图像中通常伴有灰度不均、高噪声的缺点,且传统水平集无法有效分割的问题,提出了一种基于NL-Means的双水平集算法。首先,利用改进型NL-Means算法对带有噪声的医学图像进行去噪处理,再通过双水平集算法对图像进行分割,提取多目标区域,为了去除医学图像中灰度不均对分割效果的影响,所提算法引入了偏移场拟合项,进一步改进了双水平集模型,进而对去噪图像分割效果进行了优化处理。实验结果表明,所提算法能有效地解决灰度不均与高噪声的问题,能够将伴有灰度不均的高噪声脑部MR图像完全分割出来,从而获得预期的分割效果。 相似文献
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S.R. Kannan Author Vitae A. Sathya Author Vitae Author Vitae R. Devi Author Vitae 《Journal of Systems and Software》2010,83(12):2487-2495
The aim of this paper is to develop an effective fuzzy c-means (FCM) technique for segmentation of Magnetic Resonance Images (MRI) which is seriously affected by intensity inhomogeneities that are created by radio-frequency coils. The weighted bias field information is employed in this work to deal the intensity inhomogeneities during the segmentation of MRI. In order to segment the general shaped MRI dataset which is corrupted by intensity inhomogeneities and other artifacts, the effective objective function of fuzzy c-means is constructed by replacing the Euclidean distance with kernel-induced distance. In this paper, the initial cluster centers are assigned using the proposed center initialization algorithm for executing the effective FCM iteratively. To assess the performance of proposed method in comparison with other existed methods, experiments are performed on synthetic image, real breast and brain MRIs. The clustering results are validated using Silhouette accuracy index. The experimental results demonstrate that our proposed method is a promising technique for effective segmentation of medical images. 相似文献
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Benoît Caldairou Author Vitae Nicolas Passat Author Vitae Author Vitae Colin Studholme Author Vitae Author Vitae 《Pattern recognition》2011,44(9):1916-1927
The Fuzzy C-Means (FCM) algorithm is a widely used and flexible approach to automated image segmentation, especially in the field of brain tissue segmentation from 3D MRI, where it addresses the problem of partial volume effects. In order to improve its robustness to classical image deterioration, namely noise and bias field artifacts, which arise in the MRI acquisition process, we propose to integrate into the FCM segmentation methodology concepts inspired by the non-local (NL) framework, initially defined and considered in the context of image restoration. The key algorithmic contributions of this article are the definition of an NL data term and an NL regularisation term to efficiently handle intensity inhomogeneities and noise in the data. The resulting new energy formulation is then built into an NL-FCM brain tissue segmentation algorithm. Experiments performed on both synthetic and real MRI data, leading to the classification of brain tissues into grey matter, white matter and cerebrospinal fluid, indicate a significant improvement in performance in the case of higher noise levels, when compared to a range of standard algorithms. 相似文献
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A Modified Fuzzy C-Means Algorithm for Brain MR Image Segmentation and Bias Field Correction
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In quantitative brain image analysis, accurate brain tissue segmentation from brain magnetic resonance image (MRI) is a critical step. It is considered to be the most important and difficult issue in the field of medical image processing. The quality of MR images is influenced by partial volume effect, noise, and intensity inhomogeneity, which render the segmentation task extremely challenging. We present a novel fuzzy c-means algorithm (RCLFCM) for segmentation and bias field correction of brain MR images. We employ a new gray-difference coefficient and design a new impact factor to measure the effect of neighbor pixels, so that the robustness of anti-noise can be enhanced. Moreover, we redefine the objective function of FCM (fuzzy c-means) by adding the bias field estimation model to overcome the intensity inhomogeneity in the image and segment the brain MR images simultaneously. We also construct a new spatial function by combining pixel gray value dissimilarity with its membership, and make full use of the space information between pixels to update the membership. Compared with other state-of-the-art approaches by using similarity accuracy on synthetic MR images with different levels of noise and intensity inhomogeneity, the proposed algorithm generates the results with high accuracy and robustness to noise. 相似文献
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脑MR图像中普遍存在灰度不均匀性,传统的分割方法无法得到理想的脑组织分割结果.为此提出一种基于互信息最大化准则的变分水平集凸优化分割模型.首先建立最大化图像灰度与标记之间互信息能量的分割模型,并融入偏移场信息;对模型进行水平集表示和凸优化后,再引入边缘指示函数加权的总变差范数;最后采用SplitBregman方法快速求解.实验结果表明,该模型可以得到较准确的脑组织分割和偏移场矫正结果,对噪声和灰度不均匀性有很好的鲁棒性. 相似文献
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脑部MRI的快速准确分割是脑部疾病临床诊断过程的关键步骤之一。针对FCM算法部分参数设置影响分割结果和鲁棒性差的缺陷,提出一种基于非局部空间信息的快速模糊C均值核聚类改进算法,并应用于脑部MRI分割中。依次通过直方图、K-means算法、核函数、基于积分图的非局部空间信息解决了部分初始参数值难以控制、抗噪性差和运算效率低等问题。实验表明,该算法错分率低至2.0%,运行时间平均减少至13.89 s。 相似文献
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多分辨率双水平集医学图像分割算法 总被引:1,自引:1,他引:0
由于医学图像通常伴有灰度不均、背景复杂的特点,传统水平集无法对其进行有效分割,因此提出了一种多分辨率改进型双水平集算法。首先,利用小波进行多尺度空间分析,从而获取医学图像的粗尺度图像;然后由改进型双水平集对图像进行分割,提取多目标区域;为了去除医学图像中灰度不均对分割效果的影响,该算法引入偏移场拟合项,以进一步改进双水平集模型,进而对粗尺度分割效果进行优化处理。实验结果表明,所提算法能有效地解决灰度不均与背景复杂的问题,将伴灰度不均的多目标医学图像完全分割出来,从而获得预期的分割效果。 相似文献
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Zexuan Ji Yong Xia Qiang Chen Quansen Sun Deshen Xia David Dagan Feng 《Applied Soft Computing》2012,12(6):1659-1667
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. 相似文献
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基于隶属度光滑约束的模糊C均值聚类算法 总被引:5,自引:0,他引:5
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。 相似文献