这里提出了一种高效的基于模糊c均值(FCM)聚类的彩色图像分割方法，它利用塔形数据结构对彩色图像进行多层分割。
An efficient segmentation method based upon fuzzy c-means (FCM) clustering principles is proposed. The approach utilizes a pyramid data structure for the hierarchical ana - lysis of color images.
该文根据FCM算法和灰度图像的特点，提出了一种适用于灰度图像分割的抑制式模糊C -均值聚类算法(S - FCM)。
In the paper, a suppressed fuzzy c-means (S-FCM) algorithm, for intensity image segmentation, is proposed on the basis of the characters of FCM algorithm and intensity images.
采用自适应门限进行阈值分割，得到二值化的图像；利用聚类的方法去掉噪声点。
Binaryzation image is obtained through adaptive threshold segmentation and noise is removed by sorting method.
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。
A novel FCM segmentation algorithm is proposed based on bias field estimation with respect to the segmentation issue of defocused images with illumination patterns under bias field.
在合理选择冷却进度表的基础上，依据FCM聚类算法建立目标函数，实现了基于SA和FCM聚类的图像分割算法。
Based on choosing reasonable cooling schedule, the objection function for SA is set up according to FCM clustering, and the image segmentation algorithm based on SA and FCM clustering is implemented.
前言：目的探讨颅脑mri图像模糊聚类分割算法中最佳模糊聚类数。
Objective: To discuss the best fuzzy clustering number of MRI brain images segmentation.
模糊聚类是模糊理论的一个重要的分支，在图像分割中得到广泛应用。
Fuzzy clustering is an important branch of fuzzy set theory, and is widely applied in image segmentation.
在上面的图片，你可以看到，我是用3类，这意味着该图像分割在3个不同的颜色。
In the above image, you can see that I have used 3 clusters which means the image is segmented in 3 distinct colours.
经典的C -均值聚类算法（CMA）是将图像分割成C类的常用方法，但依赖于初始聚类中心的选择。
The classical C-means clustering algorithm (CMA) is a well-known clustering method to partition an image into homogeneous regions.
根据视觉的颜色聚类特性，提出一种图像分割算法。
Proposes an image segmentation algorithm based on perceptual color clustering.
本文给出了模糊聚类算法在图像分割中的应用结果。
In this paper, the application of suppressed fuzzy clustering algorithm in image segmentation is introduced.
算法首先对图像进行量化处理，而后在量化后的色彩空间中集成先验的分割信息进行色彩聚类。
The algorithm first has the image quantized and then clusters in the quantized color space with prior segmentation information.
这种无监督的聚类方法能够自动搜索最佳的网络输出节点数而获取图像中的目标数，从而完成对图像的自动分割。
This kind of unsupervised clustering method can search for the optimal number of output nodes automatically to get the number of textures in the 'image, and finish the automatic segmentation.
为了对低信噪比的超声图像进行有效分割，提出一种谱聚类集成的超声图像分割算法。
A novel ultrasound image segmentation algorithm, which is based on the spectral cluster ensemble, is proposed to segment ultrasound images with low SNR.
框架由四部分组成：图像输入阶段、图像特征处理阶段、聚类择优阶段和最后的分割结果。
There are four part of it: image input, dealing with image feature, clustering and choosing the best and output the result.
针对模糊核聚类对红外图像分割存在的不足，提出了一种改进的模糊核聚类红外图像分割算法。
Due to the problems of infrared image segmentation using fuzzy kernel clustering, an improved method for infrared image segmentation was proposed.
模糊c -均值聚类是模式识别中的重要算法之一，很早就被应用到图像分割中。
Fuzzy C-means clustering is one of the important learning algorithms in the field of pattern recognition, which has been applied early to image segmentation.
最小类内方差法分割图像时需要计算二次统计量，运算量大，效率不高。
The method of minimum interclass variance needs to compute conic statistic in segmenting image, which costs a large computation but has less efficiency.
目的介绍一种动态模糊聚类算法并利用该算法对磁共振图像进行分割研究。
Objective to introduce a dynamic fuzzy clustering algorithm and use it to do the study of segmentation of the brain in MRI.
结果模糊K- 均值聚类算法能很好地分割出磁共振颅脑图像中的灰质、 白质和脑脊液。
Results Fuzzy K means clustering algorithm can segment white matter, gray matter and CSF better from the MR head images.
首先，运用K-均值聚类方法提取出细胞核，并且采用多域值分割演算法去除细胞图像中的背景区域。
Firstly, nucleus regions of leukocytes in images are automatically segmented by K-mean clustering method. Then single leukocyte region is detected by utilizing thresholding algorithm segmentation.
本文处理的对象是灰度图像，分割的核心是对像素进行聚类，属于优化问题。
This thesis deals with gray image. The kernel of segmentation is pixel clustering. It belongs to optimization problem.
作为一种重要的分类器，模糊聚类技术在磁共振图像的分割中已经得到了成功的应用，并成为了一种有效的磁共振图像的分割工具。
As an important classifier, fuzzy clustering technique has been widely used in segmentation of MRI image and became an effective segmentation tool of MR image.
在自动分割模式下，我们讨论了如何提取图像的主类特征和主块特征用于图像检索。
Then the main class features and the main block features of the image are extracted which can also be used for image retrieval.
算法用于图像分割是一种非监督模糊聚类后再标定的过程。
It is a procedure of the label following an unsupervised fuzzy clustering that fuzzy c-means (FCM) algorithm is applied to image segmentation.
为了克服传统FCM算法的局限性，本文提出了一种基于空间邻域信息的二维模糊聚类图像分割方法（2DFCM）。
In order to overcome the limitation of FCM, a novel Two-dimension Fuzzy Cluster Method (2DFCM) was proposed based on the spatial information.
为了克服传统FCM算法的局限性，本文提出了一种基于空间邻域信息的二维模糊聚类图像分割方法（2DFCM）。
In order to overcome the limitation of FCM, a novel Two-dimension Fuzzy Cluster Method (2DFCM) was proposed based on the spatial information.
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