Semi-supervised statistical region refinement for color image segmentation

Some authors have recently devised adaptations of spectral grouping algorithms to integrate prior knowledge, as constrained eigenvalues problems. In this paper, we improve and adapt a recent statistical region merging approach to this task, as a non-parametric mixture model estimation problem. The approach appears to be attractive both for its theoretical benefits and its experimental results, as slight bias brings dramatic improvements over unbiased approaches on challenging digital pictures.     

融入空间信息的医学图像优质分割

传统的FCM分割算法只考虑到图像的灰度信息,而忽略了灰度的空间信息,对于迭加了噪声的图像,难以得到准确的结果。从马尔可夫随机场（MRF）中得到启示,考虑到图像灰度信息及其空间分布出发,提出了一种新的基于邻域（Neighbor）信息FCM分割算法,即NFCM算法。实验结果表明该算法所得到的目标图像的边界特征保持完好,图像边界细腻、连续且定位性能好。     

A fast and robust image segmentation using FCM with spatial information

Automated segmentation of images has been considered an important intermediate processing task to extract semantic meaning from pixels. In general, the fuzzy c-means approach (FCM) is highly effective for image segmentation. But for the conventional FCM image segmentation algorithm, cluster assignment is based solely on the distribution of pixel attributes in the feature space, and the spatial distribution of pixels in an image is not taken into consideration. In this paper, we present a novel FCM image segmentation scheme by utilizing local contextual information and the high inter-pixel correlation inherent. Firstly, a local spatial similarity measure model is established, and the initial clustering center and initial membership are determined adaptively based on local spatial similarity measure model. Secondly, the fuzzy membership function is modified according to the high inter-pixel correlation inherent. Finally, the image is segmented by using the modified FCM algorithm. Experimental results showed the proposed method achieves competitive segmentation results compared to other FCM-based methods, and is in general faster.     

Fuzzy spectral clustering with robust spatial information for image segmentation

In recent years, spectral clustering has become one of the most popular clustering algorithms in areas of pattern analysis and recognition. This algorithm uses the eigenvalues and eigenvectors of a normalized similarity matrix to partition the data, and is simple to implement. However, when the image is corrupted by noise, spectral clustering cannot obtain satisfying segmentation performance. In order to overcome the noise sensitivity of the standard spectral clustering algorithm, a novel fuzzy spectral clustering algorithm with robust spatial information for image segmentation (FSC_RS) is proposed in this paper. Firstly, a non-local-weighted sum image of the original image is generated by utilizing the pixels with a similar configuration of each pixel. Then a robust gray-based fuzzy similarity measure is defined by using the fuzzy membership values among gray values in the new generated image. Thus, the similarity matrix obtained by this measure is only dependent on the number of the gray-levels and can be easily stored. Finally, the spectral graph partitioning method can be applied to this similarity matrix to group the gray values of the new generated image and then the corresponding pixels in the image are reclassified to obtain the final segmentation result. Some segmentation experiments on synthetic and real images show that the proposed method outperforms traditional spectral clustering methods and spatial fuzzy clustering in efficiency and robustness.     

彩色互信息在细胞图像分割中的应用

基于均值聚类的彩色图像分类数的选择对细胞图像的分割具有至关重要的作用,为了准确并快速得出k均值聚类的最佳聚类数,提出了一种基于彩色互信息的聚类数选取法。对原图像通过选择m个不同的聚类数k值得到m幅彩色聚类效果图,利用信息熵理论将m幅聚类图片分别与原图像进行互信息熵计算,同时将第k幅聚类图像与第k+1幅聚类图像进行互信息熵计算,通过设定k相关互信息差的阈值以确定最佳聚类数。实验结果表明,该方法具有较好的分割效果和运算效率。     

Spatial color histogram-based image segmentation using texture-aware region merging

We propose a new image segmentation method using spatial-color histograms that include the color and spatial information of a given image. Previous methods used a histogram with only the color information of the image or did not effectively suppress the texture components of the same object to form segmented regions, and they frequently led to the false merging of two different regions. Thus, these methods caused an over-segmentation result in the same object or an under-segmentation result in the regional boundary between two different objects. To resolve these problems, the proposed method performs a clustering that considers both color and spatial information of the image in the histogram domain and texture-aware region merging. Moreover, using a total variation-based regularizer that can remove the texture components in the same object and preserve the edge components between different objects, we improve the accuracy of region merging process that is applied to the result of the proposed histogram-based segmentation. Compared to the best results obtained using previous histogram-based methods, the proposed method achieved improvements of 0.02335 (2.910%), 0.0195 (3.977%), 0.05515 (2.431%), and 0.9639 (9.250%) in probability rand index, segmentation covering, variation of information, and boundary displacement error, which are the most widely used for segmentation evaluation metrics, respectively. Further, when compared to the state-of-the-art methods, which use the superpixel, iterative contraction and merging, and deep learning-based methods, the proposed method provides promising segmentation quality with fast operation speed.

     

The integration of directional information and local region information for accurate image segmentation

The original local binary fitting (LBF) model is sensitive to contour initialization and thus easily obtains an inaccurate result due to improper initialization. This paper presents a new method that not only can arrive at sub-pixel accuracy, but also allows for more flexible initialization of the contour. Two important terms play main role in our new method. One is an image gradient alignment term (IGA) which uses the directional information of the image gradient, the other is a local intensity fitting term (LIF) which makes use of local region information. The integration of the above two terms prevents our method from being sensitive to contour initialization. In addition, a global intensity fitting term (GIF) multiplied by a stopping function is included, which can speed up our algorithm while do not influence the accuracy of the segmentation result. Using the simple central difference, the gradient descend flow equation for the level set function can be easily and efficiently implemented. The results on several synthetic and real images demonstrate the effectiveness and accuracy of our method.     

Kernel generalized fuzzy c-means clustering with spatial information for image segmentation

The generalized fuzzy c-means clustering algorithm with improved fuzzy partition (GFCM) is a novel modified version of the fuzzy c-means clustering algorithm (FCM). GFCM under appropriate parameters can converge more rapidly than FCM. However, it is found that GFCM is sensitive to noise in gray images. In order to overcome GFCM?s sensitivity to noise in the image, a kernel version of GFCM with spatial information is proposed. In this method, first a term about the spatial constraints derived from the image is introduced into the objective function of GFCM, and then the kernel induced distance is adopted to substitute the Euclidean distance in the new objective function. Experimental results show that the proposed method behaves well in segmentation performance and convergence speed for gray images corrupted by noise.     

自适应加权空间信息的FCM医学图像分割

提出了改进的mFCM算法,该算法引入自适应加权系数控制邻域像素对中心像素的影响程度,充分利用像素的邻域特性对Chen聚类算法的目标函数进行改进。为了实现快速聚类,该算法的开始使用快速FCM确定初始聚类中心。实验结果表明,相对于标准FCM和FCM_S1算法,改进算法既能快速有效地分割图像,又能提高对噪声的鲁棒性。     

A new approach for color image segmentation based on color mixture

The aim of this paper is to propose a new methodology for color image segmentation. We have developed an image processing technique, based on color mixture, considering how painters do to overlap layers of various hues of paint on creating oil paintings. We also have evaluated the distribution of cones in the human retina for the interpretation of these colors, and we have proposed a schema for the color mixture weight. This method expresses the mixture of black, blue, green, cyan, red, magenta, yellow and white colors quantified by the binary weight of the color that makes up the pixels of an RGB image with 8 bits per channel. The color mixture generates planes that intersect the RGB cube, defining the HSM (Hue, Saturation, Mixture) color space. The position of these planes inside the RGB cube is modeled, based on the distribution of r, g and b cones of the human retina. To demonstrate the applicability of the proposed methodology, we present in this paper, the segmentation of “human skin” or “non-skin” pixels in digital color images. The performance of the color mixture was analyzed by a Gaussian distribution in the HSM, HSV and YCbCr color spaces. The method is compared with other skin/non-skin classifiers. The results demonstrate that our approach surpassed the performance of all compared methodologies. The main contributions of this paper are related to a new way for interpreting color of binary images, taking into account the bit-plane levels and the application in image processing techniques.     

一种织物彩色图像的分割算法

提出了一种针对织物彩色图像的分割算法。该算法在颜色聚类的基础上,把每种颜色用 不同的序号来表示,生成一个与原图同大小的序号矩阵,然后根据矩阵中序号的分布进行初始分割并 产生种子区域,再把初始分割未能确定的像素指派到种子区域中,最后根据颜色信息合并过分割的区 域,并对区域边缘进行平滑,得到最终的分割结果。实验表明,该算法能较有效地实现各类彩色织物 图像的分割。     

Pixel clustering for color image segmentation

Programming and Computer Software - Image segmentation using a hierarchical sequence of piecewise constant approximations that minimally differ from the original image in terms of the total squared...     

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.     

A top-down region dividing approach for image segmentation

Histogram-based and region-based segmentation approaches have been widely used in image segmentation. Difficulties arise when we use these techniques, such as the selection of a proper threshold value for the histogram-based technique and the over-segmentation followed by the time-consuming merge processing for the region-based technique. To provide efficient algorithms that not only produce better segmentation results but also maintain low computational complexity, a novel top-down region dividing based approach is developed for image segmentation, which combines the advantages of both histogram-based and region-based approaches. Experimental results show that our algorithm can efficiently perform image segmentation without distorting the spatial structure of an image. Furthermore, two potential applications in medical image analysis are presented to show the advantages of using the proposed algorithm.     

A novel self-learning weighted fuzzy local information clustering algorithm integrating local and non-local spatial information for noise image segmentation

Applied Intelligence - Fuzzy clustering algorithm (FCM) can be directly used to segment images, it takes no account of the neighborhood information of the current pixel and does not have a robust...     

A multiobjective spatial fuzzy clustering algorithm for image segmentation

This article describes a multiobjective spatial fuzzy clustering algorithm for image segmentation. To obtain satisfactory segmentation performance for noisy images, the proposed method introduces the non-local spatial information derived from the image into fitness functions which respectively consider the global fuzzy compactness and fuzzy separation among the clusters. After producing the set of non-dominated solutions, the final clustering solution is chosen by a cluster validity index utilizing the non-local spatial information. Moreover, to automatically evolve the number of clusters in the proposed method, a real-coded variable string length technique is used to encode the cluster centers in the chromosomes. The proposed method is applied to synthetic and real images contaminated by noise and compared with k-means, fuzzy c-means, two fuzzy c-means clustering algorithms with spatial information and a multiobjective variable string length genetic fuzzy clustering algorithm. The experimental results show that the proposed method behaves well in evolving the number of clusters and obtaining satisfactory performance on noisy image segmentation.     

基于均值漂移与空间信息的导向模糊C均值遥感图像分割算法

作为图像数据结构分割的重要工具,模糊C均值已被广泛应用于计算机视觉领域。然而模糊C均值在图像分割过程中不能有效地保留边缘和抑制噪声,往往得不到理想的分割结果。为解决这一问题,本文利用导向滤波器推导出一种新的改进模糊C均值算法。该算法的第一个创新点是其线性平移不变滤波过程,利用边缘保持平滑特性来保留分割中的边缘结构。第二个创新点是该技术通过将空间信息引入目标函数来改善对噪声的鲁棒性,空间信息通过导向滤波的平均输出获得。为了解决聚类算法中初始聚类中心问题,在图像分割过程中使用均值漂移算法选取初始聚类中心。本文方法的主要优点在于其对边缘保留和噪声具有鲁棒性,进而提高分割精度。基于合成图像和真实遥感图像的实验结果表明,与其他主流分割算法相比,该方法在分割性能方面表现出了良好的性能。