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.     

Robust image segmentation using resampling and shape constraints

Automated segmentation of images has been considered an important intermediate processing task to extract semantic meaning from pixels. We propose an integrated approach for image segmentation based on a generative clustering model combined with coarse shape information and robust parameter estimation. The sensitivity of segmentation solutions to image variations is measured by image resampling. Shape information is included in the inference process to guide ambiguous groupings of color and texture features. Shape and similarity-based grouping information is combined into a semantic likelihood map in the framework of Bayesian statistics. Experimental evidence shows that semantically meaningful segments are inferred even when image data alone gives rise to ambiguous segmentations.     

峰值检测 FCM 算法的医学图像分割

为了更好地平衡传统FCM及其相关改进算法的分割效果与分割效率问题,提出了一种基于峰值检测的快速FCM图像分割算法。首先基于峰值检测策略对聚类中心进行初始化;然后在初始化聚类中心的基础上对医学图像进行分割。其本质是运用峰值检测技术指导聚类中心的初始化,以使初始化的聚类中心尽可能靠近最终的聚类中心,从而以提高算法的工作效率。在医学图像上进行的实验表明,算法可以有效地提高图像分割的效率,并能得到很好的分割效果。     

Pavement image segmentation based on fast FCM clustering with spatial information in internet of things

Pavement image segmentation needs to deal with noise spots and has real time requirement. The original FCM method only considers the pixel’s gray value and doesn’t fully utilize the spatial information of the image. A new fast FCM algorithm is proposed, and it has noise immunity. By comparing with other FCM algorithms, it achieves better segmentation results through less iteration times and more rapid runtime. It is an effective and noise-resistant algorithm for pavement image segmentation from video multimedia in IOT (internet of things) platform.

     

Color image segmentation using feedforward neural networks with FCM

This paper proposes a hybrid technique for color image segmentation. First an input image is converted to the image of CIE L*a*b* color space. The color features “a” and “b” of CIE L*a*b* are then fed into fuzzy C-means (FCM) clustering which is an unsupervised method. The labels obtained from the clustering method FCM are used as a target of the supervised feed forward neural network. The network is trained by the Levenberg-Marquardt back-propagation algorithm, and evaluates its performance using mean square error and regression analysis. The main issues of clustering methods are determining the number of clusters and cluster validity measures. This paper presents a method namely co-occurrence matrix based algorithm for finding the number of clusters and silhouette index values that are used for cluster validation. The proposed method is tested on various color images obtained from the Berkeley database. The segmentation results from the proposed method are validated and the classification accuracy is evaluated by the parameters sensitivity, specificity, and accuracy.     

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。     

基于GLCM特征的改进FCM的SAR图像分割方法

为了克服了较大窗口提取图像边缘处特征值的不足,提出一种基于GLCM特征矩阵的动态滑动窗口算法.针对模糊C均值算法中,聚类中心不容易确定,聚类容易陷入局部最优解的问题,将粒子群优化算法(PSO)引入到聚类算法中,实现全局搜索.应用改进的模糊C均值算法完成了基于SAR纹理特征的图像分割,克服了传统聚类算法仅依赖灰度值进行分割的局限性,也一定程度上克服了斑噪声对SAR图像分割的影响.实验结果表明,该方法应用于SAR图像分割时,取得了很好的分割效果.     

二型模糊融合的尺度约束多分辨率FCM分割

针对传统多分辨率模糊聚类图像分割算法的不足,提出了将二型模糊应用于多分辨率模糊聚类图像分割的新方法.将最粗尺度图像的聚类中心作为下一较细分辨率图像的初始聚类中心,并采用较粗分辨率图像聚类的类内最大距离对细分辨率图像的模糊聚类目标函数进行约束.对较小的粗分辨率图像进行了模糊隶属度扩展,得到一组隶属度值,再采用二型模糊算法有效融合该隶属度集合,完成聚类分割.实验结果表明,该算法能有效实现目标区域分离,获得理想分割效果.     

一种基于自适应空间信息改进FCM的图像分割算法

为了更好地改善图像分割效果,提出一种自适应空间信息的模糊聚类算法(adaptive spatial information fuzzy clustering,ASIFC).算法将图像空间信息与FCM算法相结合,改进了FCM算法的目标函数;使用信息最大化识别噪声数据和消除异常值.在合成图像和核磁共振脑部图像数据库Brainweb上的实验结果表明,该算法能自适应地实现图像分割,有效识别噪声数据,解决了FCM的空间信息缺乏问题,增强了算法的鲁棒性,相比其他几种较新的聚类算法,取得了更好的分割效果.