A 3D rib cage model helps to study anatomical structures in some medical applications such as biomechanical and surgical operations. Its quality directly depends on rib cage segmentation if it is reconstructed from image data. This paper presents an optional segmentation method based on K-means clustering. It uses a hierarchical concept to control the clustering, and it organizes clustered regi...

Up to now, several algorithms for clustering large data sets have been presented. Most clustering approaches for data sets are the crisp ones, which cannot be well suitable to the fuzzy case. In this paper, the authors explore a single pass approach to fuzzy possibilistic clustering over large data set. The basic idea of the proposed approach (weighted fuzzy-possibilistic c-means, WFPCM) is to ...

The management and analysis of big data has been identified as one of the most important emerging needs in recent years. This is because of the sheer volume and increasing complexity of data being created or collected. Current clustering algorithms can not handle big data, and therefore, scalable solutions are necessary. Since fuzzy clustering algorithms have shown to outperform hard clustering...

IntroductIon By definition, image segmentation represents the partitioning of an image into nonoverlapping, consistent regions, which appear to be homogeneous with respect to some criteria concerning gray level intensity and/or texture. The fuzzy c-means (FCM) algorithm is one of the most widely used method for data clustering, and probably also for brain image segmentation (Bezdek & Pal., 1991...

Clustering is a popular data analysis and data mining technique. In this paper, a novel chaotic particle swarm fuzzy clustering (CPSFC) algorithm based on chaotic particle swarm (CPSO) and gradient method is proposed. Fuzzy clustering model optimization is challenging, in order to solve this problem, adaptive inertia weight factor (AIWF) and iterative chaotic map with infinite collapses (ICMIC)...

One of the main drawbacks of the FCM clustering algorithm is that it does not calculate the suitable number of clusters. This paper presents a method to solve this problem, by means of an equalization function (using uniform data) for the FCM functional J. The results for 2 and 3 dimensional data tests are also presented.

information from the original image as compared with crisp or hard segmentation methods. In practice, noisy images (even high noise images) are very common. It's very essential and critical to deal with such images to process real-image segmentation and pattern recognition. In this paper, differences of credibilistic clustering algorithm (CCA) and fuzzy c-means algorithm (FCM) in dealing with n...

Image segmentation plays a significant role in computer vision. It aims at extracting meaningful objects lying in the image. Generally there is no unique method or approach for image segmentation. Clustering is a powerful technique that has been reached in image segmentation. The cluster analysis is to partition an image data set into a number of disjoint groups or clusters. The clustering meth...

This paper presents an approach to medical image registration using a segmentation step segmentation based on Fuzzy C-Means (FCM) clustering and the Scale Invariant Feature Transform (SIFT) for matching keypoints in segmented regions. To obtain robust segmentation, FCM is applied on feature vectors composed by local information invariant to image scaling and rotation, and to change in illuminat...

FCM-type cluster validation is a technique for searching for the optimal fuzzy partition, in which the number of clusters is evaluated by considering the degree of overlapping of fuzzy memberships, cluster compactness or cluster separation. In this paper, a new approach for FCM-type cluster validation in fuzzy co-clustering is proposed. Because fuzzy co-clustering does not use cluster prototype...