In this study, we present a comprehensive comparative analysis of kernel-based fuzzy clustering and fuzzy clustering. Kernel based clustering has emerged as an interesting and quite visible alternative in fuzzy clustering, however, the effectiveness of this extension vis-à-vis some generic methods of fuzzy clustering has neither been discussed in a complete manner nor the performance of cluster...

Among available level set based methods in image segmentation, Fast Two Cycle (FTC) model is efficient and also the fastest one. But its efficiency is highly dependent to contour initialization. This paper tries to improve this method by using a kernel-based fuzzy c-means (KFCM) clustering algorithm. The proposed approach consists of two successive stages for image segmentation. Firstly, the KF...

Clustering is the process of grouping data objects into set of disjointed classes called clusters so that objects within a class are highly similar to one another and dissimilar to the objects in other classes. K-means (KM) and Fuzzy c-means (FCM) algorithms are popular and powerful methods for cluster analysis. However, the KM and FCM algorithms have considerable trouble in a noisy environment...

Images are imitations of factual world substances. Processing it to get better visualization is called as image processing. With the increasing availability and decreasing cost of satellite imagery, the Remote sensing image enhancement, segmentation and classification has become the most important research issue in field of Remote sensing. In this proposed work, Land sat 7 Remote Sensing images...

In this paper, we present alternative Kernelized FCM algorithms (KFCM) that could improve magnetic resonance imaging (MRI) segmentation. Then we implement the proposed KFCM method with considering some spatial constraints on the objective function. The algorithms incorporate spatial information into the membership function and the validity procedure for clustering. We use the intra-cluster dist...

Fuzzy c-means clustering algorithm (FCM) is a method that is frequently used in pattern recognition. It has the advantage of giving good modeling results in many cases, although, it is not capable of specifying the number of clusters by itself. Aimed at the problems existed in the FCM clustering algorithm, a kernelbased fuzzy c-means (KFCM) is clustering algorithm is proposed to optimize fuzzy ...

Mono-nuclear kernel function is presented in this paper based on the fuzzy c-means clustering algorithm for data clustering to do the improvement in the field of data mining, puts forward the fuzzy c-means clustering algorithm based on multiple kernel function (MKFCM) algorithm. Under fully unsupervised learning method, a set of Gaussian kernel function combination are assigned different weight...

Using thresholding method to segment an image, a fixed threshold is not suitable if the background is rough here, we propose a new adaptive thresholding method using KFCM. The method requires only one parameter to be selected and the adaptive threshold surface can be found automatically from the original image. An adaptive thresholding scheme using adaptive tracking and morphological filtering....

Classical fuzzy C -means (FCM) clustering is performed in the input space, given the desired number of clusters. Although it has proven effective for spherical data, it fails when the data structure of input patterns is non-spherical and complex. In this paper, we present a novel kernel-based fuzzy C-means clustering algorithm (KFCM). Its basic idea is to transform implicitly the input data int...

Tissue segmentation and visualization are useful for breast lesion detection and quantitative analysis. In this paper, a 3D segmentation algorithm based on Kernel-based Fuzzy C-Means (KFCM) is proposed to separate the breast MR images into different tissues. Then, an improved volume rendering algorithm based on a new transfer function model is applied to implement 3D breast visualization. Exper...