Anisotropic Mean Shift Based Fuzzy C-Means Segmentation of Dermoscopy Images

Evaluation of Performance of Fuzzy C Means and Mean Shift based Segmentation for Multi-Spectral Images

Image Segmentation has become very useful vision application because it can be used in many image processing applications. An image segmentation results in an images where each object is differentiated from other one. Many segmentation techniques have been proposed so far to get accurate segmentation results. This paper has focused on Mean Shift and Fuzzy C means clustering algorithm to segment...

Fuzzy c-means image segmentation of side-scan sonar images

Synthetic aperture side-scan sonar (SAS) is an imaging modality for detecting objects on the sea floor and in shallow water. SAS images provide an echo of an object along with its acoustic shadow; both of which can be used in the classification and localization of the object. We developed a Fuzzy C-Means (FCM) image segmentation algorithm that segments the echo of an object and its acoustic sha...

Segmentation of Photospheric Solar Images by Using c-Means,k-Means, and FCM Algorithms

Image Segmentation: Type–2 Fuzzy Possibilistic C-Mean Clustering Approach

Image segmentation is an essential issue in image description and classification. Currently, in many real applications, segmentation is still mainly manual or strongly supervised by a human expert, which makes it irreproducible and deteriorating. Moreover, there are many uncertainties and vagueness in images, which crisp clustering and even Type-1 fuzzy clustering could not handle. Hence, Type-...

Automated MRI brain tissue segmentation based on mean shift and fuzzy c-means using a priori tissue probability maps

This paper presents a novel fully automated unsupervised framework for the brain tissue segmentation in magnetic resonance (MR) images. The framework is a combination of Bayesian-based adaptive mean shift, a priori spatial tissue probability maps and fuzzy c-means. Mean shift is employed to cluster the tissues in the joint spatial-intensity feature space and then a fuzzy c-means is applied with...