Background: Variations of image segmentation techniques, particularly those used for Brain
MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more …

Cluster analysis is a data mining technique that has been widely used to exploit useful
information in a great amount of data. Because of their evaluation mechanism based on an …

This paper proposes a new fuzzy approach for the automatic segmentation of normal and
pathological brain magnetic resonance imaging (MRI) volumetric datasets. The proposed …

Cluster analysis plays an important role in many areas of science, and clustering algorithms
and cluster validation are two essential elements. Before clustering, the number of clusters is …

In this paper, we determine the suitable validity criterion of kernelized fuzzy C-means and
kernelized fuzzy C-means with spatial constraints for automatic segmentation of magnetic …

Background Image segmentation is considered an important step in image processing.
Fuzzy c-means clustering is one of the common methods of image segmentation. However …

In this paper, we present alternative Kernelized FCM algorithms (KFCM) that could improve
magnetic resonance imaging (MRI) segmentation. Then we implement the proposed KFCM …

In this paper, a new dynamic clustering approach based on the harmony search algorithm
(HS) called DCHS is proposed. In this algorithm, the capability of standard HS is modified to …

We propose a new approach to tackle the well known fuzzy c-means (FCM) initialization
problem. Our approach uses a metaheuristic search method called Harmony Search (HS) …

Image processing of digital images is one of the essential categories of image
transformation in the theory and practice of digital pattern analysis and computer vision …