The Expectation Maximization (EM) algorithm and the clustering method Fuzzy-C-Means
(FCM) are widely used in image segmentation. However, the major drawback of these …

Fuzzy c‐means (FCMs) with spatial constraints have been considered as an effective
algorithm for image segmentation. The well‐known Gaussian mixture model (GMM) has also …

To overcome the noise sensitiveness of conventional fuzzy c-means (FCM) clustering
algorithm, a novel extended FCM algorithm for image segmentation is presented in this …

Image segmentation is a low-level processing operation; it is the basis for many applications
such as industrial vision and medical imaging. Segmentation provides a partition of the …

Fuzzy c-means (FCM) algorithms have been shown effective for image segmentation. A
series of enhanced FCM algorithms incorporating spatial information have been developed …

This paper presents a new technique for incorporating local membership information into the
standard fuzzy C-means (FCM) clustering algorithm. In this technique, the objective consists …

Fuzzy clustering techniques, especially fuzzy c-means (FCM) clustering algorithm, have
been widely used in automated image segmentation. However, as the conventional FCM …

Segmentation of images has found widespread applications in image recognition systems.
Over the last two decades, there has been a growing research interest in model-based …

Fuzzy c-means (FCM) algorithm is one of the most popular methods for image segmentation.
However, the standard FCM algorithm is sensitive to noise because of not taking into …

Image segmentation is an essential processing step for many image analysis applications.
In this paper, a novel image segmentation algorithm using fuzzy C-means clustering (FCM) …