Fuzzy k-plane clustering (FkPC) is a soft plane-based clustering that efficiently clusters non-
spherically distributed data. However, the FkPC method is sensitive to noise and provides …

This paper proposed a novel 3D unsupervised spatial fuzzy-based brain MRI volume
segmentation technique in the presence of intensity inhomogeneity and noise. Instead of …

Objective Accurate brain tissue segmentation from magnetic resonance (MR) images is an
essential step in quantitative brain image analysis, and hence has attracted extensive …

Precise brain tissue segmentation and analysis in the presence of intensity non-uniformity
(INU) and noise is the challenging task due to intensity overlaps between data pixels within …

This paper is concerned with Magnetic Resonance (MR) brain image segmentation using
Adaptively Regularized Kernel-Based Fuzzy C-Means (ARKFCM) clustering algorithm …

Background: The brain magnetic resonance imaging (MRI) image segmentation method
mainly refers to the division of brain tissue, which can be divided into tissue parts such as …

In the diagnosis of clinical medicine, medical image processing plays a vital role and has
become a hot issue in image processing. Magnetic resonance imaging not only provides …

The fuzzy C-means (FCM) clustering procedure is an unsupervised form of grouping the
homogenous pixels of an image in the feature space into clusters. A brain magnetic …

In this paper, an intuitionistic center-free fuzzy c-means clustering method (ICFFCM) is
proposed for magnetic resonance (MR) brain image segmentation. First, in order to …

This paper presents a novel idea of intracranial segmentation of magnetic resonance (MR)
brain image using pixel intensity values by optimum boundary point detection (OBPD) …