Brain image segmentation is one of the most important parts of clinical diagnostic tools.
Brain images mostly contain noise, inhomogeneity and sometimes deviation. Therefore …

Automated brain tumor detection from MRI images is one of the most challenging task in
today's modern Medical imaging research. Magnetic Resonance Images are used to …

This paper presents a robust fuzzy clustering algorithm for the segmentation of brain tissues
in magnetic resonance imaging (MRI). The proposed method incorporates context-aware …

Breast cancer is one of the leading causes of female mortality in the world, and early
detection is an important means of reducing the mortality rate. The presence of …

Image segmentation plays a major role in medical imaging applications. During last
decades, develo** robust and efficient algorithms for medical image segmentation has …

Segmentation of brain MRI images becomes a challenging task due to spatially distributed
noise and uncertainty present between boundaries of soft tissues. In this work, we have …

Intensity inhomogeneity is a smooth intensity change inside originally homogeneous
regions. Filter-based inhomogeneity correction methods have been commonly used in …

FCM does not use spatial information in clustering process. Therefore, it is not robust against
noise and other imaging artefacts. In order to incorporate spatial information, an extension …

Segmentation of images, obtained by Magnetic Resonance Imaging (MRI), is a difficult task
due to the inherent noise and inhomogeneity. This paper presents a technique to segment …

This study uses an empirical study of the efficiency of Particle swarm optimization (PSO) in
segmentation of brain abnormalities. Presently, segmentation poses one of the most …