The high resolution magnetic resonance (MR) brain images contain some non-brain tissues
such as skin, fat, muscle, neck, and eye balls compared to the functional images namely …

In medical domain, diseases in critical internal organs are generally inspected using
invasive/non-invasive imaging techniques. Magnetic resonance imaging (MRI) is one of the …

Several neuroimaging processing applications consider skull strip** as a crucial pre-
processing step. Due to complex anatomical brain structure and intensity variations in brain …

In magnetic resonance imaging of the brain, an imaging-preprocessing step removes the
skull and other non-brain tissue from the images. But methods for such a skull-strip** …

In this paper, a complete and fully automatic MRI brain tumour detection and segmentation
methodology is presented as an efficient clinical-aided tool using Gaussian mixture model …

Magnetic resonance (MR) imaging is widely used for assessing infant head and brain
development and for diagnosing pathologies. The main goal of this work is the development …

In this article, two novel methods are proposed to detect the brain boundary in a magnetic
resonance image (MRI) by making an analogy between Helmholtz free energy (HFE) …

Current semantic segmentation models have limitations in addressing tunnel lining crack,
such as high complexity, misidentification, or inability to detect tiny cracks in specific …

Skull extraction from Magnetic Resonance (MR) head image datasets is the process of
segmentation of brain tissues from other tissues (eg, skin, bone, fat) and has an important …

Automatic segmentation of cerebral hemispheres in magnetic resonance (MR) brain images
help to quantify the brain asymmetry and correct several MR brain deformities. The detection …