This paper describes a segmentation algorithm designed to separate bone from soft tissue
in magnetic resonance (MR) images developed for computer-assisted surgery of the spine …

In this paper, using certain conformal mappings from complex function theory, we give an
explicit method for flattening the brain surface in a way which is bijective and which …

Though neuroimaging, pathology and pathophysiology suggest a subcortical and deep
cortical involvement in Frontotemporal Lobar Degeneration (FTLD), no studies have …

This paper describes a novel automatic statistical morphology skull stripper (SMSS) that
uniquely exploits a statistical self-similarity measure and a 2-D brain mask to delineate the …

Segmentation of MR brain images using intensity values is severely limited owing to field
inhomogeneities, susceptibility artifacts and partial volume effects. Edge based …

Segmentation of tumors in human brain aims to classify different abnormal tissues (necrotic
core, edema, active cells) from normal tissues (cerebrospinal fluid, gray matter, white matter) …

Automatic segmentation of MRI brain scans is a complex task for two main reasons: the
large variability of the human brain anatomy, which limits the use of general knowledge and …

Engineers have been actively developing tools to detect tumors and to process medical
images. Medical image segmentation is a powerful tool that is often used to detect tumors …

Brain segmentation in magnetic resonance imaging (MRI) images is the process of isolating
the brain from non-brain tissues to simplify the further analysis, such as detecting pathology …

Accurate identification of the most relevant brain regions linked to Alzheimer's disease (AD)
is crucial in order to improve diagnosis techniques and to better understand this …