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 …

Featured Application Skull stripping is the most prevalent brain image analysis method. This
method can be applied to areas such as brain tissue segmentation and volumetric …

Automatic whole-brain extraction from magnetic resonance images (MRI), also known as
skull stripping, is a key component in most neuroimage pipelines. As the first element in the …

Brain extraction is an important step in the analysis of brain images. The variability in brain
morphology and the difference in intensity characteristics due to imaging sequences make …

Characterizing the performance of segmentation algorithms in brain images has been a
persistent challenge due to the complexity of neuroanatomical structures, the quality of …

Skull stripping in brain magnetic resonance imaging (MRI) is an essential step to analyze
images of the brain. Although manual segmentation has the highest accuracy, it is a time …

The most vital challenge for a radiologist is locating the brain tumors in the earlier stage. As
the brain tumor grows rapidly, doubling its actual size in about twenty-five days. If not dealt …

Automatic skull-stripping or brain extraction of magnetic resonance (MR) images is often a
fundamental step in many neuroimage processing pipelines. The accuracy of subsequent …

In this paper, we propose a new skull stripping method for T1-weighted magnetic resonance
(MR) brain images. Skull stripping has played an important role in neuroimage research …

In a companion paper (Rehm et al., 2004), we introduced Minneapolis Consensus Strip
(McStrip), a hybrid algorithm for brain/non-brain segmentation. In this paper, we compare the …