This paper presents a computational framework for whole brain segmentation of 7 Tesla
magnetic resonance images able to handle ultra-high resolution data. The approach …

The medical image analysis field has traditionally been focused on the development of
organ-, and disease-specific methods. Recently, the interest in the development of more …

Improvements in the spatial resolution of structural and functional MRI are beginning to
enable analysis of intracortical structures such as heavily myelinated layers in 3D, a …

Visual Computing for Medicine, Second Edition, offers cutting-edge visualization techniques
and their applications in medical diagnosis, education, and treatment. The book includes …

With recent improvements in human magnetic resonance imaging (MRI) at ultra-high fields,
the amount of data collected per subject in a given MRI experiment has increased …

Deformable models are widely used for image segmentation, most commonly to find single
objects within an image. Although several methods have been proposed to segment …

In this paper, we propose a novel predictive model, active volume model (AVM), for object
boundary extraction. It is a dynamic “object” model whose manifestation includes a …

Image segmentation plays an important role in computer vision and image analysis. In this
paper, image segmentation is formulated as a labeling problem under a probability …

While the brain regions involved in action observation are relatively well documented in
humans and primates, how these regions communicate to help understand and predict …

We present a discrete unsupervised multiregion-competition algorithm for image
segmentation over different energy functionals. The number of regions present in an image …