Automated prostate segmentation in MRI is highly demanded for computer-assisted
diagnosis. Recently, a variety of deep learning methods have achieved remarkable progress …

While computed tomography and other imaging techniques are measured in absolute units
with physical meaning, magnetic resonance images are expressed in arbitrary units that are …

Convolutional neural networks (CNNs) have shown promising results on several
segmentation tasks in magnetic resonance (MR) images. However, the accuracy of CNNs …

Modern neuroimaging studies frequently combine data collected from multiple scanners and
experimental conditions. Such data often contain substantial technical variability associated …

Intensity normalization is an important pre-processing step in the study and analysis of
Magnetic Resonance Images (MRI) of human brains. As most parametric supervised …

During neurosurgical procedures the objective of the neurosurgeon is to achieve the
resection of as much diseased tissue as possible while achieving the preservation of healthy …

Magnetic resonance imaging (MRI) intensities are acquired in arbitrary units, making scans
non-comparable across sites and between subjects. Intensity normalization is a first step for …

The performance of image analysis algorithms applied to magnetic resonance images is
strongly influenced by the pulse sequences used to acquire the images. Algorithms are …

An automated scheme for magnetic resonance imaging (MRI) brain segmentation is
proposed. An adaptive mean-shift methodology is utilized in order to classify brain voxels …

Our previous works suggest that fractal texture feature is useful to detect pediatric brain
tumor in multimodal MRI. In this study, we systematically investigate efficacy of using several …