Although cancer often is referred to as “a disease of the genes,” it is indisputable that the
(epi) genetic properties of individual cancer cells are highly variable, even within the same …

This paper presents a review of methods and techniques that have been proposed for the
segmentation of magnetic resonance (MR) images of the brain, with a special emphasis on …

An automated brain tumor segmentation method was developed and validated against
manual segmentation with three-dimensional magnetic resonance images in 20 patients …

A number of supervised and unsupervised pattern recognition techniques have been
proposed in recent years for the segmentation and the quantitative analysis of MR images …

A multichannel statistical classifier for detecting prostate cancer was developed and
validated by combining information from three different magnetic resonance (MR) …

Understanding structure–function relationships in the brain after stroke is reliant not only on
the accurate anatomical delineation of the focal ischemic lesion, but also on previous …

This work presents an investigation of the potential of artificial neural networks for
classification of registered magnetic resonance and X-ray computer tomography images of …

Human investigators instinctively segment medical images into their anatomical
components, drawing upon prior knowledge of anatomy to overcome image artifacts, noise …

The application of the Hopfield neural network for the multispectral unsupervised
classification of MR images is reported. Winner-take-all neurons were used to obtain a crisp …

The most commonly reported finding from structural brain studies in autism is abnormality of
the cerebellum. Autopsy and magnetic resonance imaging (MR) studies from nine …