MRI-based medical image analysis for brain tumor studies is gaining attention in recent
times due to an increased need for efficient and objective evaluation of large amounts of …

Deformable image registration is a fundamental task in medical image processing. Among
its most important applications, one may cite: 1) multi-modality fusion, where information …

In this paper, we propose a deep learning approach for image registration by predicting
deformation from image appearance. Since obtaining ground-truth deformation fields for …

Recent studies on brain imaging analysis witnessed the core roles of machine learning
techniques in computer-assisted intervention for brain disease diagnosis. Of various …

The objective of this study is to investigate the use of pattern classification methods for
distinguishing different types of brain tumors, such as primary gliomas from metastases, and …

Moments as projections of an image's intensity onto a proper polynomial basis can be
applied to many different aspects of image processing. These include invariant pattern …

Normal and abnormal brains can be segmented by registering the target image with an
atlas. Here, an atlas is defined as the combination of an intensity image (template) and its …

High-grade gliomas are the most aggressive malignant brain tumors. Accurate pre-operative
prognosis for this cohort can lead to better treatment planning. Conventional survival …

We present a generative approach for simultaneously registering a probabilistic atlas of a
healthy population to brain magnetic resonance (MR) scans showing glioma and …

We propose a framework for the robust and fully-automatic segmentation of magnetic
resonance (MR) brain images called “Multi-Atlas Label Propagation with Expectation …