Magnetic Resonance images are normally corrupted by random noise from the measurement process complicating the automatic feature extraction and analysis of clinical data. It is because of this reason that denoising methods have been traditionally applied to improve MR image quality. Many of these methods use the information of a single image without taking into consideration the intrinsic multi...

OBJECTIVES The influences of Pueraria candollei var. mirifica (PM), a Thai medicinal plant with long tradition of medicinal consumption among menopausal women for rejuvenation and estrogen hormone replacement, on oxidative status in ovariectomized (OVX) mice were determined. METHODS The crude extract of PM and its active phytoestrogen, miroestrol (MR), were given to OVX mice. The effect of th...

We evaluated 4 volume-based automatic image registration algorithms from 2 commercially available treatment planning systems (Philips Syntegra and BrainScan). The algorithms based on cross correlation (CC), local correlation (LC), normalized mutual information (NMI), and BrainScan mutual information (BSMI) were evaluated with: (1) the synthetic computed tomography (CT) images, (2) the CT and ma...

In this paper we propose a novel algorithm which leverages models of white matter fibre dispersion to improve tractography. Tractography methods exploit directional information from diffusion weighted magnetic resonance (DW-MR) imaging to infer connectivity between different brain regions. Most tractography methods use a single direction (e.g. the principal eigenvector of the diffusion tensor) ...

The aim of the present study was to test a new oral contrast medium composed natural components for magnetic resonance (MR) imaging small bowel diseases. Between January 2018 and June 2019, 35 patients affected by ileocolic Crohn’s disease (CD) were enrolled in study. Each patient underwent two sequential MR enterographies, first with standard polyethylene glycol (PEG) water solution and, after...

We consider the problem of Breadth First Search (BFS) traversal on massive sparse undirected graphs. Despite the existence of simple linear time algorithms in the RAM model, it was considered non-viable for massive graphs because of the I/O cost it incurs. Munagala and Ranade [29] and later Mehlhorn and Meyer [27] gave efficient algorithms (refered to as MR BFS and MM BFS, respectively) for com...

In this paper we present a hierarchical generative model of medical image data, which can capture simultaneously the variability of both signal intensity and anatomical shapes across large populations. Such a model has a direct application for learning average-shaped probabilistic tissue templates in a fully automated manner. While in principle the generality of the proposed Bayesian approach m...

This paper presents a novel statistical fuzzy-segmentation method for diffusion tensor (DT) images and magnetic resonance (MR) images. Typical fuzzy-segmentation schemes, e.g. those based on fuzzyC-means (FCM), incorporate Gaussian class models which are inherently biased towards ellipsoidal clusters. Fiber bundles in DT images, however, comprise tensors that can inherently lie on more-complex ...