3D Reconstruction of Medical Images from Slices Automatically Landmarked with Growing Neural Models

In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. We present a brain ventricles fast reconstruction method. The method is based on the processing of brain sections and establishing a fixed number of landmarks onto those sections to reconstruct the ventricles 3D surface. Automated landmark extraction is accomplished through the use of the self-organising network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates the classical surface reconstruction and filtering processes. The proposed method offers higher accuracy compared to methods with similar efficiency as Voxel Grid.

Analyzing and Modeling Medical Data on Distributed Computing Infrastructures

Researchers want to analyse Health Care data which may requires large pools of compute and data resources. To have them they need access to Distributed Computing Infrastructures (DCI). To use them it requires expertise which researchers may not have. Workflows can hide infrastructures. There are many workflow systems but they are not interoperable. To learn a workflow system and create workflows in a workflow system may require significant effort. Considering these efforts it is not reasonable to expect that researchers will learn new workflow systems if they want to run workflows of other workflow systems. As a result, the lack of interoperability prevents workflow sharing and a vast amount of research efforts is wasted. The FP7 Sharing Interoperable Workflow for Large-Scale Scientific Simulation on Available DCIs (SHIWA) project developed the Coarse-Grained Interoperability (CGI) to enable workflow sharing. The project created the SHIWA Simulation Platform (SSP) to support CGI as a production-level service. The paper describes how the CGI approach can be used for analysis and simulation in Health Care.

Image, video and 3D data registration: medical, satellite and video processing applications with quality metrics

Data registration refers to a series of techniques for matching or bringing similar objects or datasets together into alignment. These techniques enjoy widespread use in a diverse variety of applications, such as video coding, tracking, object and face detection and recognition, surveillance and satellite imaging, medical image analysis and structure from motion. Registration methods are as numerous as their manifold uses, from pixel level and block or feature based methods to Fourier domain methods. This book is focused on providing algorithms and image and video techniques for registration and quality performance metrics. The authors provide various assessment metrics for measuring registration quality alongside analyses of registration techniques, introducing and explaining both familiar and state–of–the–art registration methodologies used in a variety of targeted applications.

Urocortin – From Parkinson’s disease to the skeleton

Urocortin (Ucn 1), a 40 amino acid long peptide related to corticotropin releasing factor (CRF) was discovered 19 years ago, based on its sequence homology to the parent molecule. Its existence was inferred in the CNS because of anatomical and pharmacological discrepancies between CRF and its two receptor subtypes. Although originally found in the brain, where it has opposing actions to CRF and therefore confers stress-coping mechanisms, Ucn 1 has subsequently been found throughout the periphery including heart, lung, skin, and immune cells. It is now well established that this small peptide is involved in a multitude of physiological and pathophysiological processes, due to its receptor subtype distribution and promiscuity in second messenger signalling pathways. As a result of extensive studies in this field, there are now well over one thousand peer reviewed publications involving Ucn 1. In this review, we intend to highlight some of the less well known actions of Ucn 1 and in particular its role in neuronal cell protection and maintenance of the skeletal system, both by conventional methods of reviewing the literature and using bioinformatics, to highlight further associations between Ucn 1 and disease conditions. Understanding how Ucn 1 works in these tissues, will help to unravel its role in normal and pathophysiological processes. This would ultimately allow the generation of putative medical interventions for the alleviation of important diseases such as Parkinson's disease, arthritis, and osteoporosis.