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.

Destination images of non-visitors

This article provides much needed understanding of destination images held by non-visitors. Recognizing the characteristics of non-visitor images and their formation is important in order to understand images more widely. This qualitative study assesses images of London. The views of three hundred people in the Czech Republic who have never visited London were obtained via an innovative open-ended research instrument. The study showed that non-visitors imagine destinations through comparisons with their own experiences of places. Findings indicate that images can be very persistent and that the first images formed of a destination endure over time. Although the research is based on people with no direct experience of London, the research highlights that a range of secondary ‘experiences’ influence image formation.