Imaging & Visualisation

In this invited talk, we will address different software and hardware issues related to the design and implementation of medical imaging systems using reconfigurable computing and multicore platforms. A range of algorithms, including discrete wavelet transform (DWT), Ridgelet and Curvelet transforms will be presented for imaging applications such as medical image denoising, segmentation and compression. The implementation process of these algorithms on reconfigurable platforms will be described. In addition, we will review the latest reconfigurable hardware technologies and development methods for real-time embedded and high performance computing systems, and will conclude with comprehensive case studies demonstrating the deployment of low power reconfigurable architectures for algorithms acceleration and performance evaluation methods for reconfigurable medical imaging systems.
Full Abstract

The Leibniz Supercomputing Centre (LRZ) of the Bavarian Academy of Sciences and Humanities is not only IT service provider for the Munich Universities but competent and reliable research partner as well. Over the last decade, the LRZ has acquired great expertise in the field of visualisation while focusing primarily on the topics of Virtual Reality (VR) and – more recently – Augmented Reality (AR). Large-scale VR installations (such as CAVE-like systems [1] or ultra-high resolution stereoscopic displays) have been a staple in certain industries for decades. Their complexity and cost, though, have always been a limiting factor and prevented the widespread availability and use. However, thanks to advances in hardware and software development over the last few years, VR technologies have taken a leap forward in terms of accessibility, usability, quality, and affordability, especially with regards to head mounted displays (HMDs). Full Abstract

Cerebral Aneurysm (CA) is a vascular disease that affects almost 1.5 – 5% of the general population, mostly adults. Sub-Arachnoid Hemorrhage, caused by a ruptured CA, has high morbidity and mortality rates. Therefore, radiologists aim to detect and diagnose this disease at an early stage to prevent or reduce its consequential damages.
This work contributes to the CA segmentation field by proposing a novel automated algorithm. Precisely, the contourlet transform, as a multiresolution technique, and Hidden Markov Random Field with Expectation Maximization, as a statistical technique, are the two main adopted approaches. The first technique helps in extracting images features not apparent in the normal image scale; while the second technique segments images in the contourlet domain based on the spatial contextual constraints. The proposed algorithm reveals promising results on the tested Three-Dimensional Rotational Angiography (3D RA) datasets, where both an objective and a subjective evaluation are carried out. Full Abstract

Impactful and memorable images are a great resource for science dissemination, improving the reach and engagement of complex and information rich topics. Yet scientists in general do not have the training nor the time to create high quality and appealing imagery, and journalists and designers do not have a deep understanding of the data itself nor the tools to deal with scientific datasets. On its own, each group can more easily fall into the traps of either detailed but uninteresting imagery, or non-rigorous “artist’s rendition” style of visuals generally dismissed by the domain scientists. However, when both scientists and designers work together, they can create accurate and appealing images and stories. Furthermore, from the interaction new approaches appear, like the one we present here. Inspired by popular web libraries that have allowed journalists and designers to incorporate data in their workflow and produce high quality visualizations, we created a pipeline and a set of tools that allow designers and animators to import large scientific datasets (from 3D simulations) directly into industry level software tools (Maya, Blender, Adobe suite, etc.) where they can control and manipulate the visual style more precisely, and reach higher levels of visual quality than with scientific visualization tools. On the other hand, the scientists can become more than simple advisors to the designers, and (thanks to the automation afforded by the coupled tools) create new visualizations for their publications and presentations. Full Abstract