Efficient visualization of volume data sets with region of interest and wavelets

Sebastien Piccand, Rita Noumeir, Eric Paquette, Robert L. Galloway, Jr., Kevin R. Cleary
<span title="2005-04-12">2005</span> <i title="SPIE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/r4f4cmsmlbdqjgtgzl67eibt2m" style="color: black;">Medical Imaging 2005: Visualization, Image-Guided Procedures, and Display</a> </i> &nbsp;
The growing volume of medical images acquired with new imaging modalities poses big challenges to the radiologist's interpretation process. Innovative image visualization techniques can play a major role in enabling efficient and accurate information presentation and navigation, by combining computational efficiency with diagnostic resolution. Efficiency and resolution, two opposing requirements, can be accomplished by focusing on full resolution regions of interest while maintaining sufficient
more &raquo; ... contextual information. In fact, structures of interest typically occupy a small percentage of the data, but their analysis requires context information like locations within a specific organ or adjacency to sensitive structures. We propose a 3D visualization technique that is based on the multi-resolution property of the wavelet transform in order to display a full resolution region of interest while displaying a coarser context to achieve efficiency in rendering during the exploratory navigation phase. A full resolution context can also be rendered when needed for a specific view. In a preprocessing stage the data is decomposed with a three-dimensional wavelet transform. The interactive visualization process then uses the wavelet representation and a user-specified region to render a full resolution region of interest and a coarser context directly from the wavelet space through wavelet splatting, thus avoiding volume reconstruction. This efficient rendering approach is combined with lighting calculations, in the preprocessing stage. While greatly enhancing depth perception and objects shape, lighting does not add additional cost to the interactive visualization process, resulting in a good compromise between computational efficiency and image quality. In general, the reading physician focuses on a region of interest (ROI) when interpreting medical images. The structure of interest, a tumor or an organ for example, often occupies less than 10% of all the data. Limiting the visualization to a region of interest reduces the amount of data to process, but sacrifices contextual information. Approaches that combine complete resolution ROI with a low resolution context promise to preserve
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.596031">doi:10.1117/12.596031</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/miigp/PiccandNP05.html">dblp:conf/miigp/PiccandNP05</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/id53jlsc5be4dc7nwbtcfn37se">fatcat:id53jlsc5be4dc7nwbtcfn37se</a> </span>
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