Flexible occlusion rendering for improved views of three-dimensional medical images Public Deposited

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  • March 21, 2019
  • Borland, David
    • Affiliation: College of Arts and Sciences, Department of Computer Science
  • The goal of this work is to enable more rapid and accurate diagnosis of pathology from three-dimensional (3D) medical images by augmenting standard volume rendering techniques to display otherwise-occluded features within the volume. When displaying such data sets with volume rendering, appropriate selection of the transfer function is critical for determining which features of the data will be displayed. In many cases, however, no transfer function is able to produce the most useful views for diagnosis of pathology. Flexible Occlusion Rendering (FOR) is an addition to standard ray cast volume rendering that modulates accumulated color and opacity along each ray upon detecting features indicating the separation between objects of the same intensity range. For contrast-enhanced MRI and CT data, these separation features are intensity peaks. To detect these peaks, a dual-threshold method is used to reduce sensitivity to noise. To further reduce noise and enable control over the spatial scale of the features detected, a smoothed version of the original data set is used for feature detection, while rendering the original data at high resolution. Separating the occlusion feature detection from the volume rendering transfer function enables robust occlusion determination and seamless transition from occluded views to non-occluded views of surfaces during virtual fly-throughs. FOR has been applied to virtual arthroscopy of joints from MRI data. For example, survey views of entire shoulder socket surfaces have been rendered to enable rapid evaluation by automatically removing the occluding material of the humeral head. Such views are not possible with standard volume rendering. FOR has also been successfully applied to virtual ureteroscopy of the renal collecting system from CT data, and knee fracture visualization from CT data.
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  • In Copyright
  • Taylor, Russell M.
  • Open access

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