Real-Time Computed Tomography Volume Visualization with Ambient Occlusion of Hand-Drawn Transfer Function Using Local Vicinity Statistic

In this paper, we present an efficient method to visualize computed tomography (CT) datasets using ambient occlusion, which is a global illumination technique that adds depth cues to the output image. We can change the transfer function (TF) for volume rendering and generate output images in real ti...

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Bibliographic Details
Published inHealthcare informatics research Vol. 25; no. 4; pp. 297 - 304
Main Authors Kim, Jaewoo, Ha, Taejun, Kye, Heewon
Format Journal Article
LanguageEnglish
Published Korea (South) Korean Society of Medical Informatics 01.10.2019
The Korean Society of Medical Informatics
대한의료정보학회
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Summary:In this paper, we present an efficient method to visualize computed tomography (CT) datasets using ambient occlusion, which is a global illumination technique that adds depth cues to the output image. We can change the transfer function (TF) for volume rendering and generate output images in real time. In preprocessing, the mean and standard deviation of each local vicinity are calculated. During rendering, the ambient light intensity is calculated. The calculation is accelerated on the assumption that the CT value of the local vicinity of each point follows the normal distribution. We approximate complex TF forms with a smaller number of connected line segments to achieve additional acceleration. Ambient occlusion is combined with the existing local illumination technique to produce images with depth in real time. We tested the proposed method on various CT datasets using hand-drawn TFs. The proposed method enabled real-time rendering that was approximately 40 times faster than the previous method. As a result of comparing the output image quality with that of the conventional method, the average signal-to-noise ratio was approximately 40 dB, and the image quality did not significantly deteriorate. When rendering CT images with various TFs, the proposed method generated depth-sensing images in real time.
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https://doi.org/10.4258/hir.2019.25.4.297
ISSN:2093-3681
2093-369X
DOI:10.4258/hir.2019.25.4.297