Abstract
Direct volume rendering (DVR) is a technique that emphasizes structures of interest (SOIs) within a volume visually, while simultaneously depicting adjacent regional information, e.g., the spatial location of a structure concerning its neighbors. In DVR, transfer function (TF) plays a key role by enabling accurate identification of SOIs interactively as well as ensuring appropriate visibility of them. TF generation typically involves non-intuitive trial-and-error optimization of rendering parameters, which is time-consuming and inefficient. Attempts at mitigating this manual process have led to approaches that make use of a knowledge database consisting of pre-designed TFs by domain experts. In these approaches, a user navigates the knowledge database to find the most suitable pre-designed TF for their input volume to visualize the SOIs. Although these approaches potentially reduce the workload to generate the TFs, they, however, require manual TF navigation of the knowledge database, as well as the likely fine tuning of the selected TF to suit the input. In this work, we propose a TF design approach, CBR-TF, where we introduce a new content-based retrieval (CBR) method to automatically navigate the knowledge database. Instead of pre-designed TFs, our knowledge database contains volumes with SOI labels. Given an input volume, our CBR-TF approach retrieves relevant volumes (with SOI labels) from the knowledge database; the retrieved labels are then used to generate and optimize TFs of the input. This approach largely reduces manual TF navigation and fine tuning. For our CBR-TF approach, we introduce a novel volumetric image feature which includes both a local primitive intensity profile along the SOIs and regional spatial semantics available from the co-planar images to the profile. For the regional spatial semantics, we adopt a convolutional neural network to obtain high-level image feature representations. For the intensity profile, we extend the dynamic time warping technique to address subtle alignment differences between similar profiles (SOIs). Finally, we propose a two-stage CBR scheme to enable the use of these two different feature representations in a complementary manner, thereby improving SOI retrieval performance. We demonstrate the capabilities of our CBR-TF approach with comparison with a conventional approach in visualization, where an intensity profile matching algorithm is used, and also with potential use-cases in medical volume visualization.
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This work was supported by the Korea Health Technology Research and Development Project through the Korea Health Industry Development Institute under Grant No. HI22C1651, the National Research Foundation of Korea (NRF) under Grant No. 2021R1F1A1059554, and the Culture, Sports and Tourism Research and Development Program through the Korea Creative Content Agency Grant funded by the Ministry of Culture, Sports and Tourism of Korea under Grant No. RS-2023-00227648.
Younhyun Jung received his B.S. degree in computer science from Inha University, Incheon, in 2008, and his Ph.D. degree in computer science from The University of Sydney, Sydney, in 2016. He is currently an assistant professor in computer science with School of Computing, Gachon University, Seongnam. He was a software engineer with Samsung Electronics from 2007 to 2010. His current research interests include volume rendering and multimodal medical image visualization.
Jim Kong received his B.S. (Hons.) degree in computer science from The University of Sydney, Sydney, in 2016. He is currently a software development engineer in Amazon, Sydney.
Bin Sheng received his B.A. degree in English and his B.Eng. degree in computer science from Huazhong University of Science and Technology, Wuhan, in 2004, and his M.Sc. degree in software engineering from the University of Macau, Macau, in 2007, and his Ph.D. degree in computer science and engineering from The Chinese University of Hong Kong, Hong Kong, in 2011. He is currently a professor with the Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai. He is an associate editor of the IEEE Transactions on Circuits and Systems for Video Technology, and The Visual Computer Journal. His current research interests include virtual reality and computer graphics.
Jinman Kim received his B.S. (Hons.) and his Ph.D. degrees in computer science both from the University of Sydney, Sydney, in 2001 and 2006, respectively. From 2008 to 2012, he was an ARC Post-Doctoral Research Fellow, one year leave from 2009 to 2010 to join the MIRALab Research Group, Geneva, as a Marie Curie senior research fellow. Since 2013, he has been with the School of Information Technologies, The University of Sydney, Sydney, where he was a senior lecturer, and became a professor in 2022. His current research interests include medical image analysis and visualization, computeraided diagnosis, and telehealth technologies. He is an associate editor of The Visual Computer Journal.
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Jung, Y., Kong, J., Sheng, B. et al. A Transfer Function Design for Medical Volume Data Using a Knowledge Database Based on Deep Image and Primitive Intensity Profile Features Retrieval. J. Comput. Sci. Technol. 39, 320–335 (2024). https://doi.org/10.1007/s11390-024-3419-7
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DOI: https://doi.org/10.1007/s11390-024-3419-7