Affiliation:
1. Ryerson University, Toronto, ON
2. University of Windsor, SUP’COM, Tunisia
Abstract
Transfer function (TF) generation is a fundamental problem in direct volume rendering (DVR). A TF maps voxels to color and opacity values to reveal inner structures. Existing TF tools are complex and unintuitive for the users who are more likely to be medical professionals than computer scientists. In this article, we propose a novel image-centric method for TF generation where instead of complex tools, the user directly manipulates volume data to generate DVR. The user’s work is further simplified by presenting only the most informative volume slices for selection. Based on the selected parts, the voxels are classified using our novel sparse nonparametric support vector machine classifier, which combines both local and near-global distributional information of the training data. The voxel classes are mapped to aesthetically pleasing and distinguishable color and opacity values using harmonic colors. Experimental results on several benchmark datasets and a detailed user survey show the effectiveness of the proposed method.
Publisher
Association for Computing Machinery (ACM)
Subject
Artificial Intelligence,Theoretical Computer Science
Cited by
5 articles.
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1. Artificial Intelligence and Machine Learning;Impact of Artificial Intelligence in Business and Society;2023-06-12
2. Deep Interactive Volume Exploration Through Pre-Trained 3D CNN and Active Learning;Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications;2023
3. A real-time image-centric transfer function design based on incremental classification;Journal of Real-Time Image Processing;2021-10-19
4. GPU-based multi-slice per pass algorithm in interactive volume illumination rendering;Frontiers of Information Technology & Electronic Engineering;2021-08
5. CNNs Based Viewpoint Estimation for Volume Visualization;ACM Transactions on Intelligent Systems and Technology;2019-05-31