Classification for Volume Rendering of Industrial CT Based on Moment of Histogram

Li, Jin; Lu-lu, Zhou; Yu, Hong; Hong, Liang; Wang, Lei

doi:10.1109/iciea.2007.4318542

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…He et al [11] have used the 1D TF which classifies voxels based on their scalar intensity values. and Li et al [12] have introduced the classification of volume rendering by applying a 1D TF to an automobile CT image. The 1D TF is the most common transfer function technique employed in various systems [13]- [15].…”

Section: Related Workmentioning

confidence: 99%

“…b) shows DVRIs obtained with each grid of the Grid-TF, which we call G-DVRIs. Figure7 (c) is an enlarged view of DVRIs generated with theGrid 11,12,13,21,22,23,31,32,and 33. Since the color information in the rendered image might influence the classification in the CNN, we assign only the white to each grid in the Grid-TF and apply the Phong shading for the rendering.…”

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confidence: 99%

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Image-Based TF Colorization With CNN for Direct Volume Rendering

2021

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In the direct volume rendering (DVR), it often takes a long time for a novice to manipulate the transfer function (TF) and analyze the volume data. To lessen the difficulty in volume rendering, several researchers have developed deep learning techniques. However, the existing techniques are not easy to apply directly to existing DVR pipelines. In this study, we propose an image-based TF colorization with CNN to automatically generate a direct volume rendering image (DVRI) similar to a target image. Our system includes CNN model training, TF labeling, image-based TF generation, and volume rendering by matching the target image. We introduce a technique for training CNN and labeling the TF with images similar to the input volume dataset. Moreover, we extract the primary colors from the target image according to the labels classified with the CNN model. We render the volume data with the TF to produce the DVRI reproducing the prominent colors in the target image.

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Section: Related Workmentioning

confidence: 99%

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confidence: 99%

Image-Based TF Colorization With CNN for Direct Volume Rendering

2021

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“…Li et al . [LZY*07a, LZY*07b] concentrate on industrial CT applications and 1D histograms. They propose using stochastic methods to differentiate between the clusters in the histogram.…”

Section: Aggregated Attributesmentioning

confidence: 99%

State of the Art in Transfer Functions for Direct Volume Rendering

Ljung

Krüger

Gröller

et al. 2016

Computer Graphics Forum

106

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A central topic in scientific visualization is the transfer function (TF) for volume rendering. The TF serves a fundamental role in translating scalar and multivariate data into color and opacity to express and reveal the relevant features present in the data studied. Beyond this core functionality, TFs also serve as a tool for encoding and utilizing domain knowledge and as an expression for visual design of material appearances. TFs also enable interactive volumetric exploration of complex data. The purpose of this state-of-the-art report (STAR) is to provide an overview of research into the various aspects of TFs, which lead to interpretation of the underlying data through the use of meaningful visual representations. The STAR classifies TF research into the following aspects: dimensionality, derived attributes, aggregated attributes, rendering aspects, automation, and user interfaces. The STAR concludes with some interesting research challenges that form the basis of an agenda for the development of next generation TF tools and methodologies.

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