Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Display of Surfaces from Volume Data
IEEE Computer Graphics and Applications
Footprint evaluation for volume rendering
SIGGRAPH '90 Proceedings of the 17th annual conference on Computer graphics and interactive techniques
Generation of transfer functions with stochastic search techniques
Proceedings of the 7th conference on Visualization '96
Design galleries: a general approach to setting parameters for computer graphics and animation
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
VIS '97 Proceedings of the 8th conference on Visualization '97
Semi-automatic generation of transfer functions for direct volume rendering
VVS '98 Proceedings of the 1998 IEEE symposium on Volume visualization
Image-based transfer function design for data exploration in volume visualization
Proceedings of the conference on Visualization '98
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
Fast detection of meaningful isosurfaces for volume data visualization
Proceedings of the conference on Visualization '01
Salient iso-surface detection with model-independent statistical signatures
Proceedings of the conference on Visualization '01
Tissue Classification Based on 3D Local Intensity Structures for Volume Rendering
IEEE Transactions on Visualization and Computer Graphics
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
General adaptive transfer functions design for volume rendering by using neural networks
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
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Local data features play important roles in the transfer function design for volume rendering. By examining relationships among three eigenvalues of inertia matrix, a semi-automatic data-driven transfer function design method is presented in this paper. Local features detected by local block based moments, such as flat, round, elongated shapes are used to guide the design of transfer functions. Furthermore, the proposed method can objectively fulfill the function of the previous boundary based method and extend the domain of transfer function to include more data features. Practice experiments are conducted to render real medical data sets by using the proposed transfer function method.