Direct Volume Rendering Transfer Func6on Design Han-Wei Shen The - - PowerPoint PPT Presentation

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Direct Volume Rendering Transfer Func6on Design Han-Wei Shen The - - PowerPoint PPT Presentation

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Direct Volume Rendering Transfer Func6on Design Han-Wei Shen The Ohio State University Transfer Func6on Map a data sample to color and opacity Data Set The sample could be A single value (scalar) Mul6ple values (scalar, gradient

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SLIDE 1

Direct Volume Rendering

Transfer Func6on Design

Han-Wei Shen The Ohio State University

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SLIDE 2

Transfer Func6on

  • Map a data sample to color and opacity

Data Set

  • The sample could be

– A single value (scalar) – Mul6ple values (scalar, gradient magnitude, etc)

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SLIDE 3

Transfer Func6on in Rendering Equa6on

I(D) = I0 × e−

R D τ(t)dt +

Z

D 0 g(s)e− R D

s

τ(t)dtds

I0

i=n

Y

i=1

(1 − αi)

i=n

X

i=1

gi ×

n

Y

j=i+1

(1 − αi)

( ) Opacity Color in (R,G,B)

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SLIDE 4

Transfer Func6on in Back-to-Front Composi6ng

c1 c2 c3

The ini6al pixel color = Black Back-to-Front composi6ng: use ‘under’ operator C = C1 ‘under’ background C = C2 ‘under C C = C3 ‘under C … Cout = Cin * (1-α(x)) + C(x)*α(x) (this is the alpha blending formula)

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SLIDE 5

c1 c2 c3

Front-to-Back composi6ng: use ‘over’ operator C = background ‘over’ C1 C = C ‘over’ C2 C = C ‘over’ C3 … Cout = Cin + C(x)*(1- αin); αout = αin + α(x) *(1-αin)

Transfer Func6on in Front-to-Back Composi6ng

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SLIDE 6

Color in Transfer Func6on

  • Color

– Dis6nguish different materials

  • All Materials uses same

color

  • See no features
  • Muscle: orange
  • Bone: yellow
  • Tooth: red
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SLIDE 7

Opacity in Transfer Func6on

  • Opacity

– Opacity (transparency) of each sample – That mul6ple materials is shown in the rendered image provides more context

  • Material inside the

muscle is occlude

  • See no features

α v

High Opacity For Muscle

  • Make the muscle

transparent

  • See the bone and

tooth α v

Low Opacity For Muscle

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SLIDE 8

Transfer Func6on Design

  • Goal

– Using transfer func6on to emphasize salient structures and de- emphasize other

  • Challenges

– Without knowing the data how to design a good transfer func6on? – A small difference in the transfer func6on could produce very different images – Some features are not easy to show without a lot of tweaking

Need algorithms and strategies to assist users to find the desired transfer func6on in a huge transfer func6on search space

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SLIDE 9

Transfer Func6on Design

  • Goal

– Using transfer func6on to emphasize salient structures and de- emphasize other

  • Challenges

– Without knowing the data how to design a good transfer func6on? – A small difference in the transfer func6on could produce very different images – Some features are not easy to show without a lot of tweaking

Need algorithms and strategies to assist users to find the desired transfer func6on in a huge transfer func6on search space

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SLIDE 10

Transfer Func6on Design

  • Goal

– Using transfer func6on to emphasize salient structures and de- emphasize other

  • Challenges

– Without knowing the data how to design a good transfer func6on? – A small difference in the transfer func6on could produce very different images – Some features are not easy to show without a lot of tweaking

Need algorithms and strategies to assist users to find the desired transfer func6on in a huge transfer func6on search space

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SLIDE 11

Transfer Func6on Design

  • Goal

– Using transfer func6on to emphasize salient structures and de- emphasize other

  • Challenges

– Without knowing the data how to design a good transfer func6on? – A small difference in the transfer func6on could produce very different images – Some features are not easy to show without a lot of tweaking

Need algorithms and strategies to assist users to find the desired transfer func6on in a huge transfer func6on search space

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SLIDE 12

Transfer Func6on Design

  • Goal

– Using transfer func6on to emphasize salient structures and de- emphasize other

  • Challenges

– Without knowing the data how to design a good transfer func6on? – A small difference in the transfer func6on could produce very different images – Some features are not easy to show without a lot of tweaking

Need algorithms and strategies to assist users to find the desired transfer func6on in a huge transfer func6on search space

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SLIDE 13

Trial and Error

  • Manually control the color and opacity of each scalar value using

graphical user interface (GUI)

– Very tedious work and inefficient – Small transfer func6on change can produce very different images It could be difficult to get a good image without enough prior knowledge

Shib α v α v

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SLIDE 14

Paraview

  • 8. Select ‘Volume’

Control Opacity of Transfer Func;on

Control Color

Set Different Color Map

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SLIDE 15

Histogram Assisted Transfer Func6on Design

  • Histogram

– Divide the data range into finite intervals(bins) – Frequency of a bin is the number of samples whose values are in the interval

Data Value Frequency

*hAp://en.wikipedia.org/wiki/Histogram

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SLIDE 16
  • Different features in the data set some6mes have values in different scalar

ranges

  • If this is the case, value clusters can be seen from the histogram
  • Different value clusters can be assigned with different colors and opaci6es

Air Muscle Bone & Tooth

Histogram Assisted Transfer Func6on Design

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SLIDE 17

Mul6-Dimensional Transfer Func6ons

  • Certain features cannot be captured by 1D histograms

– boundary between two materials

  • Ex: emphasize the boundary between sinuses and 6ssue

*Interac;ve Volume Rendering Using Mul;-Dimensional Transfer. Func;ons and Direct Manipula;on Widgets. Joe Kniss. Gordon Kindlmann. Charles Hansen. 2001

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SLIDE 18

Mul6-Dimensional Transfer Func6ons

  • How to detect/capture the boundaries

– Values: step func6on

– Gradients: local maximum – 2nd deriva6ves: zero crossing

  • 1st deriva6ve of the raw data at a point

*Interac;ve Volume Rendering Using Mul;-Dimensional Transfer. Func;ons and Direct Manipula;on Widgets. Joe Kniss. Gordon Kindlmann. Charles Hansen. 2001

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SLIDE 19

Mul6-Dimension Transfer Func6on Design

  • 2D Histogram

– X-axis – data value – Y-axis – gradient – Color intensity – frequency of the histogram (darker means

more here)

*Interac;ve Volume Rendering Using Mul;-Dimensional Transfer. Func;ons and Direct Manipula;on Widgets. Joe Kniss. Gordon Kindlmann. Charles Hansen. 2001

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SLIDE 20

Mul6-Dimensional Transfer Func6ons

  • 1D histogram can capture

homogeneous region only

– A : air – B : 6ssue – C : bone

  • 2D histogram can capture

– D : air and 6ssue boundary – E : 6ssue and bone boundary – F : air and bone boundary

*Interac;ve Volume Rendering Using Mul;-Dimensional Transfer. Func;ons and Direct Manipula;on Widgets. Joe Kniss. Gordon Kindlmann. Charles Hansen. 2001

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SLIDE 21

Interface for Transfer Func6on Design

  • Use graphical interface to map 3D points back

to the transfer func6on domain

  • Move the probe and interact in the

volume space

  • Indicate where the point is in the

transfer func6on domain

*Interac;ve Volume Rendering Using Mul;-Dimensional Transfer. Func;ons and Direct Manipula;on Widgets. Joe Kniss. Gordon Kindlmann. Charles Hansen. 2001

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SLIDE 22

References

  • Marc Levoy, Display of Surface from Volume Data, IEEE

Computer Graphics and Applica6ons, Vol. 8, No. 3, May, 1988, pp. 29-37

  • G. Kindlemann and J.W. Durkin, Semi-automa6c

Genera6on of Transfer Func6ons for Direct Volume Rendering, Proc. of IEEE Symposium on Volume Visualiza6on, pp. 79-86, 1998

  • J. Kinss, G. Kindlemann, C. Hansen, Mul6dimensional

Transfer Func6on for Interac6ve Volume Rendering, IEEE Transac6ons on Visualiza6on and Computer Graphics, Vol. 8, No. 3, pp. 270-285, 2002