Perception-based artifact quantification for volume rendering

• US 20080129732A1
• Filed: 07/30/2007
• Published: 06/05/2008
• Est. Priority Date: 08/01/2006
• Status: Active Grant

• Current Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Original Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Inventors: Collins, John Patrick, Nadar, Mariappan S., Nafziger, John S., Johnson, Jeffrey P., Yu, Daphne, Stingl, Thomas
• Granted Patent: US 8,711,144 B2
• Time in Patent Office: Days
• US Class Current: 345/424
• CPC Class Codes: G06T 15/08 Volume rendering
• CPC Class Codes: G06T 2207/30168 Image quality inspection
• CPC Class Codes: G06T 7/0002 Inspection of images, e.g. flaw detection

Abstract

Artifact quantification is provided in volume rendering. Since the visual conspicuity of rendering artifacts strongly influences subjective assessments of image quality, quantitative metrics that accurately correlate with human visual perception may provide consistent values over a range of imaging conditions.

Claims

1. A system for artifact quantification in volume rendering, the system comprising:

a memory operable to store a dataset representing a three-dimensional volume;

a processor operable to volume render a two-dimensional representation of the volume from the dataset, and operable to determine a quantity representing an artifact as a function of a perception-based visual quality metric; and

a display operable to display the two-dimensional representation of the volume, the quantity, or both.

2. The system of claim 1 wherein the perception-based visual quality metric is a function of vertical features, horizontal features, angle features, contrast sensitivity, luminance sensitivity, psychophysical masking, or combinations thereof.

3. The system of claim 1 wherein the processor is operable to alter at least one rendering parameter as a function of the quantity, the at least one rendering parameter comprising sample size, sampling rate, classification, sampling variation, volume size, rendering method, or combinations thereof.

4. The system of claim 1 wherein the artifact comprises shading noise, edge non-smoothness, opacity inconsistency, or combinations thereof.

5. The system of claim 4 wherein the artifact comprises shading noise, and wherein the perception-based visual quality metric comprises higher spatial frequency response of the two-dimensional representation.

6. The system of claim 4 wherein the artifact comprises edge non-smoothness, and wherein the perception-based visual quality metric comprises orientation responses at a respective plurality of orientations relative to the two-dimensional representation.

7. The system of claim 4 wherein the artifact comprises opacity inconsistency and wherein the perception-based visual quality metric comprises a plurality of measures of differences between the two-dimensional representation and another two-dimensional representation of the volume rendered from a different perspective.

8. The system of claim 4 wherein the perception-based visual quality metric is a function of vertical features, horizontal features, angle features, contrast sensitivity, luminance sensitivity, psychophysical masking, or combinations thereof;

and

wherein the processor is operable to identify, within the two-dimensional representation, shading noise, edge non-smoothness, and opacity inconsistency.

9. The system of claim 1 wherein the processor is operable to mask the dataset as a function of at least the perception-based visual quality metric.

10. The system of claim 1 wherein the quantity is a function of a histogram of the perception-based visual quality metric from different sub-regions of the two-dimensional representation.

11. A method for artifact quantification in volume rendering, the method comprising:

volume rendering a volume rendered image from a dataset; and

quantifying, with a processor, visibility to a user of an undesirable rendering feature in the volume rendered image.

12. The method of claim 11 wherein quantifying comprises calculating a perception-based visual quality metric as a function of vertical features, horizontal features, orientation features, contrast sensitivity, luminance sensitivity, psychophysical masking, or combinations thereof.

13. The method of claim 11 wherein quantifying comprises correlating responses from a plurality of perception-based visual quality metrics to rendering artifacts.

14. The method of claim 13 wherein correlating comprises identifying a greater response for higher spatial frequency of two or more of the metrics than lower spatial frequency;

and

further comprising;

identifying shading noise, as a function of the correlation, as the rendering artifact.

15. The method of claim 13 wherein correlating comprises identifying a greater response for orientation metrics;

and

further comprising;

identifying edge non-smoothness, as a function of the correlation, as the rendering artifact.

16. The method of claim 13 wherein volume rendering comprises volume rendering a spherical phantom at a first viewing direction, further comprising volume rendering the spherical phantom at a second, different viewing direction;

wherein correlating comprises identifying a greater response of one or more of the metrics at the first viewing direction than the second viewing direction; and

further comprising;

identifying opacity inconsistency, as a function of the correlation, as the rendering artifact.

17. The method of claim 11 wherein quantifying comprises calculating a just noticeable difference level of visibility of the undesirable rendering feature.

18. The method of claim 11 wherein the quantified visibility is a function of a histogram of one or more perception-based visual quality metrics from different sub-regions of the volume rendered image.

19. In a computer readable storage medium having stored therein data representing instructions executable by a programmed processor for artifact quantification in volume rendering, the storage medium comprising instructions for:

volume rendering an image representation of a volume from a data set representing the volume;

calculating a first quantity with a human visual model from the image representation; and

identifying a rendering artifact as a function of the first quantity.

20. The instructions of claim 19 wherein calculating comprises calculating as a function of two or more perception-based visual quality metrics each corresponding to one of vertical features, horizontal features, angle features, contrast sensitivity, luminance sensitivity, psychophysical masking, or combinations thereof;

and

wherein identifying comprises identifying, within the image representation, shading noise, edge non-smoothness, and opacity inconsistency.