Automated medical image visualization using volume rendering with local histograms

• US 20070008317A1
• Filed: 05/25/2005
• Published: 01/11/2007
• Est. Priority Date: 05/25/2005
• Status: Active Grant

• Current Assignee: Sectra AB
• Original Assignee: Sectra AB
• Inventors: Lundstrom, Claes
• Granted Patent: US 7,532,214 B2
• Time in Patent Office: Days
• US Class Current: 345/424
• CPC Class Codes: G06T 15/08 Volume rendering

Abstract

Methods and apparatus are configured to provide data to render (medical) images using direct volume rendering by electronically analyzing a medical volume data set associated with a patient that is automatically electronically divided into a plurality of local histograms having intensity value ranges associated therewith and programmatically generating data used for at least one of tissue detection or tissue classification of tissue having overlapping intensity values.

Claims

1. A method of evaluating data associated with direct volume renderings, comprising:

automatically electronically analyzing a plurality of local histograms derived from a volume rendering data set; and

programmatically generating data that is used for at least one of (a) material classification or (b) material detection, of materials in the data set, including materials of similar intensity, based on the analyzing step.

2. A method according to claim 1, further comprising distinguishing different materials with overlapping intensity values using range weight data values obtained from the electronic analysis of the local histograms.

3. A method according to claim 1, wherein the volume rendering data set is a medical volume rendering data set associated with a volume rendering of a target region of a patient, and wherein the generating data step generates data used for at least one of (a) tissue detection or (b) tissue classification.

4. A method according to claim 3, wherein the medical volume data set comprises MRI (Magnetic Resonance Imaging) data.

5. A method according to claim 3, wherein the medical volume data set comprises CT (computed tomography) data.

6. A method according to claim 1, further comprising accepting user input to rotate and enlarge selected portions of a rendered image generated from the volume data set to thereby allow generally real-time interactive exploration of the rendered image.

7. A method according to claim 1, further comprising programmatically generating partial range histograms of data based on the analyzing step to carry out the tissue detection.

8. A method according to claim 7, wherein a respective partial range histogram can contain local and distributed voxels having similar range weights.

9. A method according to claim 7, further comprising selectively applying a trapezoid transfer function to at least one partial range of interest thereby enhance visualization of a feature in a rendered image.

10. A method according to claim 7, further comprising automatically applying trapezoid transfer functions to substantially all of the partial range histograms to render an image.

11. A method according to claim 1, further comprising providing an interactive user tool that adjusts a transfer function for a selected portion of the image.

12. A method according to claim 1, further comprising:

allowing a user to electronically select a partial range of interest in the volume data set;

programmatically generating a partial range histogram using the user-selected partial range of interest;

applying an adaptive trapezoid transfer function to the partial range histogram; and

electronically visually emphasizing tissue in a rendering in response to the application of the adaptive trapezoid.

13. A method according to claim 2, further comprising rendering a diagnostic medical image of the target region based on the generating step.

14. A method according to claim 1, wherein the analyzing step comprises electronically allocating each local histogram to one of a plurality of partial range histograms based on the range weight of voxels in the respective local histogram.

15. A method according to claim 14, wherein the electronically allocating is based on detected peak characteristics of voxel data in the local histograms.

16. A method according to claim 1, wherein the analyzing and generating data steps are carried out by:

fitting a first Gaussian curve to a global histogram associated with the volume rendering data set;

analyzing local histograms of voxel data to find neighborhoods typical for a selected portion of the Gaussian curve;

removing the selected local neighborhood voxel data from the global histogram to define a modified global histogram; and

iteratively repeating the fitting, analyzing and removing steps to analyze different local neighborhoods of voxel data.

17. A method according to claim 7, further comprising electronically generating a first adaptive color-opacity trapezoid as a transfer function component that adapts center, width and shape to respective partial range histograms.

18. A method according to claim 2, further comprising:

allowing a user to electronically browse detected partial range histograms and select one or more partial range histograms of interest;

then

electronically define a transfer function for the selected one or more partial range histograms to generate a rendered medical image.

19. A method according to claim 7, further comprising automatically detecting peak characteristics in the partial range histograms and merging selected partial range histograms having similar peak characteristics to define merged partial range histograms.

20. A method according to claim 1, wherein the volume rendering data set comprises MRI data with an uncalibrated intensity scale.

21. A method according to claim 20, wherein the MRI data comprises MR angiography (MRA) data, and wherein the method further comprises adapting a transfer function to allow relatively precise manual diameter measurements of the aortic diameter using data from the partial range histograms.

22. A method according to claim 21, further comprising automatically detecting a contrast agent in the MRA data and automatically adjusting parameters for volume rendering of the MRA angiography data set.

23. A method according to claim 2, the method further comprising:

electronically defining partial range histograms based on identified peak characteristics in the local histograms;

fitting a respective Gaussian curve to each of the partial range histograms; and

generating adaptive trapezoidal transfer functions, one for each of the Gaussian curves of the partial range histograms.

24. A method according to claim 1, further comprising electronically identifying value ranges of different tissues having overlapping intensity values and electronically separating the different tissues and assigning a different visual characteristic with each of the different tissues with overlapping intensity values.

25. A method according to claim 24, further comprising accepting user input to adjust a visual characteristic of voxels having overlapping intensity values.

26. A method according to claim 1, wherein the generating step comprises generating a first transfer function attribute associated with intensity and generating a second transfer function attribute associated with data derived from the local neighborhood histogram analysis to automatically electronically identify different tissue of interest having overlapping intensity values in the generated image.

27. A method according to claim 1, further comprising assessing neighborhood characteristic data to classify tissue-specific features associated with voxels having overlapping intensity values.

28. A method according to claim 1, further comprising electronically employing a statistical model of classification probability to classify whether a voxel in the volume rendering data set belongs to a feature and/or material of interest.

29. A method according to claim 28, wherein the statistical model of classification probability is based on weighted inputs of confidence for selected neighborhood characterizing features.

30. A method according to claim 1, further comprising generating a multivariate data set of first and second values for a plurality of histograms associated with locations having voxels with overlapping intensity values, the second value derived from a classification probability model corresponding to a type of tissue the voxel is likely to represent.

31. A method according to claim 2, further comprising rendering blood vessels and spongy bone having overlapping intensity values in the medical data set as distinct features having different color, texture and/or opacity in the image.

32. A method according to claim 2, further comprising rendering a coronary artery having overlapping intensity values with other tissue in the medical data set as a distinct highlighted feature having different color, texture and/or opacity in the image.

33. A method according to claim 2, further comprising rendering in a manner that visually shows a liver tumor having overlapping intensity values with other tissue in the medical data set as a distinct feature having different color, texture and/or opacity in the image.

34. A method according to claim 1, wherein the programmatically generating data step is used for tissue detection, and wherein the method further comprises electronically evaluating an existing transfer function based on the generated data to thereby provide a calibration tool for assessing whether the existing transfer function properly identifies a feature of interest in an image generated from the volume rendering data set.

35. A method according to claim 2, further comprising adapting at least one a priori transfer function to evaluate different volume rendering data sets of similar examination types using programmatically generated partial range histograms of the local histogram data.

36. A method for providing a tissue exploration tool to allow a physician to interactively analyze medical volume data sets in a visualization system, comprising:

allowing a user to electronically select a partial range of interest in an intensity scale of voxel data to thereby allow the user to interactively investigate voxels in a volume rendering data set;

electronically generating a partial range histogram associated with the selected partial range; and

electronically fitting an adaptive trapezoid to the partial range histogram to render an image of material associated with the selected partial range.

37. A method according to claim 36, further comprising displaying a graphic interface that allows a user to electonically slide a partial range bar over a global histogram in the intensity scale.

38. A method for visualizing images of volume data sets, comprising:

iteratively electronically subdividing a respective volume data set using local histograms of neighborhood voxel data to allocate the neighborhoods of data into partial range histograms, each partial range histogram having voxels with intensity values in a defined range; and

electronically automatically identifying different materials in the volume data set, including materials having overlapping image intensity values which may be distributed over a target volume of interest.

39. A method according to claim 38, wherein the local neighborhoods are selected, configured and sized so that the volume data set can be analyzed with non-overlapping subdivision of the voxel data.

40. A system for generating DVR medical images, comprising:

a volume rendering medical image processor system configured to generate data for a diagnostic medical image of a target region of a patient by electronically subdividing a volume rendering data set using local histogram analysis to separate different tissues with overlapping image intensity values.

41. A system according to claim 40, wherein the processor system is configured to analyze peak characteristics and intensity values associated with the local histogram voxels to electronically automatically identify different tissue with overlapping image intensity values.

42. A system according to claim 40, wherein the processor system is configured to programmatically (a) classify and/or (b) detect tissues in the volume data set using data from the local histogram analysis.

43-60. -60. (canceled)

61. A computer program product for providing physician interactive access to patient medical volume data for rendering diagnostic medical images using a computer network, the computer program product comprising:

a computer readable storage medium having computer readable program code embodied in the medium, the computer-readable program code comprising;

computer readable program code configured to generate partial range histograms having associated peak characteristics and intensity values to electronically identify different types of tissue having overlapping image intensity values to thereby provide data used to render a diagnostic medical image of a target region of a patient.

62-70. -70. (canceled)