VOLUMETRIC IMAGE DATA PROCESSING

• US 20110075885A1
• Filed: 09/29/2009
• Published: 03/31/2011
• Est. Priority Date: 09/29/2009
• Status: Active Grant

• Current Assignee: Peking University
• Original Assignee: Peking University
• Inventors: YUAN, Xiaoru
• Granted Patent: US 8,437,538 B2
• Time in Patent Office: Days
• US Class Current: 382/106
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: G06T 15/005 General purpose rendering architectures
• CPC Class Codes: G06T 17/00 Three dimensional [3D] modelling, e.g. data description of 3D objects

Abstract

A method, apparatus, computer readable medium storing computer readable instructions are disclosed for processing volumetric image data. According to the method, 3-dimensional data points are collected. A plurality of 2-dimensional image maps is obtained from the 3-dimensional data points. At least one of the plurality of 2D image maps is extracted to form at least one image frame. A frame gallery is created from the at least one image frame.

Claims

1. A method for processing volumetric image data comprising:

obtaining a plurality of 2-dimensional image maps from 3-dimensional data points;

extracting at least one of the plurality of 2-dimensional image maps to form at least one image frame; and

creating a frame gallery from the at least one image frame.

2. The method of claim 1, further comprising constructing a volumetric image data based on the plurality of 2-dimensional image maps and temporal data associated with each of the plurality of 2-dimensional image maps.

3. The method of claim 2, further comprising producing a composite image data for rendering the frame gallery with the volumetric image data.

4. The method of claim 1, wherein the at least one image frame is one of a radically different frame or a representative frame.

5. The method of claim 4, wherein the radically different frame is determined by:

calculating a difference sum between two image frames by the sum of the square of difference between all corresponding pixels of the two image frames; and

computing a value of importance for each image frame based on the calculated difference sum, the radically different frame being determined based the computed value of importance for each image frame.

6. The method of claim 4, wherein the representative frame is determined by:

calculating a difference sum between two image frames by the sum of the square of difference between all corresponding pixels of the two image frames;

computing a value of importance for each image frame based on the calculated difference sum;

dividing the volumetric image data into bins based on the computed value of importance for each image frame, one frame from each of the bins being extracted as the representative frame.

7. The method of claim 2, wherein constructing the volumetric image data includes applying an iso-surface visualization process to the volumetric image data.

8. The method of claim 3, wherein the frame gallery is rendered within the volumetric image data.

9. The method of claim 3, wherein the frame gallery is rendered surrounding the volumetric image data.

10. An apparatus for processing volumetric image data comprising:

an obtaining unit that obtains a plurality of 2-dimensional image maps from 3-dimensional data points;

an extracting unit that extracts at least one of the plurality of 2-dimensional image maps to form at least one image frame; and

a creating unit that creates a frame gallery from the at least one image frame.

11. The apparatus of claim 10, further comprising a constructing unit that constructs a volumetric image data based on the plurality of 2-dimensional image maps and temporal data associated with each of the plurality of 2-dimensional image maps;

12. The apparatus of claim 11, further comprising a producing unit that produces a composite image data for rendering the frame gallery with the volumetric image data

13. The apparatus of claim 10, wherein the at least one image frame is one of a radically different frame or a representative frame.

14. The apparatus of claim 13, wherein the extracting unit further comprising a determining unit which determines the radically different frame by:

calculating a difference sum between two image frames by the sum of the square of difference between all corresponding pixels of the two image frames; and

computing a value of importance for each image frame based on the calculated difference sum, the radically different frame being determined based the computed value of importance for each image frame.

15. The apparatus of claim 13, wherein the determining unit determines the representative frame by:

calculating a difference sum between two image frames by the sum of the square of difference between all corresponding pixels of the two image frames;

computing a value of importance for each image frame based on the calculated difference sum; and

dividing the volumetric image data into bins based on the computed value of importance for each image frame, a frame from each of the bins being extracted as the representative frame.

16. The apparatus of claim 11, wherein the constructing unit applies an iso-surface visualization process to the volumetric image data.

17. The apparatus of claim 12, wherein the frame gallery is rendered within the volumetric image data.

18. The apparatus of claim 12, wherein the frame gallery is rendered surrounding the volumetric image data.

19. A method for processing volumetric image data, comprising:

obtaining a plurality of 2-dimensional image maps from 3-dimensional data points;

constructing a volumetric image data based on the plurality of 2-dimensional image maps and temporal data associated with each of the plurality of 2-dimensional image maps; and

applying a critical point graph to the volumetric image data.

20. The method of claim 19, wherein the step of applying a critical point graph further comprising:

computing the gradient field of the volumetric image data and critical points based on eigenvalues and eigenvectors of a Jacobian matrix; and

generating the critical point graph based on the computed critical points and streamlines in between.