System and method for confidence measures for mult-resolution auto-focused tomosynthesis

• US 20070014468A1
• Filed: 07/12/2005
• Published: 01/18/2007
• Est. Priority Date: 07/12/2005
• Status: Abandoned Application

• Current Assignee: Agilent Technologies, Inc.
• Original Assignee: Agilent Technologies, Inc.
• Inventors: Gines, David, Mueller, Horst, Heumann, John
• Application Number:
  US11/179,088
• Publication Number:
  US 20070014468A1
• Time in Patent Office: Days
• US Class Current: 382/154
• CPC Class Codes: A61B 6/5258 involving detection or reduction of artifacts or noise
• CPC Class Codes: G06T 11/008 Specific post-processing after tomographic reconstruction, e.g. voxelisation, metal artifact correction
• CPC Class Codes: G06T 2207/30168 Image quality inspection
• CPC Class Codes: G06T 7/0002 Inspection of images, e.g. flaw detection

Abstract

A method of analyzing image data to determine an appropriate resolution level for image to be generated from the image data. In the method image data can be analyzed to determine a high frequency noise quality and a low frequency noise quality in the image data. These different noise qualities can be used to determine an appropriate resolution for an image to be generated. An apparatus which can execute a method of the invention, is also provided.

Claims

1. In a system for generating an image of at least a portion of an object, a method comprising:

capturing image data for at least a portion of the object;

identifying a low frequency noise quality in the captured image data for at least a portion of the object; and

using the low frequency noise quality to select a resolution level for generating an image using the captured image data.

2. The method of claim 1, wherein the identifying a low frequency noise quality includes using the captured image data to generate an auto-focus curve for a first resolution level, and identifying local extreme points corresponding to a plurality of different heights in the object, where the local extreme points lie outside of a main peak of the auto-focus curve.

3. The method of claim 2, wherein the auto-focus curve is generated using a wavelet transform, and reflects a sharpness quality of the image data for the first resolution level.

4. The method of claim 2, further including calculating a sharpness value, using a second resolution level of the image data, for each of the plurality of different heights in the object.

5. The method of claim 4, wherein the calculating a sharpness value, using a second resolution level of image data is done using a wavelet transform operation.

6. The method of claim 1, further wherein the image of at least a portion of the object is generated using digital tomosynthesis.

7. The method of claim 1 further including:

identifying a high frequency noise quality for the captured image data;

using the high frequency noise quality and the low frequency noise quality to select a resolution for generating an image using the captured image data.

8. The method of claim 7, wherein the high frequency noise quality is captured prior to a runtime operation of the system, wherein image data for the object is being captured during the runtime operation of the system.

9. The method of claim 1, wherein the low frequency noise quality is used to determine a reliability score for the captured image data for a given resolution level.

10. The method of claim 7, wherein the low frequency noise quality is used to determine a reliability score for the captured image data for a given resolution level, and the high frequency noise quality is used to determine an accuracy confidence level for the given resolution level.

11. In an imaging system, a method for evaluating a quality of different resolution levels for viewing at least a portion of an object, the method comprising:

using image data to generate an auto-focus curve for a plurality of different resolution levels, wherein the auto-focus curves provide an estimate for a sharpest height in the object;

determining a high frequency noise quality for each of the plurality of resolution levels;

determining a low frequency noise quality for each of the plurality of different resolution levels; and

using the high frequency noise quality for each of the plurality of different resolution levels, and the low frequency noise quality for the plurality of different resolution levels to select a resolution level for generating an image of at least a portion of the object.

12. The method of claim 11, further including using the high frequency noise quality for each of the plurality of different resolution levels to determine an accuracy measure for a sharpest layer estimate in the object.

13. The method of claim 11, further including using the low frequency noise quality to determine a reliability for each of the plurality of different resolution levels.

14. The method of claim 11, wherein the auto-focus curves are generated using a wavelet transform to calculate sharpness quality using the image data.

15. The method of claim 11, wherein the image of at least a portion of the object is generated using digital tomosynthesis.

16. The method of claim 11 further including wherein determining a low frequency noise quality includes identifying local extreme points of a first auto-focus curve of the plurality of auto-focus curves, wherein the local extreme points lie outside of a main peak of the first auto-focus curve, and correspond to a plurality of different heights in the object, and wherein the first auto-focus curve is generated from a first resolution of the image data, and the local extreme points for the first auto-focus curve have associated sharpness values.

17. The method of claim 16, further, including determining sharpness values for a second resolution of the image data, at the plurality of different heights in the object, and using the sharpness values for the second resolution image data, and the sharpness values for the local extreme points identified using the first auto-focus curve to determine whether the first resolution image data or the second resolution image data has a higher reliability.

18. The method of claim 12, wherein the first resolution level is less that the second resolution level.

19. A system for generating an image of at least a portion of an object, the system including:

an imaging system which captures image data for at least a portion of an object;

a multi-resolution transform module for generating auto-focus curves based in the captured image data;

control module which determines a high frequency noise quality for the imaging system and determines a low frequency noise quality based on the captured image data, and wherein the control module is operable to select a resolution level for an image to be generated using the captured image data.

20. In a system for generating an image of at least a portion of an object, a method comprising:

identifying a high frequency noise quality for the system;

capturing image data for at least a portion of the object; and

using the high frequency noise quality to select a resolution for generating an image using the captured image data.

21. The method of claim 20, wherein the high frequency noise quality is identified prior to a runtime operation of the system, and wherein image data for the object is captured during the runtime operation of the system.