System and method for processing imaging data

• US 7,929,746 B2
• Filed: 08/11/2006
• Issued: 04/19/2011
• Est. Priority Date: 08/10/2006
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Li, Baojun, Avinash, Gopal B., Israni, Kenny Kishan
• Primary Examiner(s): Tabatabai; Abolfazl
• Application Number:
  US11/464,103
• Publication Number:
  US 20080037847A1
• Time in Patent Office: 1,712 Days
• Field of Search: 382128-134, 382/145, 382/159, 382/165, 382/170, 382/224, 382/225, 382/227, 378/4, 378 21- 27, 378/46, 378/90, 378/92, 378/101, 378/140, 378/901, 600/407, 600/408, 600/425, 128/920, 128/925
• US Class Current: 382/131
• CPC Class Codes: A61B 6/508 for non-human patients
• CPC Class Codes: G06T 2207/10112 Digital tomosynthesis [DTS]
• CPC Class Codes: G06T 2207/10116 X-ray image
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/143 involving probabilistic approaches, e.g. Markov random field [MRF] modelling
• CPC Class Codes: G06T 7/194 involving foreground-background segmentation

Abstract

A system and method includes using a point spread function based rule to classify regions in a dataset and processing the dataset based on the point spread function based classification.

Claims

1. A computer readable medium embedded with a program configured to instruct a computer to multi-resolution process a tomosynthesis dataset based on a point spread function based classification.

2. The computer readable medium of claim 1, wherein the classification is into classes comprising a background class, an in focus class, an out of focus class, and a low frequency class.

3. The computer readable medium of claim 1, wherein the program is further configured to use a point spread function based rule to classify regions of different intensity profiles in the tomosynthesis dataset.

4. The computer readable medium of claim 1, wherein the multi-resolution processing comprises a decomposition phase, the decomposition phase comprising progressive decomposition of the tomosynthesis data into a series of spatial frequency images of progressively lower spatial frequency.

5. A system, comprising:

an x-ray source;

an x-ray detector positioned to receive x-ray emitted from the source; and

a computer operationally coupled to the source and the detector, the computer configured to multi-resolution process a tomosynthesis dataset based on a point spread function based classification.

6. The system of claim 5, wherein the classification is into classes comprising a background class, an in focus class, an out of focus class, and a low frequency class.

7. The system of claim 5, wherein the computer provides a platform for 3D adaptive deblurring of tomosynthesis datasets.

8. The system of claim 5, wherein the computer is configured to provide a PSF based adaptive processing of tomosynthesis images.

9. The system of claim 5, wherein the process covers any type of multi-resolution processing for tomosynthesis images.

10. The system of claim 5, wherein the computer is further configured to use a point spread function based rule to classify regions of different intensity profiles in the tomosynthesis dataset.

11. The system of claim 5, wherein the multi-resolution processing comprises a decomposition phase, the decomposition phase comprising progressive decomposition of the tomosynthesis data into a series of spatial frequency images of progressively lower spatial frequency.

12. A method, comprising:

scanning an object to obtain a tomosynthesis dataset;

using a point spread function based rule to classify regions in the tomosynthesis dataset; and

performing multi-resolution processing of the tomosynthesis dataset based on the point spread function based classification.

13. The method of claim 12, wherein the point spread function based rule is used to classify regions of different intensity profiles in the tomosynthesis dataset.

14. The method of claim 12, wherein performing multi-resolution processing comprises generating a series of decomposed images having different spatial frequencies.

15. The method of claim 12, wherein performing multi-resolution processing comprises a filtering sequence for spatially selective enhancement of the tomosynthesis data.