Method and apparatus for signal-to-noise ratio dependent image processing

• US 20050025377A1
• Filed: 07/29/2003
• Published: 02/03/2005
• Est. Priority Date: 07/29/2003
• Status: Active Grant

• Current Assignee: GE Medical Systems Global Technology Company LLC (GE Healthcare Technologies, Inc.)
• Original Assignee: GE Medical Systems Global Technology Company LLC (GE Healthcare Technologies, Inc.)
• Inventors: Lal, Rakesh Mohan, Avinash, Gopal B., Vavrek, Robert Michael, Bajwa, Imran A.
• Granted Patent: US 7,248,749 B2
• Time in Patent Office: Days
• US Class Current: 382/260
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/10116 X-ray image
• CPC Class Codes: G06T 2207/10132 Ultrasound image
• CPC Class Codes: G06T 2207/20008 Globally adaptive
• CPC Class Codes: G06T 2207/20012 Locally adaptive
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 5/70 Denoising; Smoothing

Abstract

Certain embodiments of the present invention relate to a signal-to-noise ratio dependent image processing system. The method includes computing at least one SNR for at least one region of an image, determining a filter parameter for at least one region based on the at least one SNR, and processing at least one region of the image based on the filter parameter. In addition to SNR, a filter parameter may be determined using user preferences. The system includes a signal-to-noise ratio processor for determining a signal-to-noise ratio for an image. The system also includes a parameter selection unit for selecting at least one filter parameter based on the signal-to-noise ratio. The system further includes an image filter for filtering the image based on the filter parameter(s). In an embodiment, the SNR processor determines signal-to-noise ratio(s) for region(s) in the image.

Claims

1. A method for image processing using signal-to-noise ratio dependent filtering, said method comprising:

measuring noise in an image;

computing a signal-to-noise ratio for the image;

selecting parameters for an image filter framework based on the signal-to-noise ratio; and

processing the image in the filter framework using the parameters.

2. The method of claim 1, wherein said selecting step further comprises selecting parameters based on user preference.

3. The method of claim 1, wherein said selecting step further comprises selecting locally and globally varying parameters.

4. The method of claim 1, wherein said computing step further comprises computing a signal-to-noise ratio for a region in the image.

5. The method of claim 1, further comprising:

computing a plurality of signal-to-noise ratios for a plurality of regions in the image; and

selecting parameters for the image filter framework based on the plurality of signal-to-noise ratios.

6. A method for regional filtering of an image, said method comprising:

computing at least one signal-to-noise ratio for at least one region of an image;

determining a filter parameter for the at least one region based on the at least one signal-to-noise ratio; and

processing the at least one region of the image based on the filter parameter.

7. The method of claim 6, wherein said determining step further comprises determining the filter parameter based on user preferences.

8. The method of claim 6, wherein said selecting step further comprises selecting locally and globally varying parameters.

9. The method of claim 6, further comprising the step of calculating noise in the image.

10. The method of claim 9, wherein said calculating step further comprises calculating noise in the image based on a difference between the image and a smoothed image.

11. An image processing system for signal-to-noise ratio dependent processing of an image, said system comprising:

a signal-to-noise ratio processor for determining a signal-to-noise ratio for an image;

a parameter selection unit for selecting at least one filter parameter based on said signal-to-noise ratio; and

an image filter for filtering said image based on said at least one filter parameter.

12. The system of claim 11, wherein the signal-to-noise ratio processor determines at least one signal-to-noise ratio for at least one region in said image.

13. The system of claim 11, wherein said parameter selection unit further selects locally varying parameters and globally varying parameters.

14. The system of claim 13, wherein said locally varying parameters include blend and edge-smooth parameters.

15. The system of claim 13, wherein said globally varying parameters include a focus parameter.

16. The system of claim 13, wherein said locally varying parameters are determined based on a histogram of local signal-to-noise ratios.

17. The system of claim 13, wherein said globally varying parameters are determined based on a histogram of signal-to-noise ratios for a plurality of images.

18. The system of claim 11, wherein said parameter selection unit further comprises a lookup table relating said signal-to-noise ratio and said filter parameters.

19. The system of claim 18, wherein said lookup table relates signal-to-noise ratios to filter parameters based on user preferences.

20. The system of claim 11, wherein said parameter selection unit selects at least one filter parameter based on user preference.