Simultaneous grayscale and geometric registration of images

• US 20050163360A1
• Filed: 12/30/2004
• Published: 07/28/2005
• Est. Priority Date: 07/18/2003
• Status: Active Grant

• Current Assignee: Hologic Incorporated
• Original Assignee: R2 Technology, Inc. (Hologic Incorporated)
• Inventors: Karssemeijer, Nico, Snoeren, Peter R.
• Granted Patent: US 7,664,302 B2
• Time in Patent Office: Days
• US Class Current: 382/132
• CPC Class Codes: G06T 2207/30068 Mammography; Breast
• CPC Class Codes: G06T 7/35 using statistical methods
• CPC Class Codes: G06V 10/24 Aligning, centring, orientation detection or correction of the image

Abstract

Simultaneous grayscale and geometric registration of images, such as mammograms, facilitates temporal comparison and enhances the speed and reliability of computer aided diagnosis (CAD) detection of medical abnormalities. The method generally includes optimizing a merit function, e.g., sum of squared errors, containing parameters associated with a transformation function for simultaneous geometric and grayscale registering of the images, the optimizing of the merit function being performed by determining optimal values of the parameters using data in the images and registering one image to the other by applying the geometric and grayscale transformation function using the optimal values of the parameters. The optimizing may be performed iteratively from coarse to fine resolutions using a modified Levenberg-Marquardt method for optimizing nonlinear parameters with linear regression for optimizing linear parameters. A final iteration may be performed after removing pixel value pairs from the images that correspond to outliers of a joint pixel value histogram.

Claims

1. A method for registration of a first image and a second image, comprising:

receiving the first and second images;

optimizing a merit function containing a plurality of parameters associated with a transformation function for simultaneous geometric and grayscale registering of the first and second images relative to each other, the optimizing of the merit function being performed by determining optimal values of the parameters using data in the first and second images; and

registering the first image to the second image by applying the geometric and grayscale transformation function using the optimal values of the parameters to generate a third image, the third image being transformed from the first image and registered to the second image.

2. The method of claim 1, further comprising:

performing computer aided detection process for comparing the second and third images; and

displaying the second and third images with results of the computer aided detection process.

3. The method of claim 1, wherein the merit function is a sum of squared errors.

4. The method of claim 1, wherein the optimizing includes iteratively performing an optimization process from a coarse resolution to a fine resolution of the images.

5. The method of claim 4, wherein each iterative optimization process utilizes results from a previous iteration.

6. The method of claim 4, wherein the parameters include at least one linear parameter and at least one nonlinear parameter and wherein each iterative optimization process includes:

optimizing each linear parameter of the merit function while fixing the value of each nonlinear parameter; and

optimizing each nonlinear parameter of the merit function while fixing the value of each linear parameter.

7. The method of claim 6, wherein the optimizing each linear parameter of the merit function includes performing linear regression.

8. The method of claim 6, wherein the optimizing each nonlinear parameter of the merit function includes performing a Levenberg-Marquardt method.

9. The method of claim 1, wherein the registering includes generating a simultaneous geometric and grayscale transformation lookup table and utilizing the lookup table to generate to the pixel values of the third image from the first image.

10. The method of claim 1, wherein the optimizing the merit function further includes:

determining a preliminary set of optimal values of the parameters using data in the first and second images;

generating a joint histogram of pixel value pairs of the first and second images using the preliminary set of optimal values of the parameters, the joint histogram having its mass centered about a curve;

identifying outliers as points in the joint histogram that exceed a predetermined distance from the curve of the joint histogram;

determining pixel value pairs in the first and second images corresponding to the outliers; and

repeating the optimizing the merit function using data in the first and second images without the pixel value pairs corresponding to the outliers.

11. The method of claim 1, wherein each of the first and second images includes a target portion and a remainder portion, the method further comprising:

cropping each of the first and second images to contain only the respective target portion prior to the optimizing.

12. The method of claim 1, wherein each of the first and second images is a medical image.

13. The method of claim 1, wherein each of the first and second images is a mammogram.

14. The method of claim 1, wherein each of the first and second images is selected from a digitized analog image and a digitally acquired image.

15. A system for registering a first image to a second image, comprising:

an input for receiving data for the first and second images; and

a processor configured to optimize a merit function containing a plurality of parameters associated with a transformation function for simultaneous geometric and grayscale registering of the first and second images relative to each other, the processor being configured to optimize the merit function by determining optimal values of the parameters using the data in the first and second images, the processor being further configured to register the first image to the second image by applying the geometric and grayscale transformation function using the optimal values of the parameters to generate a third image, the third image being transformed from the first image and registered to the second image.

16. The system of claim 15, wherein the processor is further configured to perform computer aided detection process for comparing the second and third images, te system further comprising:

a display for displaying the second and third images with results of the computer aided detection process.

17. The system of claim 15, wherein the merit function is a sum of squared errors.

18. The system of claim 15, wherein the processor is further configured to iteratively perform an optimization process from a coarse resolution to a fine resolution of the images.

19. The system of claim 18, wherein the processor is further configured to utilize results from a previous iteration in each iterative optimization process.

20. The system of claim 18, wherein the parameters include at least one linear parameter and at least one nonlinear parameter and wherein the processor is further configured, for each iterative optimization process, to optimize each linear parameter of the merit function while fixing the value of each nonlinear parameter and to optimize each nonlinear parameter of the merit function while fixing the value of each linear parameter.

21. The system of claim 18, wherein the processor is further configured to optimize each linear parameter of the merit function by performing linear regression.

22. The system of claim 18, wherein the processor is further configured to optimize each nonlinear parameter of the merit function by performing a Levenberg-Marquardt method.

23. The system of claim 15, wherein the processor is further configured to register the first image to the second image by generating a simultaneous geometric and grayscale transformation lookup table and utilizing the lookup table to generate to the pixel values of the third image from the first image.

24. The system of claim 15, wherein the processor is further configured to optimize the merit function by:

determining a preliminary set of optimal values of the parameters using data in the first and second images;

generating a joint histogram of pixel value pairs of the first and second images using the preliminary set of optimal values of the parameters, the joint histogram having its mass centered about a curve;

identifying outliers as points in the joint histogram that exceed a predetermined distance from the curve of the joint histogram;

determining pixel value pairs in the first and second images corresponding to the outliers; and

repeating the optimizing the merit function using data in the first and second images without the pixel value pairs corresponding to the outliers.

25. A computer program product for directing a computing apparatus to automatically register a first image and a second image, comprising:

computer code for optimizing a merit function containing a plurality of parameters associated with a transformation function for simultaneous geometric and grayscale registering of the first and second images relative to each other, the optimizing of the merit function being performed by determining optimal values of the parameters using data in the first and second images; and

computer code for registering the first image to the second image by applying the geometric and grayscale transformation function using the optimal values of the parameters to generate a third image, the third image being transformed from the first image and registered to the second image.

26. The computer program product of claim 25, further comprising:

computer code for performing computer aided detection process for comparing the second and third images; and

computer code for displaying the second and third images with results of the computer aided detection process.

27. The computer program product of claim 25, wherein the merit function is a sum of squared errors.

28. The computer program product of claim 25, wherein the computer code for optimizing includes computer code for iteratively performing an optimization process from a coarse resolution to a fine resolution of the images.

29. The computer program product of claim 28, wherein each iterative optimization process utilizes results from a previous iteration.

30. The computer program product of claim 28, wherein the parameters include at least one linear parameter and at least one nonlinear parameter and wherein the computer code for performing each iterative optimization process includes:

computer code for optimizing each linear parameter of the merit function while fixing the value of each nonlinear parameter; and

computer code for optimizing each nonlinear parameter of the merit function while fixing the value of each linear parameter.

31. The computer program product of claim 30, wherein the computer code for optimizing each linear parameter of the merit function includes computer code for performing linear regression.

32. The computer program product of claim 30, wherein the computer code for optimizing each nonlinear parameter of the merit function includes computer code for performing a Levenberg-Marquardt method.

33. The computer program product of claim 25, wherein the computer code for optimizing the merit function further includes:

computer code for determining a preliminary set of optimal values of the parameters using data in the first and second images;

computer code for generating a joint histogram of pixel value pairs of the first and second images using the preliminary set of optimal values of the parameters, the joint histogram having its mass centered about a curve;

computer code for identifying outliers as points in the joint histogram that exceed a predetermined distance from the curve of the joint histogram;

computer code for determining pixel value pairs in the first and second images corresponding to the outliers; and

computer code for repeating the optimizing the merit function using data in the first and second images without the pixel value pairs corresponding to the outliers.