IMAGE QUALITY ASSESSMENT OF MICROSCOPY IMAGES

• US 20140270425A1
• Filed: 03/18/2013
• Published: 09/18/2014
• Est. Priority Date: 03/18/2013
• Status: Active Grant

• Current Assignee: Leica Microsystems Gmbh (Danaher Corporation)
• Original Assignee: General Electric Company
• Inventors: Kenny, Kevin Bernard, Rothney, Megan Pearl
• Granted Patent: US 9,064,304 B2
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: G06T 2200/32 involving image mosaicing
• CPC Class Codes: G06T 2207/30024 Cell structures in vitro; Tissue sections in vitro
• CPC Class Codes: G06T 2207/30072 Microarray; Biochip, DNA array; Well plate
• CPC Class Codes: G06T 7/0002 Inspection of images, e.g. flaw detection
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/37 using transform domain methods
• CPC Class Codes: G06V 10/993 Evaluation of the quality of the acquired pattern
• CPC Class Codes: G06V 20/693 Acquisition

Abstract

Automated assessment of registration quality, focus, and area defects in sequentially acquired images, such as images acquired by a digital microscope, is disclosed. In one embodiment, acquired images are registered and whole-image defects are automatically detected based on a figure of merit generated by the registration process. In related implementations, area defects may be automatically detected by calculating correlations in localized image regions for images acquired in different imaging rounds.

Claims

1. A computer-implemented method for assessing image quality, comprising:

acquiring a first image and a second image, wherein at least a portion of the first image and the second image overlap;

determining a rotation and a scale relating the first image and the second image;

rotating and scaling a respective Fourier transform of the first image to correspond to a respective Fourier transform of the second image;

determining a translation for the respective first image and the second image based upon the rotated and scaled Fourier transforms of the first image and the second image; and

determining a score quantifying the quality of the registration of the first image and the second image.

2. The computer-implemented method of claim 1, wherein the first image and the second image are acquired using a microscope.

3. The computer-implemented method of claim 1, wherein the first image and the second image are acquired in different imaging rounds of a sequential staining imaging protocol.

4. The computer-implemented method of claim 1, wherein determining the rotation and the scale comprises:

Fourier transforming the first image and the second image to generate a respective first spatial frequency bin and second spatial frequency bin;

extracting a first modulus from the first spatial frequency bin and a second modulus from the second spatial frequency bin to generate a first translation invariant signature and a second translation invariant signature;

performing a log-polar transformation of the first translation invariant signature and the second translation invariant signature;

in log-polar space, performing a Fourier domain correlation operation; and

determining the rotation and the scale based on the result of the Fourier domain correlation operation.

5. The computer-implemented method of claim 1, wherein determining the translation comprises:

performing a phase correlation on the rotated and scaled Fourier transforms of the first image and the second image to obtain the translation.

6. The computer-implemented method of claim 5, wherein performing the phase correlation comprises:

performing an inverse Fourier transform to return the rotated and scaled Fourier transforms of the first image and the second image to the pixel domain; and

locating a correlation peak in the pixel domain, wherein the location of the correlation peak corresponds to the translation.

7. The computer-implemented method of claim 6, wherein determining the score comprises:

determining the height of the correlation peak, wherein the height of the correlation peak corresponds to the score.

8. The computer-implemented method of claim 7, wherein the height of the correlation peak comprises the zero-mean normalized cross-power correlation coefficient for the registered first image and second image.

9. An image analysis system, comprising:

a memory storing one or more routines; and

a processing component configured to execute the one or more routines stored in the memory, wherein the one or more routines, when executed by the processing component, cause acts to be performed comprising;

acquiring or accessing a first image and a second image, wherein at least a portion of the first image and the second image overlap;

determining a rotation and a scale relating the first image and the second image;

rotating and scaling a respective Fourier transform of the first image to correspond to a respective Fourier transform of the second image;

determining a translation for the respective first image and the second image based upon the rotated and scaled Fourier transforms of the first image and the second image; and

determining a score quantifying the quality of the registration of the first image and the second image.

10. The image analysis system of claim 9, wherein determining the rotation and the scale comprises:

Fourier transforming the first image and the second image to generate a respective first spatial frequency bin and second spatial frequency bin;

extracting a first modulus from the first spatial frequency bin and a second modulus from the second spatial frequency bin to generate a first translation invariant signature and a second translation invariant signature;

performing a log-polar transformation of the first translation invariant signature and the second translation invariant signature;

in log-polar space, performing a Fourier domain correlation operation; and

determining the rotation and the scale based on the result of the Fourier domain correlation operation.

11. The image analysis system of claim 9, wherein determining the translation comprises:

performing a phase correlation on the rotated and scaled Fourier transforms of the first image and the second image to obtain the translation.

12. The image analysis system of claim 11, wherein performing the phase correlation comprises:

performing an inverse Fourier transform to return the rotated and scaled Fourier transforms of the first image and the second image to the pixel domain; and

locating a correlation peak in the pixel domain, wherein the location of the correlation peak corresponds to the translation.

13. The image analysis system of claim 12, wherein determining the score comprises:

determining the height of the correlation peak, wherein the height of the correlation peak corresponds to the score.

14. A computer-implemented method for detecting area defects, comprising:

for each pixel in a first image, determining a comparison region;

performing a correlation between each comparison region and a corresponding region of a second image; and

generating a score for each pixel in the first image based on the respective correlation between the respective comparison region associated with each pixel and the corresponding region of the second image, wherein the score for each pixel corresponds to a likelihood of a defect within the first image at the respective pixel.

15. The computer-implemented method of claim 14, wherein each comparison region comprises a square array of pixels centered about a respective pixel.

16. The computer-implemented method of claim 15, wherein a half width of each side of the square array is equal to or between 3 pixels and 60 pixels.

17. The computer-implemented method of claim 14, wherein the correlation is a Pearson product moment correlation.

18. The computer-implemented method of claim 14, comprising:

applying a threshold to each pixel of the first image based on the respective score for each pixel; and

based on the application of the threshold, categorizing each pixel as being acceptable or not acceptable.

19. An image analysis system, comprising:

a memory storing one or more routines; and

a processing component configured to execute the one or more routines stored in the memory, wherein the one or more routines, when executed by the processing component, cause acts to be performed comprising;

for each pixel in a first image, determining a comparison region;

performing a correlation between each comparison region and a corresponding region of a second image; and

generating a score for each pixel in the first image based on the respective correlation between the respective comparison region associated with each pixel and the corresponding region of the second image, wherein the score for each pixel corresponds to a likelihood of a defect within the first image at the respective pixel.

20. The image analysis system of claim 19, wherein each comparison region comprises a square array of pixels centered about a respective pixel.

21. The image analysis system of claim 19, wherein the correlation is a Pearson product moment correlation.

22. The computer-implemented method of claim 19, wherein the one or more routines, when executed by the processing component, cause further acts to be performed comprising:

applying a threshold to each pixel of the first image based on the respective score for each pixel; and

based on the application of the threshold, categorizing each pixel as being acceptable or not acceptable.