Method for assessing image focus quality

• US 7,769,219 B2
• Filed: 12/11/2006
• Issued: 08/03/2010
• Est. Priority Date: 12/11/2006
• Status: Active Grant

• Current Assignee: Cytyc Corporation (Hologic Incorporated)
• Original Assignee: Cytyc Corporation (Hologic Incorporated)
• Inventors: Zahniser, Michael
• Primary Examiner(s): Le; Vu
• Assistant Examiner(s): TSAI, TSUNG YIN
• Application Number:
  US11/609,274
• Publication Number:
  US 20080137938A1
• Time in Patent Office: 1,331 Days
• Field of Search: 382/133
• US Class Current: 382/133
• CPC Class Codes: G02B 21/244 using image analysis techniques
• 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 for determining the quality of focus of a digital image of a biological specimen includes obtaining a digital image of a specimen using a specimen imaging apparatus. A measure of image texture is calculated at two different scales, and the measurements are compared to determine how much high-resolution data the image contains compared to low-resolution data. The texture measurement may, for example, be a Brenner auto-focus score calculated from the means of adjacent pairs of pixels for the high-resolution measurement and from the means of adjacent triples of pixels for the low-resolution measurement. A score indicative of the quality of focus is then established based on a function of the low-resolution and high-resolution measurements. This score may be used by an automated imaging device to verify that image quality is acceptable. The device may adjust the focus and acquire new images to replace any that are deemed unacceptable.

Claims

1. A method for determining the quality of focus of a digital image of a biological specimen comprising:

(a) obtaining a digital image of the specimen using the specimen imaging apparatus, the digital image comprising a plurality of pixels;

(b) calculating a measure of small-scale texture in the image, wherein the small-scale texture is measured by squaring the differences of the means of adjacent pairs of pixels across the entire digital image;

(c) calculating a measure of larger-scale texture in the image, wherein the larger-scale texture is measured by squaring the differences of the means of adjacent triples of pixels across the entire digital image; and

(d) establishing an estimate of the image focus quality based on a comparison of the small-scale and larger-scale texture measurements.

2. The method of claim 1, wherein a function of the small-scale and larger-scale texture measurements is matched to an experimentally derived data set to determine an estimated displacement from the ideal focal height.

3. The method of claim 2, further comprising the step of adjusting the focal height based at least in part of the estimated displacement value and acquiring subsequent images.

4. The method of claim 3, wherein the step is repeated until the focal height converges on the ideal focal height.

5. The method of claim 2, wherein a best-fit equation is calculated for the experimentally derived data set and its inverse is used to map measured scores to predicted displacements.

6. The method of claim 5, wherein a bell-shaped curve is fit to the experimentally derived data.

7. The method of claim 2, wherein the function applied to the small-scale and larger-scale texture measurements is to take their quotient.

8. The method of claim 1, wherein an automated imaging device obtains a plurality of digital images of different locations within a specimen and the focus quality is estimated for at least some of the images.

9. The method of claim 8, wherein the images are accepted or rejected based on the estimated focus quality.

10. The method of claim 9, wherein rejection of an image causes acquisition of a new image at the same location but at a different focal plane.

11. The method of claim 9, further comprising the step of saving the locations of all rejected images and re-acquiring images at those locations once the device has finished scanning the remainder of the sample.

12. The method of claim 9, wherein the number of rejected images is monitored for a given sample and if that number crosses a certain threshold, the imaging device will reprocess the entire sample.

13. The method of claim 8, further comprising the step of adjusting the focal height based at least in part of the estimated quality and acquiring subsequent images.

14. The method of claim 13, wherein the imaging device adds an offset to the height at which it would otherwise have acquired each image and adjusts this offset up or down depending on whether the previous adjustments resulted in an increase or decrease in focus quality.

15. The method of claim 8, wherein the automated imaging device monitors focus quality estimates and identifies hardware malfunctions based on the monitoring.

16. The method of claim 15, wherein a suspected hardware malfunction causes the imaging device to flag the results from the samples processed.

17. The method of claim 15, further comprising the step of alerting a user to the suspected malfunction.