Method and Apparatus for Automated Image Analysis of Biological Specimens

• US 20070206843A1
• Filed: 03/13/2007
• Published: 09/06/2007
• Est. Priority Date: 11/27/1996
• Status: Active Grant

• Current Assignee: Carl Zeiss Microimaging Ais, Inc. (Carl-Zeiss-Stiftung)
• Original Assignee: Carl Zeiss Microimaging Ais, Inc. (Carl-Zeiss-Stiftung)
• Inventors: Ellis, Robert, Ring, James, Riding, Thomas, McLaren, Gina, Douglass, James, Decker, William
• Granted Patent: US 7,428,325 B2
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: G01N 15/1433 using image recognition
• CPC Class Codes: G02B 21/367 providing an output produced by processing a plurality of individual source images, e.g. image tiling, montage, composite images, depth sectioning, image comparison
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/10056 Microscopic image
• CPC Class Codes: G06T 2207/20012 Locally adaptive
• CPC Class Codes: G06T 2207/20016 Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
• CPC Class Codes: G06T 2207/30024 Cell structures in vitro; Tissue sections in vitro
• CPC Class Codes: G06T 5/30 Erosion or dilatation, e.g. thinning
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• CPC Class Codes: G06T 5/77 Retouching; Inpainting; Scratch removal
• CPC Class Codes: G06T 7/136 involving thresholding
• CPC Class Codes: G06V 10/10 Image acquisition document image scanning and transmission H04N1/00; control of digital cameras H04N23/60
• CPC Class Codes: G06V 10/25 Determination of region of interest [ROI] or a volume of interest [VOI]
• CPC Class Codes: G06V 10/267 by performing operations on regions, e.g. growing, shrinking or watersheds
• CPC Class Codes: G06V 10/443 by matching or filtering
• CPC Class Codes: G06V 10/56 relating to colour
• CPC Class Codes: G06V 20/69 Microscopic objects, e.g. biological cells or cellular parts
• CPC Class Codes: G06V 20/693 Acquisition

Abstract

A method and apparatus for automated cell analysis of biological specimens automatically scans at a low magnification to acquire images which are analyzed to determine candidate cell objects of interest. The low magnification images are converted from a first color space to a second color space. The color space converted image is then filtered. The candidate interest pixels are morphologically processed and compared to blob parameters to identify candidate objects of interest which correspond to cells or other structures relevant to medical diagnosis of the biological specimen. The location coordinates of the objects of interest are stored and additional images of the candidate cell objects are acquired at high magnification.

Claims

1. A method, comprising:

loading a slide which includes a medical sample onto a scanning stage;

acquiring an electronic image of at least a portion of said slide;

varying at least one characteristic of said slide, and reacquiring an image, thereby obtaining multiple images;

based on said acquiring and said varying, fitting at least one characteristic of the multiple images to a Gaussian function; and

using said fitting to select an optimum characteristic.

2. A method as in claim 1, wherein said characteristic of said slide is a desired focus, and said varying characteristics comprises changing a distance between said slide and a hardware that obtains the electronic image.

3. A method as in claim 2, wherein said using comprises positioning a sample stage at a specified distance relative to a peak of said Gaussian function as an estimate of a coarse focus position.

4. A method as in claim 3, further comprising, after determining said coarse focus position, determining a fine focus position.

5. A method as in claim 4, wherein said determining the fine focus position comprises finding a least squares fit to a second order polynomial.

6. A method as in claim 5, wherein said fine focus position is taken as a position relative to a peak within the least squares fit.

7. A method as in claim 2, wherein said fitting comprises determining a characteristic relative to a pixel value mean between different portions of the slide.

8. A method as in claim 7, wherein said characteristic used by said fitting comprises a variance.

9. A method comprising;

acquiring images of medical slides at a plurality of different focus positions;

determining a position which produces a maximum value of pixel values relative to a pixel value mean; and

producing a focus control signal that is related to said maximum value to control a focus position.

10. A method as in claim 9, wherein said determining comprises using a pixel value mean as a coarse estimate of coarse focus position, and subsequently refining said coarse focus position to find a fine focus position.

11. A method as in claim 10, wherein said refining comprises fitting to a polynomial, and using a specified portion of the polynomial as a fine estimate of focus position.

12. A method as in claim 10, wherein said determining comprises fitting a plurality of points to a Gaussian function, and using a specified portion on the Gaussian function as the coarse estimate.

13. A method as in claim 12, wherein said specified portion is a peak of the Gaussian function.

14. A method as in claim 12, wherein said specified portion is a specified distance away from a peak of the Gaussian portion function.

15. A method as in claim 9, wherein said value related to pixel value mean is a pixel value variance.

16. A method as in claim 9, wherein said value related pixel value mean is a pixel value standard deviation.

17. An automated microscope system, comprising:

a slide stage, adapted for holding at least one medical slide;

an electronic camera, positioned to obtain an image of a slide that is mounted on the slide stage;

a slide stage moving portion, connected to said slide stage, and moving said slide stage in at least a Z direction, that moves the slide stage closer to the electronic camera and further from the electronic camera;

a processing portion, receiving an electronic signal indicative of said image of the slide, and processing said image to determine a value related to maximum pixel value mean, and to determine a second value that represents a best value in the z direction that produces a maximum focus, and to produce a signal output to said slide stage moving portion indicative of said best value.

18. A system as in claim 17, wherein said slide stage moving portion is responsive to said output from said processing portion, to move to said best value.

19. A system as in claim 17, wherein said processing portion controls said slide stage moving portion to a plurality of different values, to determine said value related to maximum pixel value mean.

20. A system as in claim 19, wherein said processing portion carries out curve fitting to a specified function to determine said value related to maximum pixel mean.

21. A system as in claim 20, wherein said processing portion carries out curve fitting to a Gaussian function, and uses a value related to peak of said Gaussian as said value related to maximum pixel mean.

22. A system as in claim 21, wherein said value related to maximum pixel mean is a pixel value variance.

23. A system as in claim 21, wherein said value related to maximum pixel mean is a pixel value standard deviation.

24. A system as in claim 21, wherein said processing portion carries out two separate focus determinations, with a first curve fitting to a Gaussian function as a coarse focus determination, and a second operation as a finer determination.

25. A system as in claim 18, wherein said slide stage moving portion can also move in x and y directions, which are substantially orthogonal to said z direction.