Operator independent image cytometer

• US 5,548,661 A
• Filed: 09/07/1994
• Issued: 08/20/1996
• Est. Priority Date: 07/12/1991
• Status: Expired due to Term

• Current Assignee: Q3DM, LLC
• Original Assignee: Jeffrey H. Price
• Inventors: Gough, David, Price, Jeffrey H.
• Primary Examiner(s): Mancuso, Joseph
• Assistant Examiner(s): Patel, Jayanti K.
• Application Number:
  US08/302,044
• Time in Patent Office: 713 Days
• Field of Search: 382/128, 382/129, 382/130, 382/132, 382/205, 382/225, 382/260, 382/261, 382/266, 348/80
• US Class Current: 382/133
• CPC Class Codes: G01N 15/147 the analysis being performed on a sample stream
• CPC Class Codes: G01N 21/6428 Measuring fluorescence of fluorescent products of reactions or of fluorochrome labelled reactive substances, e.g. measuring quenching effects, using measuring "optrodes" in vivo A61B5/00; immunoassay G01N33/53
• CPC Class Codes: G01N 21/6458 Fluorescence microscopy fluorescence microscopes per se G02B21/0076 and G02B21/16
• CPC Class Codes: G06T 2207/10056 Microscopic image
• CPC Class Codes: G06T 2207/10064 Fluorescence image
• CPC Class Codes: G06T 2207/30024 Cell structures in vitro; Tissue sections in vitro
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/12 Edge-based segmentation
• CPC Class Codes: G06T 7/168 involving transform domain methods
• CPC Class Codes: G06V 20/69 Microscopic objects, e.g. biological cells or cellular parts

Abstract

An operator independent image cytometer having a method for image segmentation. Image segmentation comprises the steps of filtering a digital image of a cellular specimen and thresholding the resultant image. In addition, the thresholding may include the sorting of features extracted from the filtered image. The present invention also includes a method for cytometer autofocus that combines the benefits of sharpening and contrast metrics. The present invention further includes an arc lamp stabilization and intensity control system. The image cytometer has broad applications in determining DNA content and other cellular measurements on as many as 10⁵ individual cells, including specimens of living cells. Image segmentation applications include PAP smear analysis and particle recognition.

Claims

1. An automated method of separating an object from a background in an image comprising pixels, the method comprising the steps of:

selecting a set of weights in a digital filter, wherein at least a portion of the weights is selected to enhance at least a portion of the object, said object portion characterized by a predetermined multispectral pattern comprising a plurality of spectral bands;

transforming the image with the digital filter to enhance pixels associated with the predetermined multispectral pattern of the object not contained in the background so as to produce a transformed image, wherein the multispectral pattern comprises a plurality of edge gradients and non-edge gradients, wherein at least a portion of non-edge gradient values falls within the range of the edge gradient values, and wherein the weights are selected so as to enhance both the edge and non-edge gradients which are included in the multispectral pattern, thereby allowing object identification with the use of said digital filter; and

thresholding the transformed image, wherein the thresholding step includes extracting object features from the transformed image and sorting the extracted object features so as to separate the object from the background.

2. The method of object separation defined in claim 1, wherein the digital filter comprises a convolution filter.

3. The method of object separation defined in claim 1, wherein the digital filter comprises a Fourier filter.

4. The method of image separation defined in claim 1, wherein the thresholding step includes an object specific threshold.

5. The method of image separation defined in claim 1, additionally comprising the step of hole filling the object.

6. The method of image separation defined in claim 1, wherein the object is a cell.

7. The method of image separation defined in claim 6, additionally comprising the step of measuring cell nucleus parameters.

8. The method of image separation defined in claim 7, wherein the step of measuring cell nucleus parameters includes measuring the DNA content of the cell.

9. The method of image separation defined in claim 1, wherein the digital filter comprises an averaging portion of a first scale and a sharpening and averaging portion of a second scale.

10. The method of image separation defined in claim 9, wherein the averaging portion is performed on a 2×

2 scale, and the sharpening and averaging portion is performed on a 4×

4 scale.

11. The method of image separation defined in claim 1, wherein the background comprises a plurality of background gradients and wherein at least a portion of the background gradient values falls within the range of the edge gradient values.

12. An operator-independent image cytometer, comprising:

a microscope, having a microscope stage, for optically magnifying at least a portion of a specimen having a plurality of cells;

a camera for creating an image of the magnified specimen, wherein the image includes a background;

means for digitizing the image so as to create a digital image;

means for digital filtering the digital image into a filtered image with a filter comprising a set of weights, wherein at least a portion of the weights is selected to enhance at least a portion of the cells, said portion characterized by a multispectral pattern comprising a plurality of spectral bands so as to create contrast between the cells and the background, wherein the multispectral pattern comprises a plurality of edge gradients and non-edge gradients, wherein at least a portion of the non-edge gradient values falls within the range of edge gradient values, and wherein the weights are selected so as to enhance both the edge and non-edge gradients which are included in the multispectral pattern; and

,

means for thresholding the filtered image.

13. The image cytometer defined in claim 12, wherein the digital filtering means includes a convolution filter.

14. The image cytometer defined in claim 12, wherein the digital filtering means includes a Fourier filter.

15. The image cytometer defined in claim 12, additionally comprising means for controlling the microscope stage in an X,Y plane so that the specimen can be scanned in a sequence of fields.

16. The image cytometer defined in claim 12, additionally comprising means for controlling the microscope stage in a Z dimension so as to autofocus the microscope.

17. The image cytometer defined in claim 12, wherein the digitizing means includes a shade corrector.

18. The image cytometer defined in claim 12, wherein the thresholding means determines a threshold intensity to segment the cells from the background.

19. The image cytometer defined in claim 12, wherein the filtered image comprises pixels indicative of the cells and the background, and wherein the thresholding means additionally includes means for averaging intensities of pixels indicative of thresholded cells so as to eliminate a portion of the pixels from each thresholded cell.

20. The image cytometer defined in claim 12, additionally comprising means for hole filling the cells.

21. The image cytometer defined in claim 12, wherein the specimen is stained with a fluorescent dye.

22. The image cytometer defined in claim 12, wherein the camera is a CCD camera.

23. The image cytometer defined in claim 12, wherein the cells are live cells.

24. An operator-independent image cytometer, comprising:

a microscope for creating a magnified image of a specimen;

a stage controller for moving the specimen relative to the microscope;

a camera for acquiring the magnified image; and

means for analyzing the acquired magnified image including a pattern filter comprising a set of weights, wherein at least a portion of the weights is selected to enhance at least a portion of the image, said portion characterized by a multispectral pattern comprising a plurality of spectral bands, wherein the multispectral pattern comprises a plurality of edge gradients and non-edge gradients, wherein at least a portion of the non-edge gradient values falls within the range of edge gradient values, and wherein the weights are selected so as to enhance both the edge and non-edge gradients which are included in the multispectral pattern.

25. The operator-independent image cytometer defined in claim 24, wherein the analyzing means further comprises:

means for transforming the image into a transformed image including object features having a first set of intensities and background features having a second set of intensities; and

means for thresholding the transformed image so as to separate the object features from the background features.

26. An image separation system, comprising:

means for acquiring a digital image wherein the digital image comprises a plurality of pixels indicative of a set of objects and a background;

a digital filter comprising a set of weights, wherein at least a portion of the weights is selected to enhance at least a portion of at least one object, said object portion characterized by a multispectral pattern comprising a plurality of spectral bands, said filter receiving the digital image and producing a filtered image and wherein the multispectral pattern comprises a plurality of edge gradients and non-edge gradients, wherein at least a portion of the non-edge gradient values falls within the range of edge gradient values, and wherein the weights are selected so as to enhance both the edge and non-edge gradients which are included in the multispectral pattern;

wherein each pixel of the digital image and the filtered image has at least one associated numeric value corresponding to a characteristic parameter of the objects and the background;

wherein a difference between the numeric values associated with the pixels of the objects and the numeric values associated with the pixels of the background is greater in the filtered image than in the digital image;

a histogrammer producing a histogram of the filtered image, wherein the histogram represents the filtered image numeric values;

first threshold means for determining a threshold numeric value from a histogram and separating a set of thresholded objects from the filtered image; and

second threshold means for determining a local threshold value for each of the thresholded objects and selectively removing a pixel from each thresholded object if the pixel has a filtered image numeric value that is on a preselected side of the local threshold value in the preselected range of values.

27. The image separation system defined in claim 26, wherein the characteristic parameter is indicative of intensity.

28. An automated method of separating an object from a background in an image comprising pixels, the method comprising the steps of:

selecting a set of weights in a digital filter, wherein at least a portion of the weights is selected to enhance at least a portion of the object, said object portion characterized by a predetermined multispectral pattern comprising a plurality of spectral bands;

transforming the image with the digital filter to enhance pixels associated with the predetermined multispectral pattern of the object not contained in the background so as to produce a transformed image, wherein the multispectral pattern comprises a plurality of edge gradients and non-edge gradients, wherein the background comprises a plurality of background gradients, wherein at least a portion of the background gradient values falls within the range of the edge gradient values, and wherein the weights are selected so as to enhance both the edge and non-edge gradients which are included in the multispectral pattern, thereby allowing object identification with the use of said digital filter; and

thresholding the transformed image, wherein the thresholding step includes extracting object features from the transformed image and sorting the extracted object features so as to separate the object from the background.

29. The method of image separation defined in claim 28, wherein at least a portion of the non-edge gradient values falls within the range of the edge gradient values.