METHOD AND MEANS FOR IMAGE SEGMENTATION IN FLUORESCENCE SCANNING CYTOMETRY

• US 20020186874A1
• Filed: 07/27/1998
• Published: 12/12/2002
• Est. Priority Date: 09/07/1994
• Status: Abandoned Application

• Current Assignee: Q3DM, LLC
• Original Assignee: Q3DM, LLC
• Inventors: GOUGH, DAVID A., PRICE, JEFFREY H.
• Application Number:
  US09/123,564
• Publication Number:
  US 20020186874A1
• Time in Patent Office: Days
• US Class Current: 382/133
• CPC Class Codes: G01N 15/147 the analysis being performed on a sample stream
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• 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 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/194 involving foreground-background segmentation
• CPC Class Codes: G06V 20/69 Microscopic objects, e.g. biological cells or cellular parts

Abstract

In an image segmentation system that processes image objects by digital filtration, a digital filter is defined. The digital filter includes a neighborhood operator for processing intensity values of neighborhoods of pixels in a pixel array. A first pixel array is received defining a pixelated image including one or more objects and a background and a second pixel array is received that defines a reference image. The reference image includes at least one object included in the pixelated image in a background. In the reference image, pixels included in the at least one object are distinguished from pixels included in the background by a predetermined amount of contrast. Pixels of the first and second images are compared to determine a merit value; the merit value is used to compute neighborhood operator values; and, the neighborhood operator is applied to images in order to create or enhance contrast between objects and background in the images.

Claims

1. A method of separating an object from a background in a pixelated image, the method comprising the computer-executed steps of:

selecting a digital filter for creating contrast in an image, the digital filter including a neighborhood operator for processing neighborhoods of pixels in pixel array;

receiving a first pixel array defining a pixelated image including one or more objects and a background;

receiving a second pixel array defining a reference image, the reference image including at least one object included in the pixelated image and a background, in which pixels included in the at least one object are distinguished from pixels included in the background by a predetermined amount of contrast;

comparing pixels of the pixelated image with pixels of the reference image to determine a merit value; and

changing the neighborhood operator of the digital filter to a new neighborhood operator in response to the merit value.

2. The method of claim 1, wherein the neighborhood operator is a convolution kernel.

3. The method of claim 1, wherein the neighborhood operator is a second order filter.

4. The method of claim 1, wherein the neighborhood operator is a Volterra series.

5. The method of claim 1, wherein the step of comparing includes thresholding the merit value by:

assigning a value of zero to the merit value when;

a pixel of the reference image has a magnitude equal to a predetermined background value and a corresponding pixel in the pixelated image has a value equal to or less than a background pixel magnitude;

or

a pixel of the reference image has a magnitude equal to a predetermined object value and a corresponding pixel of the pixelated image has a value equal to or greater than an object pixel magnitude;

otherwise

determining a positive, non zero, value for the merit value.

6. The method of claim 5 further including the step of deriving a transformed image by filtering a third pixel array with the digital filter, using the new neighborhood operator.

7. The method of claim 6, wherein the digital filter comprises a first order filter.

8. The method of claim 6, wherein the digital filter comprises a second order filter.

9. The method of claim 6, wherein the digital filter comprises a Volterra series.

10. The method of claim 1, wherein the one or more objects are cells, or portions of cells.

11. The method of claim 1, wherein the step of receiving the reference image includes:

buffering the second pixel array;

defining an array of error weights, each weight corresponding to one of the pixels in the reference image; and

multiplying each of the errors at the corresponding reference image pixel locations to create a weighted merit value.

12. The method of claim 11, wherein the weights are 1 at or adjacent edges of the one or more objects and 0 elsewhere in the one or more objects.

13. The method of claim 11, wherein the weights are 1 at or adjacent edges of the one or more objects, 0 inside the one or more objects, and 0 in the background.

14. The method of claim 11, wherein the step of comparing includes thresholding the merit value by:

assigning a value of zero to the merit value when;

a pixel with the reference image has a magnitude equal to a predetermined background value and a corresponding pixel in the pixelated image has a value equal to or less than a background pixel magnitude;

or

a pixel of the reference image has a magnitude equal to a predetermined object value and a corresponding pixel in the pixelated image has a value equal to or greater than an object pixel magnitude;

otherwise determining a positive, non-zero, value for the merit value.

16. The method of claim 15, wherein the neighborhood operator is a convolution kernel.

17. The method of claim 15, wherein the neighborhood operator is a second order filter.

18. The method of claim 11, wherein the neighborhood operator is a Volterra series.

19. The method of claim 11, wherein the step of comparing includes thresholding the merit value by:

assigning a value of zero to the merit value when;

a pixel of the reference image has a magnitude equal to a predetermined background value and a corresponding pixel and the pixelated image has a value equal to or less than a background pixel magnitude;

or

a pixel of the reference image has a magnitude equal to a predetermined object value and a corresponding pixel of the pixelated image has a value equal to or greater than an object pixel magnitude;

otherwise determining a positive, non-zero, value for the merit value.

20. The method of claim 19, wherein the digital filter comprises a first order filter.

21. The method of claim 19, wherein the digital filter comprises a second order filter.

22. The method of claim 19, wherein the digital filter comprises a Volterra series.

23. The method of claim 19, wherein the one or more objects are cells, or portions of cells.

24. The method of claim 15, wherein the step of receiving the reference image includes:

buffering the second pixel array;

defining an array of error weights, each weight corresponding to one of the pixels in the reference image; and

multiplying each of the errors at the corresponding reference image pixel locations to create a weighted merit value.

25. The method of claim 24, wherein the weights are 1 at or adjacent edges of the one or more objects and 0 elsewhere in the one or more objects.

26. The method of claim 24, wherein the weights are 1 at or adjacent edges of the one or more objects, 0 inside the one or more objects, and 0 elsewhere in the background.

27. The method of claim 24, wherein the comparing step includes:

assigning a value of zero to the merit value when;

a pixel of the reference image has a magnitude equal to a predetermined background value and a corresponding pixel in the pixelated image has a value equal to or less than a background pixel magnitude;

or

a pixel of the reference image has a magnitude equal to a predetermined object value and a corresponding pixel of the pixelated image has a value equal to or greater than an object pixel magnitude;

otherwise

determining a positive, non zero, value for the merit value.

28. The method of claim 15, further including the steps of:

receiving a third pixel array defining an image to be analyzed, the image to be analyzed including one or more objects and a background; and

applying the neighborhood operator of the digital filter to the third pixel array to create or enhance contrast between the one or more objects in the background.

15. A method of separating an object from a background in a pixelated image, the method comprising computer-executed steps of:

defining a type of digital filter, the digital filter including a neighborhood operator for processing neighborhoods of pixels in a pixel array;

receiving a first pixel array defining a pixelated image including one or more objects and a background;

receiving a second pixel array defining a reference image, the reference image including at least one object included in the pixelated image and a background, in which pixels included in the at least one object are distinguished from pixels included in the background by a predetermined amount of contrast;

comparing pixels of the first pixel array with pixels of the second pixel array to determine a merit value;

computing values of neighborhood operator elements in response to the merit value;

receiving a third pixel array defining an image including one or more objects and a background; and

applying the neighborhood operator to the third pixel array to create or enhance contrast between the one or more objects and the background.

29. An image segmentation system, comprising:

means for acquiring an array of pixels defining a pixelated image including one or more objects and a background;

a digital filter for producing a transformed array of pixels representing the transformation of a pixelated image, the digital filter including a neighborhood operator for processing intensities of pixels in a pixel array;

means coupled to the means for acquiring and to the digital filter for setting values of the neighborhood operator in response to a first pixel array defining a pixelated image including one or more objects and a background and a second pixel array defining a reference image, the reference image including at least one object included in the pixelated image and a background in which pixels included in the at least one object are distinguished from pixels included in the background by a predetermined amount of contrast; and

means for applying the neighborhood operator of the digital filter to a third array of pixels defining an image to be analyzed, the image to be analyzed including one or more objects and a background, such that the neighborhood operator creates or enhances contrast between the one or more objects in the background.