Method for determining the positions of a plurality of objects in a digital image

• US 10,733,707 B2
• Filed: 12/09/2016
• Issued: 08/04/2020
• Est. Priority Date: 12/10/2015
• Status: Active Grant

• Current Assignee: Qiagen GmbH (Qiagen NV)
• Original Assignee: Qiagen GmbH (Qiagen NV)
• Inventors: Unterer, Thomas, Schuster, Guido, Lohel, Maiko, Labitzke, Bjorn, Zerfass, Thorsten
• Primary Examiner(s): Park, Soo Jin
• Application Number:
  US16/060,887
• Publication Number:
  US 20190012770A1
• Time in Patent Office: 1,334 Days
• Field of Search: None
• CPC Class Codes: G06T 2207/10064 Fluorescence image
• CPC Class Codes: G06T 2207/20021 Dividing image into blocks, subimages or windows
• CPC Class Codes: G06T 2207/20032 Median filtering
• CPC Class Codes: G06T 2207/30072 Microarray; Biochip, DNA array; Well plate
• CPC Class Codes: G06T 3/4007 based on interpolation, e.g. bilinear interpolation image demosaicing G06T3/4015; edge-driven or edge-based scaling G06T3/403
• CPC Class Codes: G06T 3/4023 based on decimating pixels or lines of pixels; based on inserting pixels or lines of pixels
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/136 involving thresholding
• CPC Class Codes: G06T 7/44 using image operators, e.g. filters, edge density metrics or local histograms
• CPC Class Codes: G06T 7/70 Determining position or orientation of objects or cameras camera calibration G06T7/80

Abstract

The present invention relates to a method and a system for determining the positions of a plurality of objects in a digital image by discriminating true positive positions of the plurality of objects from false positive candidate positions of the plurality of objects. In particular, the invention relates to a method for determining the positions of a plurality of objects in a digital image by discriminating true positive positions of the plurality of objects from false positive candidate positions of the plurality of objects, the plurality of objects being configured to receive molecules comprising genetic information.

Claims

1. A computer implemented method for determining positions of a plurality of objects in a digital image, the plurality of objects being configured to receive molecules comprising genetic information, the method comprising:

determining a plurality of pixels in the digital image, each pixel having a local maximum intensity value with respect to intensity values of a plurality of neighboring pixels of the respective pixel;

determining a variance of an intensity value of each pixel having a respective local maxim urn intensity value with respect to the intensity value of the respective pixel and the intensity value of each of the plurality of the respective neighboring pixels;

weighting the intensity value of each of the plurality of pixels having a respective local maximum intensity value by the respective determined variance of the intensity value;

creating a histogram comprising the respective weighted intensity of each pixel of the plurality of pixels each having a respective local maximum intensity value;

determining a plurality of local minimums and a global maximum of the histogram;

selecting a local minimum of the plurality of local minimums as a threshold value for discriminating true positive positions of the plurality of objects from false positive positions of the plurality of objects, wherein the local minimum is selected under consideration of a position of the selected local minimum of the histogram relative to a position of the global maximum; and

using the threshold value for discriminating the true positive positions of the plurality of objects from false-positive candidate positions of the plurality of objects.

2. The method according to claim 1, further comprising:

determining a sub-pixel position of each object by parabola fitting for the determined pixels.

3. The method according to claim 1, further comprising:

removing weighted pixels having a weighted intensity value below a first threshold or above a second threshold.

4. The method according to claim 1, further comprising:

scaling weighted pixels with a logarithmic scale.

5. The method according to claim 1, wherein at least one object of the plurality of objects is a bead configured to receive DNA or RNA.

6. The method according to claim 1, further comprising:

removing at least one pixel of the digital image having an intensity below a first threshold or above a second threshold.

7. The method according to claim 1, further comprising:

removing uneven illumination in the digital image.

8. The method according to claim 7, wherein the removing uneven illumination comprises:

applying a low pass filter to a copy of the digital image;

dividing the digital image by the low pass filtered copy of the digital image;

dividing the digital image into a plurality of N tiles;

determining a first threshold intensity value and a second threshold intensity value for each tile of the plurality of N tiles;

creating a first matrix comprising the first threshold intensity values for the N tiles, and a second matrix comprising the second threshold intensity values for the N tiles;

applying a median filter to the first matrix; and

a median filter to the second matrix;

upscaling the first matrix and the second matrix, the upscaled first matrix and the upscaled second matrix having an original size of the digital image; and

normalizing each intensity value of each pixel of the digital image under consideration of the corresponding value of the upscaled first matrix and of the corresponding value of the upscaled second matrix.

9. The method according to claim 8, further comprising:

applying a low pass filter to the first matrix, and a low pass filter to the second matrix.

10. The method according to claim 8, wherein the upscaling is performed using bicubic interpolation.

11. The method according to claim 8, wherein the normalizing each intensity value of each pixel of the digital image is performed by a linear transformation.

12. A non-transitory computer readable media having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:

determining a plurality of pixels in a digital image;

each pixel having a local maximum intensity value with respect to intensity values of a plurality of neighboring pixels of the respective pixel;

determining a variance of an intensity value of each pixel having a respective local maximum intensity value with respect to the intensity value of the respective pixel and the intensity value of each of the plurality of the respective neighboring pixels;

weighting the intensity value of each of the plurality of pixels having a respective local maximum intensity value by the respective determined variance of the intensity value;

creating a histogram comprising the respective weighted intensity of each pixel of the plurality of pixels each having a respective local maximum intensity value;

determining a plurality of local minimums and a global maximum of the histogram;

selecting a local minimum of the plurality of local minimums as a threshold value for discriminating true positive positions of the plurality of objects from false positive positions of the plurality of objects, wherein the local minimum is selected under consideration of a position of the selected local minimum of the histogram relative to a position of the global maximum; and

using the threshold value for discriminating the true positive positions of the plurality of objects from false-positive candidate positions of the plurality of objects.

13. The non-transitory computer readable media according to claim 12, wherein the operations further comprise:

removing uneven illumination in the digital image.

14. The non-transitory computer readable media according to claim 13, wherein the removing uneven illumination comprises:

applying a low pass filter to a copy of the digital image;

dividing the digital image by the low pass filtered copy of the digital image;

dividing the digital image into a plurality of N tiles;

determining a first threshold intensity value and a second threshold intensity value for each tile of the plurality of N tiles;

creating a first matrix comprising the first threshold intensity values for the N tiles, and a second matrix comprising the second threshold intensity values for the N tiles;

applying a median filter to the first matrix, and a median filter to the second matrix;

upscaling the first matrix the second matrix, the upscaled first matrix and the upscaled second matrix having an original size of the digital image; and

normalizing each intensity value of each pixel of the digital image under consideration of the corresponding value of the upscaled first matrix and of the corresponding value of the upscaled second matrix.

15. The non-transitory computer readable media according to claim 14, wherein the operations further comprise:

applying a low pass filter to the first matrix, and a low pass filter to the second matrix.

16. A system for determining positions of a plurality of objects in a digital image, the plurality of objects being configured to receive molecules comprising genetic information, wherein the system comprises:

a memory; and

at least one processor coupled to the memory and configured to;

determine a plurality of pixels in the digital image, each pixel having a local maximum intensity value with respect to intensity values of a plurality of neighboring pixels of the respective pixel;

determine a variance of an intensity value of each pixel having a respective local maximum intensity value with respect to the intensity value of the respective pixel and the intensity value of each of the plurality of the respective neighboring pixels;

weight the intensity value of each of the plurality of pixels having a respective local maximum intensity value by the respective determined variance of the intensity value;

create a histogram comprising the respective weighted intensity of each pixel of the plurality of pixels each having a respective local maximum intensity value;

determine a plurality of local minimums and a global maximum of the histogram;

select a local minimum of the local minimums as a threshold value for discriminating true positive positions of the plurality of objects from false positive positions of the plurality of objects, wherein the local minimum is selected under consideration of a position of the selected local minimum of the histogram relative to a position of the global maximum; and

determine the pixels which correspond to the true positive positions of the plurality of objects by applying the threshold value to the histogram.

17. The system according to claim 16, wherein the at least one processor is further configured to:

remove uneven illumination.

18. The system according to claim 17, wherein to remove uneven illumination the at least one processor is configured to:

apply a low pass filter to a copy of the digital image;

divide the digital image by the low pass filtered copy of the digital image;

divide the digital image into a plurality of N tiles;

determine a first threshold intensity value and a second threshold intensity value for each tile of the plurality of N tiles;

create a first matrix comprising the first threshold intensity values for the N tiles, and a second matrix comprising the second threshold intensity values for the N tiles;

apply a median filter to the first matrix, and apply a median filter to the second matrix;

upscale the first matrix and the second matrix, the upscaled first matrix and the upscaled second matrix having an original size of the digital image; and

normalize each intensity value of each pixel of the digital image under consideration of the corresponding value of the upscaled first matrix and of the corresponding value of the upscaled second matrix.

19. The system according to claim 18, wherein the at least one processor is further configured to:

apply a low pass filter to the first matrix, and a low pass filter to the second matrix.

20. The system according to claim 18, wherein to normalize each intensity value of each pixel of the digital image the at least one processor is configured to normalize each intensity value of each pixel of the digital image using a linear transformation.