IMAGE ANALYSIS USEFUL FOR PATTERNED OBJECTS

• US 20150125053A1
• Filed: 10/31/2014
• Published: 05/07/2015
• Est. Priority Date: 11/01/2013
• Status: Active Grant

• Current Assignee: Illumina Incorporated
• Original Assignee: Illumina Incorporated
• Inventors: Tanner, Stephen, Moon, John A., Bowen, M. Shane, Vieceli, John S.
• Granted Patent: US 10,540,783 B2
• Time in Patent Office: Days
• US Class Current: 382/129
• CPC Class Codes: G06T 2207/30024 Cell structures in vitro; Tissue sections in vitro
• CPC Class Codes: G06T 2207/30204 Marker
• CPC Class Codes: G06T 7/74 involving reference images or patches

Abstract

A method of registering features in a repeating pattern can include (a) providing an object having a repeating pattern of features and a fiducial; (b) obtaining a target image of the object, wherein the target image includes the repeating pattern of features and the fiducial; (c) comparing the fiducial in the target image to reference data, wherein the reference data includes xy coordinates for a virtual fiducial; and (d) determining locations for the features in the target image based on the comparison of the virtual fiducial in the reference data to the fiducial in the data from the target image. The fiducial can have at least concentric circles that produce three different signal levels. The locations of the features can be determined at a variance of less than 5 μm.

Claims

1. A method of determining the locations of features in a repeating pattern, comprising

(a) providing an object having a repeating pattern of features in an xy plane and a fiducial, wherein the features comprise an area of 100 μ

m² or less and a pitch of 5 μ

m or less;

(b) obtaining a target image of the object using a detection apparatus, wherein the target image comprises the repeating pattern of features and the fiducial;

(c) providing data from the target image to a computer, wherein the computer comprises reference data, wherein the reference data indicates xy coordinates for a virtual fiducial, and

(d) performing an algorithm on the computer to determine locations for the features in the target image based on comparison of the virtual fiducial in the reference data to the fiducial in the data from the target image, wherein the locations of the features are determined at a variance of less than 5 μ

m.

2. The method of claim 1, wherein the reference data further comprises z coordinates for the virtual fiducial.

3. The method of claim 2, wherein the algorithm is further performed to determine focus for the features in the image based on comparison of the virtual fiducial in the reference data to the fiducial in the data from the target image.

4. The method of claim 1, wherein the comparison of (d) comprises determination of an offset between the virtual fiducial in the reference data and the fiducial in the data from the target image.

5. The method of claim 4, wherein the locations for the features are determined in (d) by shifting the relative orientation between the target image and the reference data, based on the offset, to improve the correlation between the target image and the reference data.

6. The method of claim 1, wherein the algorithm comprises an Affine transform.

7. The method of claim 1, wherein the locations of the features are determined at a variance of less than 100 nm.

8. The method of claim 7, wherein the locations of the features are determined at a variance of less than 40 nm.

9. The method of claim 1, wherein the locations of the features are determined at a variance of less than 1 pixel of the detection apparatus.

10. The method of claim 1, wherein the locations of the features are determined at a variance of less than 0.5 pixel of the detection apparatus.

11. The method of claim 1, further comprising

(e) repeating steps (b), (c) and (d) for a second target image of the object obtained using the detection apparatus at a second setting, wherein the second setting detects signals having a characteristic that is distinct from characteristics of the object detected in (b), wherein the algorithm determines locations for the features in the second target image based on comparison of the virtual fiducial in the reference data to the fiducial in the data from the second target image.

12. The method of claim 11, wherein the second setting in (e) detects optical signals at a different wavelength than a wavelength detected in (b).

13. The method of claim 11, wherein the target image comprises a first subset of the features of the object that are dark and a second subset of the features of the object that produce a specific signal.

14. The method of claim 13, wherein at least some of the features in the first subset of features of the object produce a specific signal in the second target image and at least some of the features in the second subset of features of the object are dark in the second target image.

15. The method of claim 1, wherein the target image is a composite of at least two images.

16. The method of claim 1, further comprising

(e) repeating steps (b), (c) and (d) for a second, third and fourth image of the object obtained using the detection apparatus at a second, third and fourth setting, respectively, wherein each of the settings detects signals having a characteristic that is distinct from a characteristic of the object detected using the other settings, respectively, wherein the algorithm determines locations for the features in the second, third and fourth images based on comparison of the virtual fiducials in the reference data to the fiducial in the data from the respective image.

17. The method of claim 1, wherein at least three fiducials are used in place of the fiducial and at least three virtual fiducials are used in place of the virtual fiducial.

18. The method of claim 1, wherein subsets of the features are arranged in geometric units that are repeated in the repeating pattern, and wherein the fiducial has a footprint that is larger than the geometric unit.

19. The method of claim 1, wherein the fiducial comprises circular symmetry.

20. The method of claim 19, wherein the fiducial comprises at least 2 concentric circles.

21. The method of claim 20, wherein the fiducial comprises 3 different circles that produce three different signal levels, respectively.

22. The method of claim 21, wherein the virtual fiducial simulates predefined signal levels and xy coordinates for the 3 different circles.

23. The method of claim 1, wherein the reference data further comprises a correction factor that adjusts the fiducial or the virtual fiducial for distortions of the detection apparatus.

24. The method of claim 23, wherein the reference data comprises a correction factor that adjusts for stretching distortions of the features along only the x dimension of the xy plane.

25. The method of claim 24, wherein the detection apparatus scans a line along the y dimension of the object, the longest dimension of the line occurring along the x dimension.

26. The method of claim 23, wherein the reference data comprises a correction factor that adjusts for area distortions of the features along the x and y dimensions.

27. The method of claim 26, wherein the detection apparatus comprises an area detector.

28. The method of claim 1, wherein the detection apparatus comprises a scanning detector that scans along they dimension of the object, wherein the scanning detector scans parallel swaths of the object.

29. The method of claim 28, wherein the object further comprises coarse-alignment markers that distinguish the relative locations of the swaths along the x dimension.

30. The method of claim 29, further comprising detecting one or more of the coarse-alignment markers with the detection apparatus, and determining the location of at least one of the swaths.

31. The method of claim 30, further comprising adjusting the relative position of the detection apparatus and the object along the x dimension based on the location determined for the at least one of the swaths.

32. The method of claim 30, wherein the determining of the location of at least one of the swaths is performed by an algorithm on the computer.

33. The method of claim 32, wherein step (d) comprises performing the algorithm on the computer to determine locations for the features in the target image based on the comparison of the virtual fiducial in the reference data to the fiducial in the data from the target image and based on the location of the at least one of the swaths.

34. The method of claim 1, wherein subsets of the features are arranged in geometric units that are repeated in the repeating pattern, and wherein the geometric unit comprises an area of 500 μ

m² or less.

35. The method of claim 1, wherein the repeating pattern comprises a hexagonal arrangement of the features.

36. The method of claim 1, wherein the repeating pattern comprises a rectilinear arrangement of the features.

37. The method of claim 1, further comprising

(e) obtaining a plurality of images of the object using the detection apparatus, wherein each image in the plurality of images comprises the repeating pattern of features and the fiducial;

(f) providing data from the plurality of images to the computer; and

(g) performing an algorithm on the computer to determine locations for the features in each of the plurality of images based on comparison of the virtual fiducial in the reference data to the fiducial in the data from each of the plurality of images, wherein the locations of the features in each of the images are determined.

38. The method of claim 37, further comprising creating a plurality of data files specifying the locations of the features that are determined in (d) and (g).

39. The method of claim 38, further comprising comparing at least one characteristic of respective features across the plurality of data files to determine a property of the respective features.

40. The method of claim 39, wherein the characteristic comprises a sequence of detected signals and the property comprises a nucleic acid sequence.