Systems, methods and apparatus for identifying a specimen container cap

Patent metadata

Field	Value
Publication number	US-11035870-B2
Application number	US-201716320198-A
Country	US
Kind code	B2
Filing date	Jul 7, 2017
Priority date	Jul 25, 2016
Publication date	Jun 15, 2021
Grant date	Jun 15, 2021

Abstract

A model-based method of determining characteristics of a specimen container cap to identify the container cap. The method includes providing a specimen container including a container cap; capturing backlit images of the container cap taken at different exposures lengths and using a plurality of different nominal wavelengths; selecting optimally-exposed pixels from the images at different exposure lengths at each nominal wavelength to generate optimally-exposed image data for each nominal wavelength; classifying the optimally-exposed pixels as at least being one of a tube, a label or a cap; and identifying a shape of the container cap based upon the optimally-exposed pixels classified as being the cap and the image data for each nominal wavelength. Quality check modules and specimen testing apparatus adapted to carry out the method are described, as are numerous other aspects.

First claim

What is claimed is:

1. A method of identifying a specimen container cap, the method comprising:
providing a specimen container including a container cap;
capturing backlit images of the container cap taken at different exposures lengths and using a plurality of different nominal wavelengths;
selecting optimally-exposed pixels from the images at different exposure lengths at each nominal wavelength to generate optimally-exposed image data for each nominal wavelength;
classifying the optimally-exposed pixels as at least being one of a tube, a label or a cap; and
identifying a shape of the container cap based upon the optimally-exposed pixels classified as being the cap and the image data for each nominal wavelength.

2. The method of claim 1 , further including identifying a cap type of the specimen container based on the identified shape of the container cap.

3. The method of claim 1 , wherein the capturing backlit images of the container cap includes taking multiple images from a number of different viewpoints.

4. The method of claim 3 , wherein the number of different viewpoints includes three or more different viewpoints.

5. The method of claim 1 , wherein the classifying of the optimally-exposed pixels is based upon a multi-class classifier generated from multiple training sets.

6. The method of claim 5 , wherein the multi-class classifier includes a support vector machine.

7. The method of claim 1 , wherein the plurality of different nominal wavelengths includes three or more different nominal wavelengths.

8. The method of claim 7 , wherein the plurality of different nominal wavelengths includes nominal wavelengths in the red, green, and blue light spectrums.

9. The method of claim 1 , wherein the intensity and duration of light used in at least some of the different exposures is sufficient to reveal an internal structure of the container cap.

10. A quality check module adapted to identify a specimen container cap, the quality check module comprising:
a plurality of cameras arranged at multiple viewpoints around an imaging location adapted to receive a specimen container including a container cap, each of the plurality of cameras configured to capture multiple images of the container cap at different exposures lengths and using a plurality of different nominal wavelengths from the multiple viewpoints; and
a computer coupled to the plurality of cameras, the computer configured and operable to:
select optimally-exposed pixels from the images at different exposure lengths at each nominal wavelength to generate optimally-exposed image data for each nominal wavelength;
classify the optimally-exposed pixels as at least being one of a tube, a label or a cap; and
identify a shape of the container cap based upon the optimally-exposed pixels classified as being the cap and the image data for each nominal wavelength.

11. The quality check module of claim 10 , including a housing surrounding the imaging location.

12. The quality check module of claim 10 , including a back light source surrounding the imaging location.

13. The quality check module of claim 10 , including front light source surrounding the imaging location.

14. The quality check module of claim 10 , wherein the imaging location is on a track and the specimen container is adapted to be received in a receptacle of a carrier moveable on the track.

15. The quality check module of claim 10 , wherein the computer is further operable to identify a cap type of the specimen container based on the identified shape of the container cap.

16. The quality check module of claim 10 , wherein the classifying of the optimally-exposed pixels is based upon a multi-class classifier generated from multiple training sets.

17. The quality check module of claim 16 , wherein the multi-class classifier includes a support vector machine.

18. The quality check module of claim 10 , wherein the plurality of different nominal wavelengths includes three or more different nominal wavelengths and wherein the plurality of different nominal wavelengths includes nominal wavelengths in the red, green, and blue light spectrums.

19. The quality check module of claim 10 , wherein the intensity and duration of light used in at least some of the different exposures is sufficient to reveal an internal structure of the container cap.

20. A specimen testing apparatus, comprising:
a track;
specimen carriers moveable on the track, the specimen carriers configured to carry specimen containers including container caps; and
a quality check module arranged on the track and adapted to identify a specimen container cap, the quality check module comprising:
a plurality of cameras arranged at multiple viewpoints around an imaging location adapted to receive a specimen container including a container cap, each of the plurality of cameras configured to capture multiple images of the container cap at different exposures lengths and using a plurality of different nominal wavelengths from the multiple viewpoints; and
a computer coupled to the plurality of cameras, the computer configured and operable to:
select optimally-exposed pixels from the images at different exposure lengths at each nominal wavelength to generate optimally-exposed image data for each nominal wavelength;
classify the optimally-exposed pixels as at least being one of a tube, a label or a cap; and
identify a shape of the container cap based upon the optimally-exposed pixels classified as being the cap and the image data for each nominal wavelength.

Assignees

• Siemens Healthcare Diagnostics Inc

Inventors

• Kluckner Stefan
• Chang Yao-Jen
• Chen Terrence
• Pollack Benjamin S

Classifications

• G01N35/02 Primary

  using a plurality of sample containers moved by a conveyor system past one or more treatment or analysis stations {(G01N35/0098 and G01N35/0099 take precedence)} · CPC title

• G01N35/00732 Primary

  Identification of carriers, materials or components in automatic analysers · CPC title

• G06V20/66

  Trinkets, e.g. shirt buttons or jewellery items (recognising microscopic objects G06V20/69) · CPC title

• G06V10/764

  using classification, e.g. of video objects · CPC title

• G06F18/2411

  based on the proximity to a decision surface, e.g. support vector machines · CPC title