Flow metering using a difference image for liquid parameter estimation

• US 9,856,990 B2
• Filed: 08/09/2017
• Issued: 01/02/2018
• Est. Priority Date: 12/21/2011
• Status: Active Grant

• Current Assignee: Deka Products Limited Partnership
• Original Assignee: Deka Products Limited Partnership
• Inventors: Peret, Bob D., Kane, Derek G., Kamen, Dean, Murphy, Colin H., Kerwin, John M.
• Primary Examiner(s): PONTIUS, JAMES M
• Application Number:
  US15/672,994
• Publication Number:
  US 20170335988A1
• Time in Patent Office: 146 Days
• CPC Class Codes: A61M 2005/14208 with a programmable infusion control system, characterised by the infusion program
• CPC Class Codes: A61M 2205/3306 Optical measuring means
• CPC Class Codes: A61M 2205/3334 Measuring or controlling the flow rate
• CPC Class Codes: A61M 2205/3576 with non implanted data transmission devices, e.g. using external transmitter or receiver
• CPC Class Codes: A61M 2205/50 with microprocessors or computers
• CPC Class Codes: A61M 2205/502 User interfaces, e.g. screens or keyboards
• CPC Class Codes: A61M 39/281 Automatic tube cut-off devices, e.g. squeezing tube on detection of air
• CPC Class Codes: A61M 39/284 Lever clamps
• CPC Class Codes: A61M 5/14 Infusion devices, e.g. infusing by gravity; Blood infusion; Accessories therefor
• CPC Class Codes: A61M 5/1411 Drip chambers A61M5/162, A61M5/1689, A61M5/40 take precedence
• CPC Class Codes: A61M 5/14212 Pumping with an aspiration and an expulsion action
• CPC Class Codes: A61M 5/16804 Flow controllers
• CPC Class Codes: A61M 5/16877 Adjusting flow; Devices for setting a flow rate
• CPC Class Codes: A61M 5/16881 Regulating valves on-off valves, e.g. clamps A61M39/28
• CPC Class Codes: A61M 5/16886 for measuring fluid flow rate, i.e. flowmeters
• CPC Class Codes: A61M 5/1689 Drip counters
• CPC Class Codes: A61M 5/172 electrical or electronic A61M5/16804, A61M5/16831 take precedence
• CPC Class Codes: B65D 47/2018 comprising a valve or like element which is opened or closed by deformation of the container or closure
• CPC Class Codes: F16K 1/00 Lift valves or globe valves, i.e. cut-off apparatus with closure members having at least a component of their opening and closing motion perpendicular to the closing faces in combination with sliding valves F16K3/246, F16K3/267 ; diaphragm valves F16K7/00
• CPC Class Codes: F16K 27/00 Construction of housing methods for welding housings B23K; Use of materials therefor
• CPC Class Codes: F16K 31/02 : electric F16K31/004 takes precedence; magnetic
• CPC Class Codes: F16K 7/04 : constrictable by external radial force
• CPC Class Codes: F16K 7/06 : by means of a screw-spindle, cam, or other mechanical means F16K7/045 takes precedence
• CPC Class Codes: F16M 11/041 : Allowing quick release of the apparatus
• CPC Class Codes: F16M 13/00 : Other supports for positioning apparatus or articles heads thereof F16M11/02 ; undercarriages thereof F16M11/20 ; adapted to be stuck in the ground A45F3/44 ; sockets or holders for poles or posts E04H12/22; Means for steadying hand-held apparatus or articles supports for measuring instruments G01D11/30; supports for casings of remote control switching devices H01H9/025
• CPC Class Codes: F16M 13/022 : repositionable
• CPC Class Codes: G01F 1/661 : using light
• CPC Class Codes: G01F 11/30 : with supply and discharge valves of the lift or plug-lift type
• CPC Class Codes: G05B 15/02 : electric
• CPC Class Codes: G05D 7/0635 : by action on throttling means G05D7/0688, G05D7/0694 take precedence
• CPC Class Codes: G06F 18/22 : Matching criteria, e.g. proximity measures
• CPC Class Codes: G06T 2207/20182 : Noise reduction or smoothing in the temporal domain; Spatio-temporal filtering
• CPC Class Codes: G06T 2207/20224 : Image subtraction
• CPC Class Codes: G06T 2207/30004 : Biomedical image processing
• CPC Class Codes: G06T 2207/30232 : Surveillance
• CPC Class Codes: G06T 3/18 : Image warping, e.g. rearranging pixels individually
• CPC Class Codes: G06T 5/50 : using two or more images, e.g. averaging or subtraction
• CPC Class Codes: G06T 5/70 : Denoising; Smoothing
• CPC Class Codes: G06T 7/0012 : Biomedical image inspection
• CPC Class Codes: G06T 7/20 : Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G06T 7/60 : Analysis of geometric attributes
• CPC Class Codes: G06V 10/141 : Control of illumination
• CPC Class Codes: G06V 10/30 : Noise filtering
• CPC Class Codes: G06V 10/42 : Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation
• CPC Class Codes: G06V 10/75 : Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
• CPC Class Codes: G06V 10/758 : Involving statistics of pixels or of feature values, e.g. histogram matching
• CPC Class Codes: G06V 10/761 : Proximity, similarity or dissimilarity measures
• CPC Class Codes: G06V 30/19073 : Comparing statistics of pixel or of feature values, e.g. histogram matching
• CPC Class Codes: G16H 20/17 : delivered via infusion or injection
• CPC Class Codes: G16H 40/40 : for the management of medical equipment or devices, e.g. scheduling maintenance or upgrades
• CPC Class Codes: G16H 40/67 : for remote operation
• CPC Class Codes: H04N 7/183 : for receiving images from a single remote source
• CPC Class Codes: Y10T 137/7287 : Liquid level responsive or maintaining systems

Abstract

A flow meter includes an image sensor, a coupler, a support member and one or more processors. The coupler is adapted to couple to a drip chamber. The support member is operatively coupled to the coupler. The image sensor has a field of view and is operatively coupled to the support member. The image sensor is positioned to view the drip chamber within the field of view. The processor receives data from the image sensor and is configured to: receive a first image from the image sensor, compare the first image to a second image, and generate a difference image based upon the comparison between the first and second images.

Claims

1. A flow meter, comprising:

a coupler adapted to couple to a drip chamber;

a support member operatively coupled to the coupler;

a background pattern disposed behind the drip chamber;

an image sensor having a field of view and operatively coupled to the support member, wherein the image sensor is positioned to view the drip chamber within the field of view; and

at least one processor in communication with the image sensor to receive data from the image sensor, wherein the at least one processor is configured to;

receive a first image from the image sensor,

compare the first image to a second image, and

generate a difference image based upon the comparison between the first and second images.

2. The flow meter according to claim 1, wherein the at least one processor is further configured to estimate a parameter of liquid using the difference image.

3. The flow meter according to claim 1, wherein the at least one processor is further configured to determine an existence of a free flow condition using the difference image.

4. The flow meter according to claim 1, wherein the at least one processor is further configured to determine a drop size using the difference image.

5. The flow meter according to claim 1, wherein the at least one processor is further configured to determine a drop growth rate using the difference image.

6. The flow meter according to claim 1, wherein the at least one processor is further configured to identifying an optical distortion of an area behind a free flow condition within the drip chamber using the difference image.

7. The flow meter according to claim 1, wherein the at least one processor is further configured to estimate an optical distortion by using the difference image.

8. The flow meter according to claim 1, wherein the second image is a background image.

9. The flow meter according to claim 8, wherein the background image is a dynamic background image.

10. The flow meter according to claim 1, wherein the second image is a previously captured image.

11. The flow meter according to claim 1, wherein the at least one processor is further configured to estimate an optical distortion by using the difference image, wherein the difference image is an absolute difference between the first image and the second image.

12. The flow meter according to claim 11, wherein the at least one processor is further configured to estimate the optical distortion by determining a squared absolute difference between the first image to the second image.

13. The flow meter according to claim 1, wherein the at least one processor is further configured to:

subtract the first image from the second image to thereby generate the difference image, and

convert each pixel of the difference image to a true value if an absolute value of a respective pixel is greater than a predetermined threshold or to a false value if the absolute value of the respective pixel is less than the predetermined threshold.

14. The flow meter according to claim 13, wherein the at least one processor is further configured to:

sum each row of the converted difference image to generate a plurality of summation values, wherein each summation value of the plurality of summation values corresponds to a respective row of the converted difference image, and

examine the plurality of summation values.

15. The flow meter according to claim 14, wherein the at least one processor is further configured to determine if a free flow condition exists within the drip chamber when examining the plurality of summation values.

16. The flow meter according to claim 15, wherein the at least one processor is further configured to determine that the free flow condition exists when the plurality of summation values includes a plurality of contiguous summation values above another predetermined threshold.

17. The flow meter according to claim 14, wherein the at least one processor is further configured to determine a drop has been formed when the at least one processor examines the plurality of summation values.

18. The flow meter according to claim 17, wherein the at least one processor is further configured to determine if the plurality of summation values includes a plurality of contiguous summation values within a predetermined range greater than a minimum value and less than a maximum value to thereby determine if the drop has been formed within the drip chamber.

19. The flow meter according to claim 14, wherein the at least one processor is configured to smooth the plurality of summation values.

20. The flow meter according to claim 19, wherein the at least one processor smoothes the plurality of summation values is in accordance with at least one of a spline function, a cubic spline function, a B-spline function, a Bezier spline function, a polynomial interpolation, a moving average, a data smoothing function, and a cubic-spline-type function.

21. The flow meter according to claim 1, wherein the at least one processor is further configured to convert each pixel of the difference image to a squared value of each pixel.

22. The flow meter according to claim 1, wherein the at least one processor is further configured to create a first thresholded image using the first image.

23. The flow meter according to claim 1, wherein the at least one processor is further configured to determine a set of pixels within a first thresholded image connected to a predetermined set of pixels within the first thresholded image.

24. The flow meter according to claim 1, wherein the at least one processor is further configured to filter all remaining pixels of a first thresholded image that are not within a set of pixels, the filter operates on a pixel-by-pixel basis within a time domain to generate a first filtered image.

25. The flow meter according to claim 1, wherein the at least one processor is further configured to remove pixels determined to not be part of a drop from a first thresholded image using a first filtered image to generate a generated second image.

26. The flow meter according to claim 1, wherein the at least one processor is further configured to determine a second set of pixels within a generated second image connected to a predetermined set of pixels within the generated second image to generate a third image, the third image identifies the second set of pixels within the generated second image.

27. The flow meter according to claim 1, wherein the at least one processor is further configured to determine a first length of a drop by counting a number of rows containing pixels corresponding to a second set of pixels within a third image, the first length corresponding to a first estimated drop size.

28. The flow meter according to claim 1, wherein the at least one processor is configured to update a generated second image using the first image.

29. The flow meter according to claim 1, wherein the at least one processor is further configured to create a second thresholded image by comparing the first image with the second image.

30. The flow meter according to claim 1, wherein the at least one processor is further configured to sum rows of a second thresholded image to create a plurality of row sums, each row sum corresponds to a row of the second thresholded image.

31. The flow meter according to claim 1, wherein the at least one processor is further configured to set a start at a row position of a second thresholded image having a first sum of the plurality of row sums that corresponds to a first length.

32. The flow meter according to claim 1, wherein the at least one processor is further configured to increment a row position until the row position corresponds to a corresponding row sum that is zero.

33. The flow meter according to claim 1, wherein the at least one processor is further configured to determine a second length is equal to a present row position, the second length corresponding to a second estimated drop size.

34. The flow meter according to claim 1, wherein the at least one processor is further configured to average a first length and a second lengths to determine an average length, the average length corresponding to a third estimated drop size.

35. The flow meter according to claim 1, wherein the at least one processor is further configured estimate a parameter of liquid by estimating an optical distortion of the background pattern.

36. The flow meter according to claim 1, wherein the at least one processor is further configured to subtract the first image from the second image to thereby generate the difference image.

37. The flow meter according to claim 1, wherein the at least one processor is further configured to convert each pixel of the difference image to a true value if an absolute value of a respective pixel is greater than a predetermined threshold or to a false value if the absolute value of the respective pixel is less than the predetermined threshold.

38. The flow meter according to claim 1, wherein the at least one processor is further configured to sum each row of a converted difference image to generate a plurality of summation values, wherein each summation value of the plurality of summation values corresponds to a respective row of the converted difference image.

39. The flow meter according to claim 38, wherein the at least one processor is further configured to examine the plurality of summation values.

40. The flow meter according to claim 1, wherein a blur function is applied to the first image.