Flow meter

• US 10,113,660 B2
• Filed: 04/02/2018
• Issued: 10/30/2018
• 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
• Application Number:
  US15/943,238
• Publication Number:
  US 20180224012A1
• Time in Patent Office: 211 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 a background pattern disposed behind a drip chamber, an image sensor, and a processor. The image sensor has a field of view and is configured to view the drip chamber within the field of view. The processor is coupled to the image sensor to receive image data therefrom and captures, using the image sensor, an image of the drip chamber and at least a portion of the background pattern, examines the image, and adjusts a flow rate of fluid flowing through a fluid line in accordance with the examination of the image.

Claims

1. A method for adjusting a flow rate of a fluid line, the method comprising:

capturing an image of a drip chamber using an image sensor;

subtracting the image from a background image to thereby generate a difference image;

converting each pixel of the difference image to a first value where an absolute value of a respective pixel is beyond a predetermined threshold or to a second value where the absolute value of the respective pixel is less than the predetermined threshold to generate a converted difference image;

summing 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;

examining a plurality of contiguous summation values within the plurality of summation values; and

adjusting a flow rate of fluid flowing through the fluid line in accordance with the examination of the plurality of contiguous summation values.

2. The method according to claim 1, wherein the background image is generated using another image captured by the image sensor.

3. The method according to claim 1, further comprising the act of estimating a volume of a drop using the image of the drip chamber and the background image.

4. The method according to claim 1, further comprising the act of identifying a plurality of pixels of interest within the image.

5. The method according to claim 4, wherein the plurality of pixels of interest are identified by comparing the image to the background image.

6. The method according to claim 4, further comprising the act of determining a subset of pixels within the plurality of pixels of interest, wherein each pixel of the plurality of pixels of interest is determined to be within the subset of pixels when there is a path to a baseline corresponding to the drip chamber.

7. The method according to claim 4, further comprising the act of determining a volume of a drop based upon the plurality of pixels of interest.

8. The method according to claim 1, further comprising the act of initializing the background image.

9. The method according to claim 1, further comprising the act of updating the background image using the image captured by the image sensor.

10. The method according to claim 9, wherein the background image is updated in accordance with:

P_{background,i,j}=P_{background,i,j}(1−

α

_background)+α

_background P_input,i,j.

11. The method as in claim 1, further comprising the act of updating an array of variances using the image captured by the image sensor.

12. The method according to claim 11, wherein the array of variances is updated in accordance with:

σ

_temp²=(P_{background,i,j}−

P_input,i,j)²

σ

_{background,i,j}²=σ

_{background,i,j}²(1−

β

_background)+β

_backgroundσ

_temp².

13. The method according to claim 1, further comprising the act of updating an array of integers in accordance with the image captured by the image sensor.

14. The method according to claim 13, wherein each integer of the array of integers corresponds to a number of updates of a pixel of the background image.

15. The method according to claim 14, wherein a comparison of the image to the background image only compares pixels within the image to pixels within the background image where a respective integer of the array of integers indicates a respective pixel within the background image has been updated at least a predetermined number of times.

16. The method according to claim 1, further comprising the acts of:

identifying a drop in the image and a predetermined band near an edge of the drop; and

initializing the background image by setting each pixel of the background image to the image unless it is within the identified drop or the predetermined band near the edge of the drop.

17. The method according to claim 16, further comprising the act of setting a pixel of the background image to a predetermined value where a corresponding pixel of the image is within the identified drop or the predetermined band near the edge of the drop.

18. The method according to claim 17, wherein the corresponding pixel of the image has a location corresponding to a location of the pixel of the background image.

19. The method according to claim 1, further comprising the act of determining a baseline corresponding to an opening of the drip chamber.

20. The method according to claim 1, wherein an act of determining a subset of pixels within a plurality of pixels of interest that corresponds to a drop includes determining each of the plurality of pixels of interest is within the subset of pixels where the respective pixel of the plurality of pixels of interest has a contiguous path back to a baseline of the drop forming at an opening of the drip chamber.

21. The method according to claim 1, further comprising the acts of:

identifying a drop within the image and a predetermined band near an edge of the drop;

initializing the background image by setting each pixel to the image unless it is within the identified drop or the predetermined band near the edge of the drop;

setting pixels within the region of the drop or within the predetermined band to a predetermined value;

initializing an array of integers; and

initializing an array of variances.

22. The method according to claim 21, further comprising the act of updating the background image, the array of integers, and the array of variances using the image.

23. The method according to claim 1, further comprising the act of adjusting a valve to control the flow rate.

24. The method according to claim 1, wherein the background image is configured to filter out low frequency artifacts.

25. The method according to claim 1, wherein the act of examining the plurality of contiguous summation values includes determining a free flow condition exists by determining the plurality of contiguous summation values is above another predetermined threshold.

26. The method according to claim 1, wherein act of examining the plurality of contiguous summation values includes determining a drop has been formed within the drip chamber.

27. The method according to claim 26, wherein the act of determining the drop has been formed within the drip chamber includes determining the plurality of contiguous summation values is within a predetermined range greater than a minimum value and less than a maximum value.

28. The method according to claim 1, further comprising the act of converting each pixel of the difference image to a squared value of each pixel after the subtracting act and prior to the converting act.

29. The method according to claim 1, further comprising the acts of:

creating a first thresholded image from the image by comparing each pixel of the image to a threshold value;

determining a set of pixels within the first thresholded image connected to a predetermined set of pixels within the first thresholded image;

filtering all remaining pixels of the first thresholded image that are not within the set of pixels, the filtering operates on a pixel-by-pixel basis within the time domain to generate a first filtered image;

removing pixels determined to not be part of a drop from the first thresholded image using the first filtered image to generate a second image;

determining a second set of pixels within the second image connected to a predetermined set of pixels within the second image to generate a third image, the third image identifies the second set of pixels within the second image;

determining a first length of the drop by counting the number of rows containing pixels corresponding to the second set of pixels within the third image, the first length corresponding to a first estimated drop size;

updating the background image using the image;

creating a second thresholded image by comparing the image with the background image;

summing the rows of the second thresholded image to create a plurality of row sums, each row sum corresponds to a row of the second thresholded image;

starting at a row position of the second thresholded image having a first sum of the plurality of row sums that corresponds to the first length;

incrementing the row position until the row position corresponds to a corresponding row sum that is zero;

determining a second length is equal to the present row position, the second length corresponding to a second estimated drop size; and

averaging the first and second lengths to determine an average length, the average length corresponding to a third estimated drop size.

30. The method according to claim 1, further comprising the act of updating each pixel of the background image using a low-pass filter.

31. The method according to claim 1, further comprising the act of remotely controlling the flow rate by a monitoring client.

32. The method according to claim 31, wherein the monitoring client is a tablet.

33. A flow meter, comprising:

a coupler adapted to couple to a drip chamber;

a support member operatively coupled to the coupler;

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 operatively coupled to the image sensor to receive image data therefrom, wherein the at least one processor is configured to;

capture an image of the drip chamber using the image sensor;

subtract the image from a background image to thereby generate a difference image;

convert each pixel of the difference image to a first value where an absolute value of a respective pixel is beyond a predetermined threshold or to a second value where the absolute value of the respective pixel is less than the predetermined threshold to generate a converted difference image;

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;

examine a plurality of contiguous summation values within the plurality of summation values; and

adjust a flow rate of fluid flowing through the fluid line in accordance with the examination of the plurality of contiguous summation values.

34. The flow meter according to claim 33, wherein the background image is generated using another image captured by the image sensor.

35. The flow meter according to claim 33, wherein the at least one processor is further configured to estimate a volume of a drop using a comparison between the image of the drip chamber and the background image.

36. The flow meter according to claim 33, wherein the at least one processor is further configured to identify a plurality of pixels of interest within the image.

37. The flow meter according to claim 36, wherein the plurality of pixels of interest are identified by comparing the image to the background image.

38. The flow meter according to claim 36, wherein the at least one processor is further configured to determine a subset of pixels within the plurality of pixels of interest, wherein each pixel of the plurality of pixels of interest is determined to be within the subset of pixels when there is a path to a baseline corresponding to the drip chamber.

39. The flow meter according to claim 36, wherein the at least one processor is further configured to determine a volume of a drop based upon the plurality of pixels of interest.

40. The flow meter according to claim 33, wherein the at least one processor is further configured to initialize the background image.

41. The flow meter according to claim 33, wherein the at least one processor is further configured to update the background image using the image captured by the image sensor.

42. The flow meter according to claim 41, wherein the background image is updated in accordance with:

P_{background,i,j}=P_{background,i,j}(1−

α

_background)+α

_background P_input,i,j.

43. The flow meter according to claim 33, wherein the at least one processor is further configured to update an array of variances using the image captured by the image sensor.

44. The flow meter according to claim 43, wherein the array of variances is updated in accordance with:

σ

_temp²=(P_{background,i,j}−

P_input,i,j)²

σ

_{background,i,j}²=σ

_{background,i,j}²(1−

β

_background)+β

_backgroundσ

_temp².

45. The flow meter according to claim 33, wherein the at least one processor is further configured to update an array of integers in accordance with the image captured by the image sensor.

46. The flow meter according to claim 45, wherein each integer of the array of integers corresponds to a number of updates of a pixel of the background image.

47. The flow meter according to claim 46, wherein a comparison of the image to the background image only compares pixels within the image to pixels within the background image where a respective integer of the array of integers indicates a respective pixel within the background image has been updated at least a predetermined number of times.

48. The flow meter according to claim 33, wherein the at least one processor is further configured to:

identify a drop in the image and a predetermined band near an edge of the drop; and

initialize the background image by setting each pixel of the background image to the image unless it is within the identified drop or the predetermined band near the edge of the drop.

49. The flow meter according to claim 48, wherein the at least one processor is further configured to set a pixel of the background image to a predetermined value where a corresponding pixel of the image is within the identified drop or the predetermined band near the edge of the drop.

50. The flow meter according to claim 49, wherein the corresponding pixel of the image has a location corresponding to a location of the pixel of the background image.

51. The flow meter according to claim 33, wherein the at least one processor is further configured to determine a baseline corresponding to an opening of the drip chamber.

52. The flow meter according to claim 33, wherein a determination of a subset of pixels within a plurality of pixels of interest that corresponds to a drop includes determining each of the plurality of pixels of interest is within the subset of pixels where the respective pixel of the plurality of pixels of interest has a contiguous path back to a baseline of the drop forming at an opening of the drip chamber.

53. The flow meter according to claim 33, wherein the at least one processor is further configured to:

identify a drop within the image and a predetermined band near an edge of the drop;

initialize the background image by setting each pixel to the image unless it is within the identified drop or the predetermined band near the edge of the drop;

set pixels within the region of the drop or within the predetermined band to a predetermined value;

initialize an array of integers; and

initialize an array of variances.

54. The flow meter according to claim 53, wherein the at least one processor is further configured to update the background image, the array of integers, and the array of variances using the image.

55. The flow meter according to claim 33, wherein the background image is configured to filter out low frequency artifacts.

56. The flow meter according to claim 33, wherein the at least one processor is further configured to determine a free flow condition exists within the drip chamber.

57. The flow meter according to claim 33, wherein the at least one processor is further configured to determine a free flow condition exists by determining the plurality of contiguous summation values is above another predetermined threshold.

58. The flow meter according to claim 33 wherein the examination of the plurality of contiguous summation values includes determining a drop has been formed within the drip chamber.

59. The flow meter according to claim 58, wherein the determination of whether the drop has been formed within the drip chamber includes determining the plurality of contiguous summation values is within a predetermined range greater than a minimum value and less than a maximum value.

60. The flow meter according to claim 33, wherein the at least one processor is further configured to convert each pixel of the difference image to a squared value of each pixel after the subtraction and prior to the conversion.

61. The flow meter according to claim 33, wherein the at least one processor is further configured to:

create a first thresholded image from the image by comparing each pixel of the image to a threshold value;

determine a set of pixels within the first thresholded image connected to a predetermined set of pixels within the first thresholded image;

filter all remaining pixels of the first thresholded image that are not within the set of pixels, the filter operates on a pixel-by-pixel basis within the time domain to generate a first filtered image;

remove pixels determined to not be part of a drop from the first thresholded image using the first filtered image to generate a second image;

determine a second set of pixels within the second image connected to a predetermined set of pixels within the second image to generate a third image, the third image identifies the second set of pixels within the second image;

determine a first length of the drop by counting the number of rows containing pixels corresponding to the second set of pixels within the third image, the first length corresponding to a first estimated drop size;

update the background image using the image;

create a second thresholded image by comparing the image with the background image;

sum the rows of the second thresholded image to create a plurality of row sums, each row sum corresponds to a row of the second thresholded image;

start at a row position of the second thresholded image having a first sum of the plurality of row sums that corresponds to the first length;

increment the row position until the row position corresponds to a corresponding row sum that is zero;

determine a second length is equal to the present row position, the second length corresponding to a second estimated drop size; and

average the first and second lengths to determine an average length, the average length corresponding to a third estimated drop size.

62. The flow meter according to claim 33, wherein the at least one processor is further configured to update each pixel of the background image using a low-pass filter.

63. The flow meter according to claim 33, wherein the at least one processor is further configured to bed remotely controlled by a monitoring client.

64. The flow meter according to claim 63, wherein the monitoring client is a tablet.

65. The flow meter according to claim 33, wherein the at least one processor is configured to apply a blurring function to the image.