Flow meter

• US 9,724,465 B2
• Filed: 09/19/2014
• Issued: 08/08/2017
• 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:
  US14/491,128
• Publication Number:
  US 20150002667A1
• Time in Patent Office: 1,054 Days
• CPC Class Codes: A61M 1/3627 Degassing devices; Buffer reservoirs; Drip chambers; Blood filters
• 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 2205/50 with microprocessors or computers
• CPC Class Codes: A61M 39/284 Lever clamps
• 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: G01D 5/342 the sensed object being the obturating part
• CPC Class Codes: G01F 22/00 Methods or apparatus for measuring volume of fluids or fluent solid material, not otherwise provided for
• CPC Class Codes: G06T 2207/10004 Still image; Photographic image
• CPC Class Codes: G06T 2207/20061 Hough transform
• CPC Class Codes: G06T 2207/20076 Probabilistic image processing
• 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 5/50 using two or more images, e.g. averaging or subtraction
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• CPC Class Codes: G06T 7/248 involving reference images or patches
• CPC Class Codes: G06T 7/254 involving subtraction of images

Abstract

An flow meter includes a coupler, a support member, an image sensor, 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 its field of view. The one or more processors are operatively coupled to the image sensor to receive data therefrom. The one or more processors (1) receive image data from the image sensor, and (2) determine an existence of a free flow condition by identifying an optical distortion of an area behind the free flow condition within the drip chamber using the received image data.

Claims

1. 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 including an image of the drip chamber using the image sensor having the field of view including the drip chamber;

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

examine the difference image to determine whether a free flow condition exists.

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

calculate a plurality of summation values.

3. The flow meter according to claim 1, wherein the at least one processor is configured to examine the difference image to determine whether the free flow condition exists by:

converting each pixel of the difference image to a true value if an absolute value of a respective pixel is beyond a predetermined threshold or to a false value if the absolute value of the respective pixel is less than the predetermined threshold; and

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.

4. The flow meter according to claim 3, wherein the at least one processor is further configured to determine if the free flow condition exists within the drip chamber by examining the plurality of summation values.

5. The flow meter according to claim 4, wherein the at least one processor is further configured to determine if the plurality of summation values includes a plurality of contiguous summation values above another predetermined threshold when the at least one processor determines if the free flow condition exists.

6. The flow meter according to claim 4, wherein the at least one processor is further configured to determine if a drop has been formed within the drip chamber when the at least one processor examines the plurality of summation values.

7. The flow meter according to claim 6, wherein the at least one processor is further configured to determine that the drop has been formed 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 and a location of the plurality of contiguous summation values corresponds to a predetermined range of locations in which the drop can form.

8. The flow meter according to claim 4, the at least one processor is further configured to smooth the plurality of summation values prior to when the at least one processor examines the plurality of summation values.

9. The flow meter according to claim 8, wherein the at least one processor smoothes 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.

10. The flow meter according to claim 1, wherein the at least one processor is further configured to initialize the dynamic background image.

11. The flow meter according to claim 1, wherein the at least one processor is further configured to update the dynamic background image using the image captured by the image sensor.

12. The flow meter according to claim 11, wherein the dynamic background image is updated in accordance with:

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

α

_background)+α

_backgroundP_input,i,j.

13. The flow meter as in claim 11, wherein the at least one processor is further configured to update an array of variances using the image captured by the image sensor.

14. The flow meter according to claim 13, 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}=σ

_{background,i,j}²(1−

β

_background)+β

_backgroundσ

_temp².

15. The flow meter according to claim 11, wherein the at least one processor is further configured to update an array of integers in according with the image captured by the image sensor.

16. The flow meter according to claim 15, wherein each integer of the array of integers corresponds to a number of updates of a pixel of the dynamic background image.

17. The flow meter according to claim 16, wherein a comparison of the image to the dynamic background image only compares pixels within the image to pixels within the dynamic background image if a respective integer of the array of integers indicates a respective pixel within the dynamic background image has been updated at least a predetermined number of times.

18. A processor-implemented method implemented by an operative set of processor executable instruction configured for execution on at least one processor, the method comprising:

capturing an image of a drip chamber and a background pattern disposed behind the drip chamber, wherein the image is captured using an image sensor;

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

examining the difference image to determine whether a free flow condition exists based upon a distortion of the background pattern as indicated by the captured image.

19. The method according to claim 18, further comprising initializing the dynamic background image.

20. The method according to claim 18, further comprising updating the dynamic background image using the image captured by the image sensor.

21. The method according to claim 20, wherein the dynamic background image is updated in accordance with:

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

α

_background)+α

_backgroundP_input,i,j.

22. The method according to claim 20, further comprising updating an array of variances using the image captured by the image sensor.

23. The method according to claim 22, 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}=σ

_{background,i,j}²(1−

β

_background)+β

_backgroundσ

_temp².

24. The method according to claim 20, further comprising updating an array of integers in accordance with the image captured by the image sensor.

25. The method according to claim 24, wherein each integer of the array of integers corresponds to a number of updates of a pixel of the dynamic background image.

26. The method according to 25, wherein a comparison of the image to the dynamic background image only compares pixels within the image to pixels within the dynamic background image if a respective integer of the array of integers indicates a respective pixel within the dynamic background image has been updated at least a predetermined number of times.

27. The method according to claim 18, further comprising

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

and

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

28. The method according to claim 27, further comprising setting a pixel of the dynamic background image to a predetermined value if a corresponding pixel of the image is within the identified drop or the predetermined band near the edge of the drop.

29. The method according to claim 28, wherein the corresponding pixel of the image has a location corresponding to a location of the pixel of the dynamic background image.

30. The method according to claim 29, further comprising determining a baseline corresponding to an opening of the drip chamber.

31. The method according to claim 30, wherein an act of determining a subset of pixels within a plurality of pixels of interest that corresponds to the drop includes determining each of the plurality of pixels of interest is within the subset of pixels if the respective pixel of the plurality of pixels of interest has a contiguous path back to the baseline of the drop forming at an opening of the drip chamber.

32. The method according to claim 18, further comprising:

capturing a first image using the image sensor;

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

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

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

initializing an array of integers; and

initializing an array of variances.

33. The method according to claim 32, further comprising updating the dynamic background image, the array of integers, and the array of variances using the image.