Flow metering using a difference image for liquid parameter estimation

Patent metadata

Field	Value
Publication number	US-9772044-B2
Application number	US-201514939015-A
Country	US
Kind code	B2
Filing date	Nov 12, 2015
Priority date	Dec 21, 2011
Publication date	Sep 26, 2017
Grant date	Sep 26, 2017

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.

First claim

What is claimed is:

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 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 wherein the second image contains a pattern having a plurality of lines, 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 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 during when the at least 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 from the first 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, and
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.

23. The flow meter according to claim 22 , wherein the at least one processor is configured to update the second image using the first image.

24. The flow meter according to claim 23 , wherein the at least one process is configured to:
create a second thresholded image by comparing the first image with the second 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 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.

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

Assignees

• Deka Products Lp

Inventors

• Peret Bob D
• Kane Derek G
• Kamen Dean
• Murphy Colin H
• Kerwin John M

Classifications

• G06T7/20

  Analysis of motion (motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51) · CPC title

• A61M5/1411 Primary

  Drip chambers (A61M5/162, A61M5/1689, A61M5/40 take precedence) · CPC title

• G06V10/761

  Proximity, similarity or dissimilarity measures · CPC title

• G06V10/75

  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 title

• G06F18/22

  Matching criteria, e.g. proximity measures · CPC title