System and method for segmentation and automated measurement of chronic wound images

Patent metadata

Field	Value
Publication number	US-9990472-B2
Application number	US-201615078313-A
Country	US
Kind code	B2
Filing date	Mar 23, 2016
Priority date	Mar 23, 2015
Publication date	Jun 5, 2018
Grant date	Jun 5, 2018

Abstract

Disclosed are systems and methods for automated monitoring of the size, area or boundary of chronic wound images. The disclosure includes use of a probability map that measures the likelihood of wound pixels belonging to granulation, slough or eschar, which can then be segmented using any standard segmentation techniques. Measurement of the wound size, area or boundary occurs automatically and without user input related to outlining, filling in, or making measurement lines over the image on a display.

First claim

What is claimed is:

1. A method for segmentation and automated measurement of chronic wound images comprising:
obtaining a digital image, wherein at least a portion of the image comprises a wound;
transforming each pixel of the digital image to a modified Hue-Saturation-Value (HSV) color space, wherein transforming each pixel of the digital image to a modified Hue-Saturation-Value (HSV) color space comprises scaling Saturation (S) and Value (V) components of a HSV color space by:

S
mod

=

log
⁡

(

α
*
S

+
1

)

log
⁡

(

α
+
1

)

⁢

⁢
and
⁢

⁢

V
mod

=

log
⁡

(

α
*
V

+
1

)

log
⁡

(

α
+
1

)

,

where S mod and V mod are modified Saturation and modified Value of the modified HSV color space, respectively, and a is a constant;
creating a probability map by determining a probability of each of the transformed pixels in the modified HSV color space belonging to each of four colors of a four-dimensional color map;
segmenting each of the transformed pixels in the modified HSV color space to one or more groups based on each transformed pixel's color as determined by the probability map, wherein each of the one or more groups correspond to only one of the four colors of the four-dimensional color map;
automatically identifying each of the one or more groups as belonging to the wound as granulation, slough or eschar tissue based on the one of the four colors of the four-dimensional color map that corresponds to each of the one or more groups;
automatically determining at least one of a boundary, size, or an area of the wound based on the one or more groups identified as belonging to the wound as granulation, slough or eschar tissue; and
providing the determined at least one of the boundary, the size or the area of the wound, wherein the provided determined at least one of the boundary, the size or the area of the wound is used to monitor wound healing.

2. The method of claim 1 , wherein the determining at least one of the boundary, the size, or the area of the wound comprises determining a pixel size for at least the one or more pixels of the digital image that are classified as belonging to the wound.

3. The method of claim 2 , wherein the pixel size is determined by placing an object of a known size in the digital image proximate to the wound.

4. The method of claim 3 , wherein the object of known size comprises a label of known size.

5. The method of claim 4 , wherein determining the pixel size comprises:
detecting the label in the digital image using image analysis algorithms;
measuring a number of pixels that span a given detected edge of known size of the label; and
determining the pixel size by dividing the known size of the given detected edge by the number of pixels that span the given detected edge of the label.

6. The method of claim 4 , wherein the label further comprises thereon descriptive information of a patient associated with the wound.

7. The method of claim 1 , wherein α=8.

8. The method of claim 7 , wherein the probability of each of the transformed pixels in the modified HSV color space belonging to each of the four colors of the four-dimensional color map is computed based on a distance of each of the transformed pixels to red, yellow, black and white colors in the modified Hue-Saturation-Value (HSV) color space.

9. The method of claim 8 , wherein pixels having the highest probability of being red are associated with granulation tissue, pixels having the highest probability of being yellow are associated with slough tissue, and pixels having the highest probability of being black are associated with eschar tissue.

10. The method of claim 9 , wherein pixels having the highest probability of being white are associated with epibole tissue, skin or an object of a known size in the digital image used to determine pixel size.

11. The method of claim 1 , wherein the one or more pixels segmented as belonging to the wound are further identified as granulation, slough or eschar tissue based on the probability of the pixel belonging to the color of a four-dimensional color map as determined by a region-growing algorithm or an optimal thresholding algorithm.

12. The method of claim 1 , further comprising determining the boundary of the wound and reporting the wound boundary as an ordered vector of pixel coordinates.

13. The method of claim 12 , wherein determining the size and area of a wound comprises
determining a maximum distance between two boundary pixel values;
reporting the maximum distance as a length of the wound;
determining a perpendicular maximum distance between two boundary pixels, wherein the perpendicular maximum distance is a maximum distance between two boundary pixels such that a straight line drawn between the two boundary pixels that form the perpendicular maximum distance would be perpendicular to a straight line drawn between the two boundary pixels that form the length of the wound;
reporting the perpendicular maximum distance as a width of the wound;
calculating pixel values of pixels that belong to the wound that are within the boundary of the wound; and
reporting the total pixel values within the wound boundary as the area of the wound.

14. The method of claim 13 , wherein the width is reported in centimeters, the width is reported in centimeters, and the area is reported in square centimeters.

Assignees

• Ohio State Innovation Foundation

Inventors

• Gurcan Metin N
• Sen Chandan K
• Gordillo Gayle

Classifications

• G16H30/20

  for handling medical images, e.g. DICOM, HL7 or PACS · CPC title

• G16H30/40

  for processing medical images, e.g. editing · CPC title

• G06F18/2433

  Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection · CPC title

• G06T7/62

  of area, perimeter, diameter or volume · CPC title

• G06T2207/20076

  Probabilistic image processing · CPC title