# US 11,304,604 B2 · Reflective mode multi-spectral time-resolved optical imaging methods and apparatuses for tissue classification

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- Number: US 11,304,604 B2
- Document ID: US11304604B2
- Status: Granted
- Kind: B2
- Application number: 15/523,287
- Filed: 2015-10-28
- Published: 2017-12-28
- Granted: 2022-04-19
- Earliest claimed: 2014-10-29
- Assignees: Spectral Md, Inc.
- Applicants: Spectral MD, Inc.
- Inventors: John Michael DiMaio; Wensheng Fan; Jeffrey E. Thatcher; Weizhi Li; Weirong Mo
- Web page: https://patentgrep.com/patent/US11304604B2

## CPC

- A61B 5/0064: {Body surface scanning}
- A61B 5/0077: {Devices for viewing the surface of the body, e.g. camera, magnifying lens}
- A61B 5/445: {Evaluating skin irritation or skin trauma, e.g. rash, eczema, wound, bed sore}
- A61B 5/7271: {Specific aspects of physiological measurement analysis (specific diagnostics methods using bioelectric or biomagnetic signals A61B5/316)}
- Y02A 90/10: Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- Y02A: TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE

## Abstract

Certain aspects relate to apparatuses and techniques for non-invasive optical imaging that acquires a plurality of images corresponding to both different times and different frequencies. Additionally, alternatives described herein are used with a variety of tissue classification applications, including assessing the presence and severity of tissue conditions, such as burns and other wounds.

## Claims

### Claim 1

A method of assessing burn severity comprising:
positioning a subject approximate to a light source and an image acquisition device;
illuminating a tissue region of the subject using the light source;
acquiring a plurality of images of the tissue region using the image acquisition device, a first subset of the plurality of images captured at different times and a second subset of the plurality of images captured at different frequency bands;
classifying a burn status of areas of the tissue region based at least in part on photoplethysmography (PPG) data derived from the first subset of the plurality of images acquired with the image acquisition device and on multispectral data derived from the second subset of the plurality of images;
generating a classified image based at least in part on the plurality of images and the burn status of the areas of the tissue region; and
segmenting individual pixels of the classified image among a plurality of tissue categories based at least in part on the burn status of the areas of the tissue region corresponding to the individual pixels.

### Claim 2 (depends on 1)

The method of claim 1, further comprising illuminating the tissue region with approximately uniform intensity from the light source.

### Claim 3 (depends on 1)

The method of claim 1, further comprising displaying the classified image of the tissue region, the classified image comprising a first representation of pixels classified as the excised skin and a different second representation of pixels classified as the burn.

### Claim 4 (depends on 1)

The method of claim 1, further comprising calculating an estimate of a degree of burn of at least one area of the tissue region.

### Claim 5 (depends on 1)

The method of claim 1, further comprising:
identifying a portion of the tissue region corresponding to healthy tissue and a portion of the tissue region corresponding to necrotic tissue; and
determining a margin for debridement at an interface between the healthy tissue and the necrotic tissue based on the plurality of images.

### Claim 6 (depends on 5)

The method of claim 5, further comprising debriding the tissue region within the margin for debridement.

### Claim 7 (depends on 1)

The method of claim 1, wherein the tissue region comprises approximately a total body surface area of the subject, the method further comprising calculating an estimate of a percentage of total burned body surface area of the subject based at least in part on the classifying the burn status.

### Claim 8 (depends on 7)

The method of claim 7, wherein the plurality of images depict a plurality of regions of the total body surface area of the subject, the method further comprising calculating the estimate of the percentage of the total burned body surface area of the subject based at least in part on classifying the burn status of the plurality of regions of the total body surface area of the subject.

### Claim 9 (depends on 8)

The method of claim 8, further comprising stitching together portions of the plurality of images depicting the plurality of regions of the subject based at least in part on performing a cross-correlation of the plurality of regions.

### Claim 10 (depends on 9)

The method of claim 9, wherein the stitching further comprises detecting an edge of a body section of the subject.

### Claim 11 (depends on 7)

The method of claim 7, wherein calculating the estimate of the percentage of total burned body surface area of the subject further comprises:
classifying areas corresponding to one or more of a first degree of burns, a second degree of burns, and a third degree of burns across the total body surface area of the subject; and
calculating the estimate of the percentage of the total burned body surface area of the subject at least in part by adding the areas corresponding to the first degree of burns, second degree of burns, and third degree of dividing the added areas by the total body surface area of the subject.

### Claim 12 (depends on 1)

The method of claim 1, wherein the classified image comprises pixels modified or processed to have different visual representations based on the burn status corresponding to the pixels.

### Claim 13 (depends on 1)

The method of claim 1, wherein the segmenting comprises using graph theory, thresholding, k-means clustering, hierarchical clustering, fuzzy clustering, watershed algorithms, edge detection, region growing, statistical grouping, shape recognition, morphological image processing, computer training, or histogram-based methods.

### Claim 14

An apparatus for assessing burn severity of a subject comprising:
one or more light sources configured to illuminate a tissue region;
one or more image acquisition devices configured to receive light reflected from the tissue region; and
a processor configured to:
control the one or more light sources and the one or more image acquisition devices to acquire a plurality of images the tissue region, a first subset of the plurality of images captured at different times and a second subset of the plurality of images captured at different frequency bands;
classify a burn status of areas of the tissue region based on photoplethysmography (PPG) data derived from the first subset of the plurality of images and on multispectral data derived from the second subset of the plurality of images;
generate a classified image based at least in part on the plurality of images and the burn status of the areas of the tissue region; and
segment individual pixels of the classified image among a plurality of tissue categories based at least in part on the burn status of the areas of the tissue region corresponding to the individual pixels.

### Claim 15 (depends on 14)

The apparatus of claim 14, wherein the one or more light sources are configured to illuminate the tissue region with approximately uniform intensity.

### Claim 16 (depends on 14)

The apparatus of claim 14, further comprising a display, wherein the processor is configured to output the classified image to the display, the classified image comprising a first representation of pixels classified as the excised skin and a different second representation of pixels classified as the burn.

### Claim 17 (depends on 14)

The apparatus of claim 14, wherein the processor is configured to:
identify a portion of the tissue region corresponding to healthy tissue and a portion of the tissue region corresponding to necrotic tissue; and
determine a margin for debridement at an interface between the healthy tissue and the necrotic tissue based on the plurality of images.

### Claim 18 (depends on 14)

The apparatus of claim 14, wherein the tissue region comprises approximately a total body surface area of the subject, and wherein the processor is configured to calculate an estimate of a percentage of total burned body surface area of the subject based at least in part on the classified burn status.

### Claim 19 (depends on 18)

The apparatus of claim 18, wherein the plurality of images depict a plurality of regions of the total body surface area of the subject, and wherein the processor is configured to calculate the estimate of the percentage of the total burned body surface area of the subject based at least in part on classification of the burn status of the plurality of regions of the subject.

### Claim 20 (depends on 19)

The apparatus of claim 19, wherein the processor is configured to stitch together portions of the plurality of images depicting the plurality of regions of the subject based at least in part on performing a cross-correlation of the plurality of regions.

### Claim 21 (depends on 20)

The apparatus of claim 20, wherein, to stitch together the portions of the plurality of images, the processor is further configured to detect an edge of a body section of the subject.

### Claim 22 (depends on 18)

The apparatus of claim 18, wherein the processor is configured to:
classify areas corresponding to one or more of a first degree of burns, a second degree of burns, and a third degree of burns across the whole surface of the subject; and
estimate the percentage of the total burned body surface area of the subject based at least in part on adding the areas corresponding to the first degree of burns, second degree of burns, and third degree of and dividing the added areas by the total body surface area of the subject.

### Claim 23 (depends on 14)

The apparatus of claim 14, wherein the processor modifies or processes pixels of the classified image to have different visual representations based on the burn status corresponding to the pixels.

### Claim 24 (depends on 14)

The apparatus of claim 14, wherein the processor is configured to segment individual pixels of the classified image among a plurality of tissue categories based at least in part on the burn status corresponding to the pixel using graph theory, thresholding, k-means clustering, hierarchical clustering, fuzzy clustering, watershed algorithms, edge detection, region growing, statistical grouping, shape recognition, morphological image processing, computer training, or histogram-based methods.