# US 12,602,777 B2 · Apparatus and method for quantitative assessment of medical images for diagnosis of chronic obstructive pulmonary disease

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- Number: US 12,602,777 B2
- Document ID: US12602777B2
- Status: Granted
- Kind: B2
- Application number: 18/123,853
- Filed: 2023-03-20
- Published: 2023-10-26
- Granted: 2026-04-14
- Earliest claimed: 2022-03-18
- Assignees: Coreline Soft Co., Ltd.; The Asan Foundation; University of Ulsan Foundation for Industry Cooperation
- Applicants: Coreline Soft Co,, Ltd.; The Asan Foundation; University of Ulsan Foundation for Industry Coop
- Inventors: Sang Min Lee; Joon Beom Seo; Jaeyoun Yi; Donghoon Yu; Hye Jeon Hwang; Heejun Park
- Web page: https://patentgrep.com/patent/US12602777B2

## CPC

- G06T 7/0012: {Biomedical image inspection}
- G06T 7/11: Region-based segmentation
- G06T 7/194: involving foreground-background segmentation
- G06T 7/70: Determining position or orientation of objects or cameras (camera calibration G06T7/80)
- G06T 11/26: Drawing of charts or graphs
- G16H 30/40: for processing medical images, e.g. editing
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/30: for calculating health indices; for individual health risk assessment
- G06T 7/30: Determination of transform parameters for the alignment of images, i.e. image registration
- G06T 2200/24: involving graphical user interfaces [GUIs]
- G06T 2207/10081: Computed x-ray tomography [CT]
- G06T 2207/20021: Dividing image into blocks, subimages or windows
- G06T 2207/30061: Lung
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

Disclosed are a visualization method for assisting medical image diagnosis comprising: acquiring first intensity values of first voxels in a lung region during inspiration, segmented from a chest computed tomography (CT) image acquired during inspiration, as first coordinate values of the first voxels; acquiring differences between second intensity values of second voxels, registered into the first voxels as voxels in the lung region during expiration segmented from a chest CT image acquired during expiration, and the first intensity values as second coordinate values of the first voxels; and visualizing a distribution of the first voxels by mapping the first voxels based on the first coordinate values and the second coordinate values.

## Claims

### Claim 1

A visualization method for assisting medical image diagnosis, the visualization method comprising:
acquiring first intensity values of first voxels in a lung region during inspiration, segmented from a chest computed tomography (CT) image acquired during inspiration, as first coordinate values of the first voxels;
acquiring differences between second intensity values of second voxels, registered into the first voxels as voxels in the lung region during expiration segmented from a chest CT image acquired during expiration, and the first intensity values as second coordinate values of the first voxels; and
visualizing a distribution of the first voxels by mapping the first voxels based on the first coordinate values and the second coordinate values.

### Claim 2 (depends on 1)

The visualization method of claim 1, further comprising classifying and visualizing a plurality of regions corresponding to different medical findings based on a first threshold value for the first coordinate values and a second threshold value for the second coordinate values.

### Claim 3 (depends on 2)

The visualization method of claim 2, further comprising providing quantitative analysis results of a distribution of the first voxels in each of the plurality of regions as quantitative assessment information associated with a medical finding corresponding to each of the plurality of regions.

### Claim 4 (depends on 2)

The visualization method of claim 2, further comprising further comprising segmenting the lung region during inspiration into a plurality of sub-regions;
wherein the classifying and visualizing is performed on at least one of the plurality of sub-regions.

### Claim 5 (depends on 1)

The visualization method of claim 1, further comprising classifying and visualizing a region, in which the first coordinate values are smaller than a first threshold value and the second coordinate values are smaller than the second threshold value, as a functional air trapping (fAT) region.

### Claim 6 (depends on 5)

The visualization method of claim 5, further comprising classifying and visualizing a region, in which the first coordinate values are smaller than a third threshold value, as an emphysema region.

### Claim 7 (depends on 6)

The visualization method of claim 6, further comprising visualizing a region, belonging to both the fAT region and the emphysema region, using a visual element that can distinguish this region from remaining regions.

### Claim 8 (depends on 6)

The visualization method of claim 6, further comprising classifying and visualizing a region, in which the second coordinate values are equal to or larger than the second threshold value and the first coordinate values are equal to or larger than the third threshold value, as a normal region.

### Claim 9 (depends on 5)

The visualization method of claim 5, further comprising classifying and visualizing a region, in which the second coordinate values are smaller than the second threshold value and the first coordinate values are equal to or larger than the first threshold value, as a normal region.

### Claim 10 (depends on 1)

The visualization method of claim 1, further comprising segmenting the lung region during inspiration into a plurality of sub-regions;
wherein the acquiring the first intensity values, the acquiring the differences, and the visualizing are performed on at least one of the plurality of sub-regions.

### Claim 11

A visualization apparatus for assisting medical image diagnosis, the visualization apparatus comprising:
memory configured to store at least one instruction; and
a processor configured to execute the at least one instruction;
wherein the processor executes the at least one instruction to:
acquire first intensity values of first voxels in a lung region during inspiration, segmented from a chest computed tomography (CT) image acquired during inspiration, as first coordinate values of the first voxels;
acquire differences between second intensity values of second voxels, registered into the first voxels as voxels in the lung region during expiration segmented from a chest CT image acquired during expiration, and the first intensity values as second coordinate values of the first voxels; and
visualize a distribution of the first voxels by mapping the first voxels based on the first coordinate values and the second coordinate values.

### Claim 12 (depends on 11)

The visualization apparatus of claim 11, wherein the processor executes the at least one instruction to classify and visualize a plurality of regions corresponding to different medical findings based on a first threshold value for the first coordinate values and a second threshold value for the second coordinate values.

### Claim 13 (depends on 12)

The visualization apparatus of claim 12, wherein the processor executes the at least one instruction to provide quantitative analysis results of a distribution of the first voxels in each of the plurality of regions as quantitative assessment information associated with a medical finding corresponding to each of the plurality of regions.

### Claim 14 (depends on 12)

The visualization apparatus of claim 12, wherein the processor executes the at least one instruction to:
segment the lung region during inspiration into a plurality of sub-regions; and
classify and visualize a plurality of regions corresponding to different medical findings for at least one of the plurality of sub-regions.

### Claim 15 (depends on 11)

The visualization apparatus of claim 11, wherein the processor executes the at least one instruction to classify and visualize a region, in which the first coordinate values are smaller than a first threshold value and the second coordinate values are smaller than a second threshold value, as a functional air trapping (fAT) region.

### Claim 16 (depends on 15)

The visualization apparatus of claim 15, wherein the processor executes the at least one instruction to classify and visualize a region, in which the first coordinate values are smaller than a third threshold value, as an emphysema region.

### Claim 17 (depends on 16)

The visualization apparatus of claim 16, wherein the processor executes the at least one instruction to visualize a region, belonging to both the fAT region and the emphysema region, using a visual element that can distinguish this region from remaining regions.

### Claim 18 (depends on 16)

The visualization apparatus of claim 16, wherein the processor executes the at least one instruction to classify and visualize a region, in which the second coordinate values are equal to or larger than the second threshold value and the first coordinate values are equal to or larger than the third threshold value, as a normal region.

### Claim 19 (depends on 15)

The visualization apparatus of claim 15, wherein the processor executes the at least one instruction to classify and visualize a region, in which the second coordinate values are smaller than the second threshold value and the first coordinate values are equal to or larger than the first threshold value, as a normal region.

### Claim 20 (depends on 11)

The visualization apparatus of claim 11, wherein the processor executes the at least one instruction to:
segment the lung region during inspiration into a plurality of sub-regions;
acquire the first intensity values as first coordinate values of the first voxels for at least one of the plurality of sub-regions;
acquire differences between second intensity values of second voxels and the first intensity values as second coordinate values of the first voxels for at least one of the plurality of sub-regions; and
visualize a distribution of the first voxels for at least one of the plurality of sub-regions.