# US 2017/0256038 A1 · Image Generating Method and Apparatus, and Image Analyzing Method

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- Number: US 2017/0256038 A1
- Document ID: US20170256038A1
- Status: Published application
- Kind: A1
- Application number: 14/787,728
- Filed: 2015-09-24
- Published: 2017-09-07
- Earliest claimed: 2015-09-24
- Assignees: Vuno Korea, Inc.
- Applicants: Vuno Korea, Inc.
- Inventors: Yeha LEE; Hyun-Jun KIM; Kyuhwan JUNG; Sangki KIM
- Web page: https://patentgrep.com/patent/US20170256038A1

## CPC

- G06V 10/82: using neural networks
- G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06F 18/2414: {Smoothing the distance, e.g. radial basis function networks [RBFN]}
- G06T 5/77: Retouching; Inpainting; Scratch removal
- G06T 7/0012: {Biomedical image inspection}
- G06V 10/764: using classification, e.g. of video objects
- G06V 10/774: Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
- G06T 2207/20081: Training; Learning
- G06T 2207/20084: Artificial neural networks [ANN]
- G06V 2201/03: Recognition of patterns in medical or anatomical images
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL

## Abstract

An image generating method and apparatus, and an image analyzing method are disclosed. The image generating method includes receiving a reference image, and generating a training image from the reference image by adding noise to at least one parameter of a window width and a window level of pixel values of the reference image.

## Claims

### Claim 1

An image generating method, comprising:
receiving a reference image; and
generating a training image from the reference image by adding noise to at least one parameter of a window width and a window level of pixel values of the reference image.

### Claim 2 (depends on 1)

The method of claim 1, wherein, in the presence of a remaining parameter between the window width and the window level to which the noise is not added, the generating of the training image comprises:
generating the training image from the reference image based on the parameter to which the noise is added and the remaining parameter to which the noise is not added.

### Claim 3 (depends on 1)

The method of claim 1, wherein the window width and the window level comprises a preset value for an object to be analyzed by a neural network to be trained based on the training image.

### Claim 4 (depends on 1)

The method of claim 1, wherein the window width indicates a range of pixel values to be comprised in the training image among the pixel values of the reference image.

### Claim 5 (depends on 1)

The method of claim 1, wherein the window level indicates a center of a range of the pixel values to be comprised in the training image.

### Claim 6 (depends on 1)

The method of claim 1, wherein the reference image is a medical image obtained by capturing an object to be analyzed by a neural network to be trained based on the training image.

### Claim 7 (depends on 1)

The method of claim 1, wherein the generating of the training image comprises:
changing a value of the at least one parameter of the window width and the window level to allow the window width and the window level to deviate from a preset value for an object to be analyzed by a neural network to be trained based on the training image.

### Claim 8 (depends on 1)

The method of claim 1, further comprising:
adding noise to a pixel value of the training image.

### Claim 9 (depends on 8)

The method of claim 8, wherein the noise to be added to the pixel value of the training image is generated based on at least one of a characteristic of a device capturing the reference image and an object comprised in the reference image.

### Claim 10

An image analyzing method, comprising:
receiving an input image; and
analyzing the input image based on a neural network, and
wherein the neural network is trained based on a training image extracted from a reference image, and
wherein the training image is generated from the reference image by adding noise to at least one parameter of a window width and a window level of pixel values of the reference image.

### Claim 11

An image generating apparatus, comprising:
a memory in which an image generating method is stored; and
a processor configured to execute the image generating method, and
wherein the processor is configured to generate a training image from a reference image by adding noise to at least one parameter of a window width and a window level of pixel values of the reference image.

### Claim 12 (depends on 11)

The apparatus of claim 11, wherein, in the presence of a remaining parameter between the window width and the window level to which the noise is not added, the processor is configured to generate the training image from the reference image based on the parameter to which the noise is added and the remaining parameter to which the noise is not added.

### Claim 13 (depends on 11)

The apparatus of claim 11, wherein the window width and the window level comprise a preset value for an object to be analyzed by a neural network to be trained based on the training image.

### Claim 14 (depends on 11)

The apparatus of claim 11, wherein the window width indicates a range of pixel values to be comprised in the training image among the pixel values of the reference image.

### Claim 15 (depends on 11)

The apparatus of claim 11, wherein the window level indicates a center of ti. 4 range of the pixel values to be comprised in the training image.

### Claim 16 (depends on 11)

The apparatus of claim 11, wherein the reference image is a medical image obtained by capturing an object to be analyzed by a neural network to be trained based on the training image.

### Claim 17 (depends on 11)

The apparatus of claim 11, wherein the processor is configured to change a value of the at least one parameter of the window width and the window level to allow the window width and the window level to deviate from a preset value for an object to be analyzed by a neural network to be trained based on the training image.

### Claim 18 (depends on 11)

The apparatus of claim 11, wherein the processor is configured to add noise to a pixel value of the training image.

### Claim 19 (depends on 18)

The apparatus of claim 18, wherein the noise to be added to the pixel value of the training image is generated based on at least one of a characteristic of a device capturing the reference image and an object comprised in the reference image.