Back to results
US 2026/0215751 A1Published application
Image processing method, training method, trained model, radiological image processing module, radiological image processing program, and radiological image processing system
AssigneeHamamatsu Photonics K.K.
InventorsSatoshi TSUCHIYA, Tatsuya ONISHI
Life of this patent
Estimating the term…
Abstract
An image processing method includes acquiring an image obtained by irradiating a subject with an energy beam and capturing an image of the energy beam transmitted through the subject, generating a spatial blur map indicating a distribution of spatial blur of noise based on the image, and inputting the image and the spatial blur map into a trained model constructed in advance through machine learning and executing image processing for removing noise from the image.
Claims23
1:
- An image processing method comprising:
- acquiring an image obtained by irradiating a subject with an energy beam and capturing an image of the energy beam transmitted through the subject;
- generating a spatial blur map indicating a distribution of spatial blur of noise based on the image;
- and
- inputting the image and the spatial blur map into a trained model constructed in advance through machine learning and executing image processing for removing noise from the image.
2 more elements
2:
- The image processing method according to claim 1, further comprising deriving a noise evaluation value obtained by evaluating a spread of a noise value from a pixel value of each pixel in the image based on first relationship data indicating a relationship between the pixel value and the noise evaluation value, and generating a noise map that is data in which the derived noise evaluation value is associated with each pixel in the image,
- wherein the image, the spatial blur map, and the noise map are input into the trained model and executing image processing for removing noise from the image.
3:
- The image processing method according to claim 1, wherein the spatial blur map is generated for each of a plurality of first partial images obtained by cutting out the image, and
- the plurality of first partial images is input into the trained model instead of the image to execute image processing for removing noise from the plurality of first partial images, and the plurality of first partial images from which noise has been removed are integrated to generate the image from which noise has been removed.
4:
- The image processing method according to claim 1, wherein blur evaluation information obtained by evaluating spatial blur of noise is derived from a pixel value of each pixel in the image based on second relationship data indicating a relationship between the pixel value and the blur evaluation information, and data in which the derived blur evaluation information is associated with each pixel in the image is generated as the spatial blur map.
5:
- A training method comprising:
- cutting out a plurality of second partial images from an entire image as training image, setting blur evaluation information obtained by evaluating spatial blur of noise as blur setting information for each of the second partial images, generating a spatial blur map indicating a distribution of spatial blur of noise for the plurality of second partial images based on the blur setting information, and generating a noise image in which noise is applied to the second partial images based on the generated spatial blur map;
- and
- using the second partial image, the spatial blur map, and the noise image as training data to construct a trained model through machine learning, the trained model taking the spatial blur map and the noise image as inputs, and outputting a noise-removed image in which noise has been removed from the noise image so that the noise-removed image approximates the second partial image.
1 more element
6:
- The training method according to claim 5, wherein the blur setting information is set as the blur evaluation information that differs for each of the second partial images.
7:
- The training method according to claim 5, wherein the blur evaluation information is set as the blur setting information for each pixel in the second partial image, the blur evaluation information derived from a pixel value of each pixel in the second partial image based on second relationship data indicating a relationship between the pixel value and the blur evaluation information obtained by evaluating spatial blur of noise, and data in which the blur setting information is set in association with each pixel in the second partial image is generated as the spatial blur map.
8:
- The training method according to claim 7, wherein the second relationship data is generated through a simulation.
9:
- The training method according to claim 7, wherein the second relationship data is generated based on an image obtained by actual image capturing.
10:
- The training method according to claim 5, wherein the blur evaluation information is at least one of a sigma value, a half width, a point spread function, a contrast transfer function, and a modulation transfer function.
11:
- A trained model constructed using the training method according to claim 5, comprising causing a processor to execute image processing for removing noise from an image obtained by capturing an image of an energy beam transmitted through a subject.
12:
- A radiological image processing module comprising a processor wherein the processor is configured to:
- acquire a radiological image obtained by irradiating a subject with radiation and capturing an image of the radiation transmitted through the subject;
- generate a spatial blur map indicating a distribution of spatial blur of noise based on the radiological image;
- and
- input the radiological image and the spatial blur map into a trained model constructed in advance through machine learning and execute image processing for removing noise from the radiological image.
2 more elements
13:
- The radiological image processing module according to claim 12, wherein the processor is configured to
- derive a noise evaluation value obtained by evaluating a spread of a noise value from a pixel value of each pixel in the radiological image based on first relationship data indicating a relationship between the pixel value and the noise evaluation value, and generate a noise map that is data in which the derived noise evaluation value is associated with each pixel in the radiological image, and
- input the radiological image, the spatial blur map, and the noise map into the trained model and execute image processing for removing noise from the radiological image.
14:
- The radiological image processing module according to claim 12, wherein the processor is configured to
- generate the spatial blur map for each of a plurality of first partial images obtained by cutting out the radiological image, and
- input the plurality of first partial images into the trained model instead of the radiological image to execute image processing for removing noise from the plurality of first partial images, and integrate the plurality of first partial images from which noise has been removed to generate the radiological image from which noise has been removed.
15:
- The radiological image processing module according to claim 12, wherein the processor is configured to derive blur evaluation information obtained by evaluating spatial blur of noise from a pixel value of each pixel in the radiological image based on second relationship data indicating a relationship between the pixel value and the blur evaluation information, and generate data in which the derived blur evaluation information is associated with each pixel in the radiological image as the spatial blur map.
16:
- The radiological image processing module according to claim 12, wherein the processor is configured to
- cut out a plurality of second partial images from an entire image as training image, set blur evaluation information obtained by evaluating spatial blur of noise as blur setting information for each of the second partial images, generate a spatial blur map indicating a distribution of spatial blur of noise for the plurality of second partial images based on the blur setting information, and generate a noise image in which noise is applied to the second partial images based on the generated spatial blur map;
- and
- use the second partial image, the spatial blur map, and the noise image as training data to construct a trained model through machine learning, the trained model taking the spatial blur map and the noise image as inputs, and outputting a noise-removed image in which noise has been removed from the noise image so that the noise-removed image approximates the second partial image.
1 more element
17:
- The radiological image processing module according to claim 16, wherein the processor is configured to set the blur setting information as the blur evaluation information that differs for each of the second partial images.
18:
- The radiological image processing module according to claim 16, wherein the processor is configured to set the blur evaluation information as the blur setting information for each pixel in the second partial image, the blur evaluation information derived from a pixel value of each pixel in the second partial image based on second relationship data indicating a relationship between the pixel value and the blur evaluation information obtained by evaluating spatial blur of noise, and generate data in which the blur setting information is set in association with each pixel in the second partial image as the spatial blur map.
19:
- The radiological image processing module according to claim 18, wherein the processor is configured to generate the second relationship data through a simulation.
20:
- The radiological image processing module according to claim 18, wherein the processor is configured to generate the second relationship data based on a radiological image obtained by actual image capturing.
21:
- The radiological image processing module according to claim 16, wherein the blur evaluation information is at least one of a sigma value, a half width, a point spread function, a contrast transfer function, and a modulation transfer function.
22:
- A radiological image processing program causing a processor to function as:
- an image acquisition unit configured to acquire a radiological image obtained by irradiating a subject with radiation and capturing an image of the radiation transmitted through the subject;
- a spatial blur map generation unit configured to generate a spatial blur map indicating a distribution of spatial blur of noise based on the radiological image;
- and
- a processing unit configured to input the radiological image and the spatial blur map into a trained model constructed in advance through machine learning and execute image processing for removing noise from the radiological image.
2 more elements
23:
- A radiological image processing system comprising:
- the radiological image processing module according to claim 12;
- a source configured to irradiate the subject with radiation;
- and
- an imaging device configured to capture an image of the radiation transmitted through the subject to acquire the radiological image.
2 more elements