Method for processing image, and image processing apparatus according thereto

Abstract

An image processing apparatus and an image processing method are provided. The image processing apparatus may obtain a target style image generated by a first imaging apparatus, input the target style image generated by the first imaging apparatus to an artificial intelligence model corresponding to a second imaging apparatus, obtain an image adjustment parameter value of the second imaging apparatus from the artificial intelligence model, perform, on the basis of the image adjustment parameter value, image adjustment for a pre-processed image generated by the second imaging apparatus, so as to display an adjustment image obtained by adjusting the pre-processed image to an image adjustment style of the target style image, and store, on the basis of receiving a user input for storing an obtained image adjustment parameter set value, the obtained image adjustment parameter set value as an image adjustment parameter set value corresponding to the second imaging apparatus.

Description

Method for processing images and image processing device according to the same

[0001] The present disclosure relates to an image processing apparatus and a method for processing an image. The present disclosure relates to an image processing apparatus that processes a preprocessed image according to the style of an input image, an image processing method, and a computer-readable recording medium that stores a computer program for performing the image processing method.

[0002] Image processing may refer to information processing that transforms an input image to output a transformed image. Image processing may include Euclidean geometric transformations such as image enlargement, reduction, and rotation. Additionally, image processing may include color correction such as brightness and contrast. Furthermore, image processing may include digital synthesis, image registration, and image segmentation.

[0003] Even for the same type of video device, the desired image processing style may differ depending on the user. Accordingly, most video devices provide parameter adjustment functions to adjust the image style.

[0004] Meanwhile, when users acquire a new video device, they may want to adjust the parameters of the new device to match the image processing style set on the existing video device. Additionally, if users like an image generated by another video device, they may want to adjust the parameters of their own video device to match the style of that image.

[0005] One aspect of the present disclosure may provide an image processing device that performs image adjustment on a preprocessed image according to the style of an input image. The image processing device may include at least one processor comprising a display, a communication module, an input interface, a memory for storing instructions, and a processing circuit. When instructions are executed individually or collectively by at least one processor, the image processing device may perform the following operations. The image processing device may acquire a target style image generated by a first image device through a communication module. The image processing device may input the target style image generated by the first image device into an artificial intelligence model corresponding to a second image device and acquire image adjustment parameter values ​​of the second image device from the artificial intelligence model. Based on the image adjustment parameter values, the image processing device may perform image adjustment on a preprocessed image generated by the second image device, thereby displaying an adjusted image through a display in which the preprocessed image is adjusted to the image adjustment style of the target style image. Based on receiving a user input that stores an acquired image adjustment parameter set value through an input interface, the image processing device can store the acquired image adjustment parameter set value as an image adjustment parameter set value corresponding to a second image device.

[0006] One aspect of the present disclosure may provide an image processing method that performs image adjustment on a preprocessed image according to the style of an input image. The image processing method may include the step of acquiring a target style image generated by a first image device. The image processing method may include the step of inputting the target style image generated by the first image device into an artificial intelligence model corresponding to a second image device and acquiring image adjustment parameter values ​​of the second image device from the artificial intelligence model. Based on the image adjustment parameter values, the image processing method may perform image adjustment on a preprocessed image generated by the second image device, thereby displaying an adjusted image in which the preprocessed image is adjusted to the image adjustment style of the target style image. Based on receiving a user input that stores the acquired image adjustment parameter set values, the image processing method may include the step of storing the acquired image adjustment parameter set values ​​as image adjustment parameter set values ​​corresponding to the second image device.

[0007] One aspect of the present disclosure may provide a computer-readable recording medium on which a program for performing an image processing method on a computer is recorded.

[0008] FIG. 1 illustrates a method for an image processing device to obtain image adjustment parameters corresponding to the style of an input image, according to one embodiment of the present disclosure.

[0009] FIG. 2 illustrates a method in which an image processing device converts a preprocessed image into an image style of a target style image according to one embodiment of the present disclosure.

[0010] FIG. 3 illustrates a block diagram of an image processing device according to one embodiment of the present disclosure.

[0011] FIG. 4 is a flowchart of a method for an image processing device to set image adjustment parameters corresponding to the style of a target style image according to one embodiment of the present disclosure.

[0012] FIG. 5 illustrates a user interface provided by an image processing device to acquire a target style image according to one embodiment of the present disclosure.

[0013] FIG. 6 illustrates a method in which an image processing device receives user input for selecting a target style image, according to one embodiment of the present disclosure.

[0014] FIG. 7 illustrates a method in which an image processing device receives user input selecting a region of interest as a target style image, according to one embodiment of the present disclosure.

[0015] FIG. 8 illustrates a flowchart of a method for receiving user input that changes an image adjustment parameter value obtained by an image processing device based on a target style image, according to one embodiment of the present disclosure.

[0016] FIG. 9 illustrates a method for an image processing device to change an image adjustment parameter set value obtained from a target style image, according to one embodiment of the present disclosure.

[0017] FIG. 10 illustrates a flowchart of a method for an image processing device to display a plurality of image adjustment parameter candidate values ​​according to one embodiment of the present disclosure.

[0018] FIG. 11 illustrates a method for an image processing device to set image adjustment parameter set values ​​based on a plurality of adjustment images, according to one embodiment of the present disclosure.

[0019] FIG. 12 illustrates a block diagram of an image processing device according to one embodiment of the present disclosure.

[0020] In the present disclosure, the expression “at least one of a, b, or c” may refer to “a”, “b”, “c”, “a and b”, “a and c”, “b and c”, “a, b, and c all”, or variations thereof.

[0021] Embodiments of the present disclosure are described below in detail with reference to the attached drawings so that those skilled in the art can easily implement them. However, the present disclosure may be embodied in various different forms and is not limited to the embodiments described herein. Furthermore, in order to clearly explain the present disclosure in the drawings, parts unrelated to the explanation have been omitted, and similar parts throughout the specification are denoted by similar reference numerals.

[0022] The terms used in this disclosure are described in their current, general form considering the functions mentioned herein; however, they may refer to various other terms depending on the intent of those skilled in the art, case law, the emergence of new technologies, etc. Accordingly, the terms used in this disclosure should not be interpreted solely by their names, but should be interpreted based on the meaning of the terms and the overall content of this disclosure.

[0023] Additionally, terms such as "first," "second," etc., may be used to describe various components, but the components should not be limited by these terms. These terms are used for the purpose of distinguishing one component from another.

[0024] Furthermore, the terms used in this disclosure are used merely to describe specific embodiments and are not intended to limit this disclosure. Singular expressions include a plural meaning unless the context clearly indicates a singular meaning. Additionally, throughout the specification, when a part is described as being "connected" to another part, this includes not only cases where they are "directly connected," but also cases where they are "electrically connected" with other elements interposed between them. Furthermore, when a part is described as "including" a component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0025] Phrases such as "in some embodiments" or "in one embodiment" appearing in various places in this specification do not necessarily refer to the same embodiment.

[0026] One embodiment of the present disclosure is intended to provide an image processing device and a method for controlling the image processing device, which acquire parameters of an image device corresponding to the style of an input external image.

[0027] One embodiment of the present disclosure is intended to provide an image processing device that modifies an image according to the style of an input external image and a method for controlling the image processing device.

[0028] FIG. 1 illustrates a method in which an image processing device (1000) obtains image adjustment parameters corresponding to the style of an input image according to one embodiment of the present disclosure.

[0029] Referring to FIG. 1, an image processing device (1000) can receive an image (10a or 10b) from a first image device. The image processing device (1000) inputs the received image (10a or 10b) into an artificial intelligence model (50) and can obtain image adjustment parameters of a second image device corresponding to the style of the image (10a or 10b) from the artificial intelligence model (50).

[0030] Image adjustment parameters may include, but are not limited to, contrast, brightness, sharpness, enhancement, transparency, or noise reduction.

[0031] The first imaging device and the second imaging device may be devices that detect light, electromagnetic waves, or energy as image data and convert the detected image data into an image. For example, the first imaging device and the second imaging device may be a color camera, an infrared camera, an X-ray imaging device, a CT (Computed Tomography) device, an ultrasound device, or an MRI (Magnetic Resonance Imaging) device, but are not limited thereto. An image generated from the detected image data may be referred to as a preprocessed image.

[0032] Additionally, the first imaging device can change the style of the preprocessed image based on preset image adjustment parameters. The style of the image may be referred to as a post-processing style or an image adjustment style. The second imaging device can also change the style of the preprocessed image generated by the second imaging device based on preset image adjustment parameters in the second imaging device.

[0033] The first imaging device may be an imaging device of the same or similar type as the second imaging device. For example, both the first imaging device and the second imaging device may be general color cameras. Also, for example, both the first imaging device and the second imaging device may be X-ray imaging devices.

[0034] The second image device may be an image processing device (1000). In this case, the image processing device (1000) detects light, electromagnetic waves, or energy as image data, generates a preprocessed image based on the detected image data, and can change the style of the generated preprocessed image to the style of the image (10a or 10b) of the first image device based on image adjustment parameters obtained from the artificial intelligence model (50).

[0035] Additionally, the second imaging device may be a separate device from the image processing device (1000). For example, the first imaging device and the second imaging device may be X-ray imaging devices, and the image processing device (1000) may be a workstation, PC (Personal Computer), or laptop computer connected to the second imaging device. In this case, the image processing device (1000) may change the style of the image obtained from the second imaging device to the style of the image (10a or 10b) of the first imaging device based on the image adjustment parameters of the second imaging device obtained from the artificial intelligence model (50).

[0036] The image processing device (1000) may include an artificial intelligence model (50). The artificial intelligence model (50) may be trained so that when an external image is input to the artificial intelligence model (50), an image adjustment parameter value that converts an image generated by a second image device into the style of the external image is output. The image adjustment parameter value may mean a single image adjustment parameter set value that includes a plurality of image adjustment parameters.

[0037] Accordingly, when the input data of the training data is a single external image, the target data of the training data may be an image adjustment parameter value of the second image device that adjusts the preprocessed image of the second image device to the style of the external image.

[0038] The image adjustment parameter value of the second image device, which adjusts the preprocessed image of the second image device to the style of the external image, can be determined experimentally. For example, while a user is adjusting the image adjustment parameter value, the image processing device (1000) may receive a user input to set the image adjustment parameter value when the style of the preprocessed image of the second image device is similar to the style of the external image (e.g., brightness, sharpness, emphasis, transparency, or degree of noise reduction) as the image adjustment parameter value of the second image device corresponding to the style of the external image. Various external images and the image adjustment parameter values ​​of the second image device corresponding to each of the various external images can be used as training data to train an artificial intelligence model (50).

[0039] The image adjustment style of the first image device and the image adjustment style of the second image device may be different. For example, the value of the brightness parameter set in the first image device and the value of the brightness parameter set in the second image device may be different. In addition, for example, the first image device may provide a transparency parameter for adjusting the transparency of the preprocessed image, but the second image device may not provide a transparency parameter.

[0040] By using an artificial intelligence model (50), the image processing device (1000) converts the style of the image generated in the second image device into the style of the target image, so that even if the user acquires a new image device, the user can apply the previously used image adjustment style to the new image device using only the image of the existing image device. Additionally, if the user likes the style of the image generated in another image device, the user can apply the style of the image to their own image device using only the image of the other image device.

[0041] FIG. 2 illustrates a method in which an image processing device (1000) converts a preprocessed image (20) into an image style of a target style image according to one embodiment of the present disclosure.

[0042] Referring to FIG. 2, the image processing device (1000) can convert the preprocessed image (20) of the second image device (200) into the image style of the target style image (10a or 10b) generated by the first image device (100).

[0043] The first imaging device (100) and the second imaging device (200) may be X-ray imaging devices. The first imaging device (100) may convert an image signal detected by a detector into image data and generate a preprocessed image based on the converted image data. Additionally, the first imaging device (100) may generate an X-ray image (10a or 10b) by performing image adjustment on the preprocessed image based on image adjustment parameter values ​​set in the first imaging device (100).

[0044] A user (e.g., a radiologist) may wish to use a new second imaging device (200) instead of the existing first imaging device (100). The image processing device (1000) may acquire images of the first imaging device (100) as target-style images (10a, 10b) from the storage device of the first imaging device (100) according to user input. The storage device may include, but is not limited to, an external hard drive of the first imaging device (100) or a PACS (Picture Archiving and Communication System).

[0045] The image processing device (1000) may acquire an artificial intelligence model (50) corresponding to the second image device (200). According to an embodiment, the image processing device (1000) may acquire an artificial intelligence model (50) corresponding to the second image device (200) and the shooting area (e.g., chest, abdomen, arm, leg). In this case, the artificial intelligence model (50) may be trained such that when a target style image (10a or 10b) of one area is input, an image adjustment parameter value is output that transforms the preprocessed image (20) of the same area into the image style of the target style image (10a or 10b).

[0046] The image processing device (1000) inputs a plurality of target style images (10a, 10b) of the first image device (100) into an acquired artificial intelligence model (50), and can obtain a plurality of image adjustment parameter values ​​of the second image device (200) corresponding to the plurality of target style images (10a, 10b) from the artificial intelligence model (50).

[0047] The image processing device (1000) can determine one of the acquired multiple image adjustment parameter values ​​as the image adjustment parameter value of the second image device (200). For example, the image processing device (1000) can determine the image adjustment parameter value with the highest usage frequency among the multiple image adjustment parameter values ​​as the image adjustment parameter value of the second image device (200). Additionally, for example, the image processing device (1000) can determine the image adjustment parameter value corresponding to the most recently generated target style image (10a or 10b) as the image adjustment parameter value of the second image device (200).

[0048] According to one embodiment of the present disclosure, an image processing device (1000) can perform image adjustment on a preprocessed image (20) generated by a second image device (200) based on a determined image adjustment parameter value of the second image device (200). By performing image adjustment, the image processing device (1000) can generate an adjusted image (30a or 30b) in which the preprocessed image (20) generated by the second image device (200) is adjusted to the image style of a target style image (10a or 10b).

[0049] According to one embodiment of the present disclosure, an image processing device (1000) may receive a user input that displays an adjusted image (30a or 30b), checks the displayed adjusted image (30a or 30b), and stores an image adjustment parameter value corresponding to the adjusted image (30a or 30b). Based on the user input, the image processing device (1000) may store the image adjustment parameter value as an image adjustment parameter value corresponding to a second image device (200).

[0050] According to one embodiment of the present disclosure, an image processing device (1000) may display an adjusted image (30a or 30b) and an image adjustment parameter value corresponding to the adjusted image (30a or 30b). Additionally, the image processing device (1000) may display a user interface for changing the image adjustment parameter value along with the adjusted image (30a or 30b) and the image adjustment parameter value. Additionally, the image processing device (1000) may receive user input for changing the image adjustment parameter value through the user interface.

[0051] According to one embodiment of the present disclosure, an image processing device (1000) may receive a user input selecting one of a plurality of target style images (10a, 10b). The image processing device (1000) may obtain an image adjustment parameter value corresponding to the selected target style image (10a or 10b) as an image adjustment parameter value of a second image device (200).

[0052] According to one embodiment of the present disclosure, an image processing device (1000) may display a plurality of adjustment images (30a, 30b) corresponding to a plurality of target style images (10a, 10b). Based on receiving a user input to select one of the plurality of adjustment images (30a, 30b), the image processing device (1000) may determine an image adjustment parameter value corresponding to the selected adjustment image (30a or 30b) as an image adjustment parameter value of a second image device (200).

[0053] According to one embodiment of the present disclosure, an image processing device (1000) receives a user input to set a region of interest in a target style image (10a or 10b) and can obtain an image adjustment parameter value corresponding to the style of the set region of interest among the entire area of ​​the target style image (10a or 10b).

[0054] The second image device (200) may be an image processing device (1000). In this case, the image processing device (1000) may generate a preprocessed image based on image data detected by the detector of the image processing device (1000), obtain an artificial intelligence model (50) corresponding to the image processing device (1000), and change the style of the preprocessed image to the image style of the target style image (10a or 10b) based on image adjustment parameter values ​​obtained from the artificial intelligence model (50).

[0055] The second imaging device (200) may be a separate device from the image processing device (1000). For example, the first imaging device (100) and the second imaging device (200) may be X-ray devices, and the image processing device (1000) may be a workstation, PC, or laptop connected to the second imaging device (200). Additionally, the image processing device (1000) may change the image style of the preprocessed image (20) generated in the second imaging device (200) to the image style of the first imaging device (100) based on the image adjustment parameter values ​​of the second imaging device (200) obtained from the artificial intelligence model (50).

[0056] The image processing device (1000) can receive a preprocessed image (20) from the second image device (200). Additionally, the preprocessed image (20) may be generated in advance and stored in advance in the image processing device (1000) as a test image. Additionally, the image processing device (1000) may store in advance a plurality of preprocessed images (20) corresponding to a plurality of shooting areas. Accordingly, even if the preprocessed image (20) is not received from the second image device (200), or even if the preprocessed image (20) is not generated to set image adjustment parameters, the image processing device (1000) may obtain image adjustment parameters of the second image device (200) based on the preprocessed image (20) of the second image device stored in advance.

[0057] FIG. 3 illustrates a block diagram of an image processing device (1000) according to one embodiment of the present disclosure.

[0058] Referring to FIG. 3, the image processing device (1000) may include a processor (1100), a communication module (1300), a memory (1400), an input interface (1500), and a display (1610).

[0059] A processor (1100) can typically control the overall operation of an image processing device (1000). The processor (1100) may include processing circuitry. The processor (1100) can control the image processing device (1000) by executing programs stored in memory (1400). There may be multiple processors (1100), and the image processing device (1000) can be controlled by the instructions stored in memory (1400) being executed individually or collectively by multiple processors (1100). For example, one processor (1100) may perform some of the functions and another processor (1100) may perform other parts of the functions. Also, for example, a single processor (1100) may perform all functions.

[0060] The memory (1400) stores various information, data, instructions, programs, etc., necessary for the operation of the image processing device (1000). The memory (1400) may include at least one of volatile memory or non-volatile memory, or a combination thereof.

[0061] The memory (1400) may include an artificial intelligence model.

[0062] An artificial intelligence model can be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values ​​and performs neural network operations through calculations between the results of the previous layer and the multiple weights.

[0063] According to one embodiment of the present disclosure, the processor (1100) may include a separate Neural Processing Unit (NPU) that performs the operation of a machine learning model. Additionally, the processor (1100) may include a central processing unit (CPU), a graphics processing unit (GPU; Graphic Processing Unit), etc.

[0064] According to one embodiment of the present disclosure, the processor (1100) may include a hardware structure specialized for processing an artificial intelligence model (e.g., a neural network processing unit). The artificial intelligence model may be generated through machine learning. Such learning may be performed, for example, on the image processing unit (1000) itself where the artificial intelligence model is performed, or through a server.

[0065] Learning algorithms may include, for example, algorithms for supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the examples described above. In addition to hardware structures, artificial intelligence models may include software structures, either additionally or substantially.

[0066] For example, learning algorithms may include, but are not limited to, support vector machines, hidden Markov models, regression, neural networks, and Naive Bayes classification.

[0067] The memory (1400) can store a preprocessed image of the second imaging device. Additionally, the memory (1400) can store a plurality of preprocessed images representing different shooting areas. Additionally, the memory (1400) can store image adjustment parameter values. The memory (1400) can store image adjustment parameter values ​​corresponding to the shooting area. In this case, the image adjustment parameter values ​​may include image adjustment parameter set values ​​that include different types of parameters.

[0068] The communication module (1300) can transmit and receive information and image data to and from an external device (e.g., a medical imaging device) or a server according to a protocol under the control of the processor (1100). The communication module (1300) may include at least one communication module and at least one port for transmitting and receiving data to and from an external device or server.

[0069] Additionally, the communication module (1300) can communicate with an external device through at least one wired or wireless communication network. The communication module (1300) may include at least one short-range communication module or a long-range communication module, or a combination thereof.

[0070] The communication module (1300) can receive target style images of the first imaging device from a server (e.g., PACS). The communication module (1300) can transmit an adjusted image to the server based on image adjustment parameter values.

[0071] The display (1610) can output an image through a display panel (not shown) under the control of the processor (1100). For example, the display (1610) can display a preprocessed image. The display (1610) can display an image adjusted based on image adjustment parameter values. The display (1610) can display image adjustment parameter values.

[0072] The input interface (1500) can receive user input for controlling the image processing device (1000). The input interface (1500) may include a key (not shown), a touchscreen (not shown), etc. The input interface (1500) receives user input and transmits it to the processor (1100).

[0073] The input interface (1500) may include, but is not limited to, a user input device including a touch panel that detects a user's touch, a button that receives a user's push operation, a wheel that receives a user's rotation operation, a keyboard, and a dome switch.

[0074] At least one processor (1100) can receive user input for acquiring an image of the first imaging device through an input interface (1500). Upon receiving the user input, at least one processor (1100) can acquire a target style image generated by the first imaging device through a communication module (1300).

[0075] At least one processor (1100) can input a target style image generated from a first image device into an artificial intelligence model corresponding to a second image device and obtain an image adjustment parameter set value of the second image device from the artificial intelligence model.

[0076] At least one processor (1100) can perform image adjustment on a preprocessed image generated by a second image device based on an image adjustment parameter set value. At least one processor (1100) can display the adjusted image through a display (1610).

[0077] At least one processor (1100) can receive user input through an input interface (1500) to store an acquired image adjustment parameter set value. Based on the user input, at least one processor (1100) can store the acquired image adjustment parameter set value as an image adjustment parameter set value corresponding to a second image device.

[0078] FIG. 4 is a flowchart of a method in which an image processing device (1000) sets image adjustment parameters corresponding to the style of a target style image according to one embodiment of the present disclosure.

[0079] In step S410, the image processing device (1000) can acquire a target style image generated by the first image device.

[0080] The image processing device (1000) can receive one or more images generated by the first image device from a storage device or server of the first image device. The first image device may be a single image device, and may include a plurality of image devices depending on the embodiment.

[0081] The second image device may be an image processing device (1000), and may be a separate device from the image processing device (1000).

[0082] The image processing device (1000) may receive user input for setting image adjustment parameter values ​​of the second image device. The image processing device (1000) may receive user input for selecting a storage device or server of the first image device as a condition for the target style image. The image processing device (1000) may receive user input for selecting at least one of the shooting area, the modality of the image device that took the target style image, or the shooting date of the target style image as a condition for the target style image. Additionally, the conditions for the target style image may further include a radiologist who took the image or a physician who diagnosed the image, but are not limited thereto.

[0083] The image processing device (1000) can receive a target style image generated by the first image device from a storage device or server of the first image device. For example, the image processing device (1000) can acquire the most recent image among the images of the first image device as the target style image. Additionally, the image processing device (1000) can receive a plurality of images of the first image device and determine at least one of the received plurality of images as the target style image. For example, the image processing device (1000) can display a plurality of images generated by the first image device and receive user input to select a target style image among the displayed plurality of images.

[0084] In step S420, the image processing device (1000) inputs a target style image generated from the first image device into an artificial intelligence model corresponding to the second image device, and can obtain image adjustment parameter values ​​of the second image device from the artificial intelligence model.

[0085] Image adjustment parameters may include, but are not limited to, contrast, brightness, sharpness, intensity, transparency, or noise reduction.

[0086] The image processing device (1000) can acquire an artificial intelligence model corresponding to the second image device. The artificial intelligence model corresponding to the second image device may be a model trained such that when an external image is input as an input to the artificial intelligence model, a set of image adjustment parameters that converts the preprocessed image generated by the second image device into an image adjustment style of the external image is output.

[0087] According to one embodiment of the present disclosure, an artificial intelligence model can be trained in correspondence with a second imaging device and a shooting area.

[0088] According to one embodiment of the present disclosure, an image processing device (1000) inputs a target style image generated from a first image device into an artificial intelligence model corresponding to a second image device, and can obtain image adjustment parameter values ​​of the second image device from the artificial intelligence model.

[0089] According to one embodiment of the present disclosure, an image processing device (1000) may receive user input for setting a region of interest on a target style image. The image processing device (1000) may input image data of the set region of interest into an artificial intelligence model and obtain the output of the artificial intelligence model as an image adjustment parameter value of a second image device.

[0090] According to one embodiment of the present disclosure, an image processing device (1000) may input a plurality of images generated by a first image device into an artificial intelligence model. The image processing device (1000) may obtain a plurality of image adjustment parameter values ​​from the artificial intelligence model. The image processing device (1000) may determine one of the plurality of image adjustment parameter values ​​as an image adjustment parameter value of a second image device. For example, the image processing device (1000) may determine the image adjustment parameter value with the highest usage frequency among the plurality of image adjustment parameter values ​​as an image adjustment parameter value of the second image device.

[0091] According to one embodiment of the present disclosure, an image processing device (1000) may input a plurality of images generated by a first image device into an artificial intelligence model. The image processing device (1000) may obtain a plurality of image adjustment parameter values ​​from the artificial intelligence model. The image processing device (1000) may determine the average of the plurality of image adjustment parameter values ​​as the image adjustment parameter value of a second image device. For example, when obtaining a plurality of parameter set values ​​from an artificial intelligence model corresponding to a plurality of images generated by a first image device, the image processing device (1000) may determine the average of the first parameters within the plurality of parameter sets as the value of the first parameter of the second image device, corresponding to a first parameter included in the parameter set.

[0092] In step S430, the image processing device (1000) can display an adjusted image in which the preprocessed image is adjusted to the style of the target style image by performing image adjustment on the preprocessed image generated by the second image device based on the image adjustment parameter value.

[0093] The image processing device (1000) can generate an adjusted image in which the preprocessed image is adjusted to the style of the target style image by performing image adjustment on the preprocessed image generated by the second image device based on the image adjustment parameter values. The image processing device (1000) can display the adjusted image.

[0094] According to one embodiment of the present disclosure, an image processing device (1000) may display an image adjustment and an image adjustment parameter value, along with a user interface for changing the image adjustment parameter value. The image processing device (1000) may receive user input for changing the image adjustment parameter value through the user interface.

[0095] In step S440, the image processing device (1000) can store the acquired image adjustment parameter value as an image adjustment parameter value corresponding to the second image device based on receiving a user input to store the acquired image adjustment parameter value.

[0096] According to one embodiment of the present disclosure, the image processing device (1000) can store an image adjustment parameter value as an image adjustment parameter value of a second image device corresponding to a shooting area.

[0097] According to one embodiment of the present disclosure, an image processing device (1000) may input a plurality of images generated by a first image device into an artificial intelligence model. The image processing device (1000) may obtain a plurality of image adjustment parameter values ​​from the artificial intelligence model. The image processing device (1000) may determine a plurality of image adjustment parameter candidate values ​​among the plurality of image adjustment parameter values. The image processing device (1000) may generate a plurality of adjusted images by performing image adjustment on a preprocessed image based on the plurality of image adjustment parameter candidate values. Based on receiving a user input selecting one of the plurality of adjusted images, the image processing device (1000) may determine an image adjustment parameter value corresponding to the selected adjusted image as an image adjustment parameter value of a second image device.

[0098] FIG. 5 illustrates a user interface provided by an image processing device (1000) to acquire a target style image according to one embodiment of the present disclosure.

[0099] Referring to FIG. 5, the image processing device (1000) can acquire a target style image from a storage device based on user input.

[0100] The image processing device (1000) may provide a menu for setting image adjustment parameters. Additionally, the image processing device (1000) may provide a sub-menu for importing the style of an external image.

[0101] Based on receiving user input selecting a submenu for retrieving the style of an external image, the image processing device (1000) can display a user interface for acquiring a target style image from a storage device.

[0102] The user interface may include an item (510) for directly selecting a storage location where an image of the first image device is stored. Based on receiving user input selecting a browsing button (515), the image processing device (1000) may display a list of storage drives or storage folders. The image processing device (1000) may receive user input selecting a storage drive or storage folder among the list of storage drives or storage folders where a target style image to be acquired is stored.

[0103] The user interface may include a server interface (520) for receiving images from the first video device from the server, and receiving images that meet the conditions.

[0104] The image processing device (1000) may be connected to a plurality of servers that store images. The server interface (520) may include a server selection interface (521) for selecting a server to retrieve target style images from among the plurality of servers.

[0105] Additionally, the server interface (520) may include an interface (523) for selecting a modality of an imaging device among the conditions of a target style image. The X-ray imaging device may include, but is not limited to, a digital X-ray imaging device, a computed radiography imaging device, a radio fluoroscopy imaging device, etc. Accordingly, the image processing device (1000) can acquire only the image generated by the imaging device modality selected by the user as a target style image.

[0106] The server interface (520) may include an interface (525) for selecting a captured area. Additionally, the server interface (520) may include an interface (526) for selecting the shooting direction and the posture of the object.

[0107] The server interface (520) may include an interface (527) for selecting a date taken. The interface (527) for selecting a date taken may include an interface (529) for selecting a recent period.

[0108] Based on receiving user input that selects a condition and presses a confirmation button (530), the image processing device (1000) can acquire an image from an input storage location and store the acquired image as an image of the first image device. Additionally, the image processing device (1000) can request an image matching the input condition from a designated server and receive the requested image from the server. The image processing device (1000) can store the image received from the server as an image of the first image device.

[0109] Although not shown in FIG. 5, the user interface may include an interface for selecting a user (e.g., a radiologist) or a doctor as a condition for the target style image.

[0110] FIG. 6 illustrates a method in which an image processing device (1000) receives a user input selecting a target style image according to one embodiment of the present disclosure.

[0111] Referring to FIG. 6, the image processing device (1000) can receive user input for selecting a target style image among a plurality of images of the first image device.

[0112] The image processing device (1000) can display one of the plurality of images (620) of the first image device. Additionally, the image processing device (1000) can display an interface (631, 633) for displaying a previous image or a next image. For example, the image processing device (1000) can display the images of the first image device in order of the most recent shooting date.

[0113] The image processing device (1000) can display information (650) about the displayed image. The information (650) about the displayed image may include, but is not limited to, the date of shooting, the radiologist who took the image, the doctor who diagnosed the image, the modality of the imaging device that generated the image, and the protocol used when taking the image.

[0114] Based on receiving user input selecting the displayed image (620) as the target style image through the selection button (640), the image processing device (1000) can determine the displayed image (620) as the target style image.

[0115] FIG. 7 illustrates a method in which an image processing device (1000) receives user input selecting a region of interest as a target style image, according to one embodiment of the present disclosure.

[0116] Referring to FIG. 7, the image processing device (1000) displays an image of the first image device and can receive user input to set a region of interest within the displayed image.

[0117] All data within a single preprocessed image is adjusted based on the same image adjustment parameters, but the applied effects may vary depending on the values ​​of the data within the preprocessed image. The user can select an area within the displayed image where the desired effect is displayed as a region of interest.

[0118] The image processing device (1000) may display a region setting interface (715) for setting a region of interest within a displayed image (620). The image processing device (1000) may receive user input to select the region setting interface (715) and set a region of interest (670) within the displayed image (620).

[0119] Upon receiving user input selecting the selection button (640), the image processing device (1000) can determine the region of interest (670) as the target style image.

[0120] FIG. 8 illustrates a flowchart of a method in which an image processing device (1000) receives a user input to change an image adjustment parameter value obtained based on a target style image, according to one embodiment of the present disclosure.

[0121] In step S810, the image processing device (1000) can display a user interface for changing the acquired image adjustment parameter value, along with the adjustment image and the acquired image adjustment parameter value.

[0122] The image processing device (1000) can obtain an adjusted image in which the preprocessed image is adjusted to the image adjustment style of the target style image by performing image adjustment on the preprocessed image generated by the second image device based on image adjustment parameter values ​​obtained from an artificial intelligence model.

[0123] The image adjustment parameter value may include an image adjustment parameter set value that includes different image adjustment parameters. A user interface for changing the image adjustment parameter value may include, corresponding to each of the plurality of image adjustment parameters, identification information of the parameter, a value of the parameter, and an interface for changing the value of the parameter.

[0124] In step S820, the image processing device (1000) can receive user input that changes the acquired image adjustment parameter value.

[0125] The image processing device (1000) can receive user input to change the value of a parameter through an interface for changing the value of a parameter. As the value of the parameter changes, the image processing device (1000) can perform image adjustment again on the preprocessed image based on the changed parameter value and display the re-adjusted preprocessed image again.

[0126] In step S830, the image processing device (1000) may store the changed image adjustment parameter value as the image adjustment parameter value of the second image device based on receiving a user input to store the changed image adjustment parameter value.

[0127] FIG. 9 illustrates a method in which an image processing device (1000) changes an image adjustment parameter set value obtained from a target style image according to one embodiment of the present disclosure.

[0128] Referring to FIG. 9, the image processing device (1000) can obtain an image adjustment parameter set value of a second image device based on a target style image and display a user interface (130) for adjusting the obtained image adjustment parameter set value.

[0129] The image processing device (1000) can generate an adjusted image by obtaining an image adjustment parameter set value corresponding to a target style image from an artificial intelligence model and performing image adjustment on a preprocessed image based on the obtained image adjustment parameter set value. The image processing device (1000) can display the target style image (620), the preprocessed image (910), and the adjusted image (920).

[0130] Additionally, the image processing device (1000) can display the acquired image adjustment parameter set value and display a user interface (930) for adjusting the displayed image adjustment parameter set value.

[0131] For example, as illustrated in FIG. 9, the image processing device (1000) can display a user interface (930) for adjusting parameter values ​​by representing parameter values ​​corresponding to each parameter as a horizontal length and adjusting the horizontal length.

[0132] Upon receiving user input to change parameter values, the image processing device (1000) can perform image adjustment on the preprocessed image again based on the changed parameter values. Additionally, the image processing device (1000) can display the adjusted image again. For example, upon receiving user input to increase the brightness value, the image processing device (1000) can display a brighter adjusted image (920) by performing image adjustment on the preprocessed image again based on the increased brightness value.

[0133] The image processing device (1000) may display a target image addition interface (940) for acquiring additional images other than the images of the first image device acquired from a storage device or server. After checking the displayed adjustment image (920), the user may want to check the style of the image of the other image device other than the first image device. Additionally, the user may want to check the style of the other image of the first image device that has not been received.

[0134] Based on receiving user input for selecting a target image addition interface (940), the image processing device (1000) may display a user interface for receiving images from a storage location or server (e.g., the user interface of FIG. 5). Additionally, the image processing device (1000) may select a target style image among other newly received images and previously received images, and obtain a set of image adjustment parameters corresponding to the selected target style image.

[0135] According to one embodiment of the present disclosure, an image processing device (1000) may store image adjustment parameter set values ​​corresponding to a shooting area. The image processing device (1000) may display an interface (950) for selecting a shooting area.

[0136] The image processing device (1000) can display an interface (960) for inputting identification information of an image adjustment parameter set value.

[0137] The image processing device (1000) can store displayed image adjustment parameter set values ​​based on receiving user input selecting a storage interface (970) for storing image adjustment parameter set values. The image processing device (1000) can store displayed image adjustment parameter set values ​​corresponding to the selected shooting area and input identification information.

[0138] FIG. 10 illustrates a flowchart of a method in which an image processing device (1000) displays a plurality of image adjustment parameter candidate values ​​according to one embodiment of the present disclosure.

[0139] In step S1010, the image processing device (1000) can acquire a plurality of images generated by the first image device.

[0140] The image processing device (1000) can receive a plurality of images generated by the first image device from a storage device or server of the first image device. The first image device may be a single image device, and may include a plurality of image devices depending on the embodiment.

[0141] In step S1020, the image processing device (1000) can obtain multiple image adjustment parameter values ​​from the artificial intelligence model by inputting the acquired multiple images into the artificial intelligence model.

[0142] The image processing device (1000) inputs each of the acquired multiple images into an artificial intelligence model and can obtain image adjustment parameter values ​​of a second image device corresponding to the style of each of the multiple images from the artificial intelligence model.

[0143] In step S1030, the image processing device (1000) can determine at least one image adjustment parameter candidate value based on a plurality of image adjustment parameter values.

[0144] For example, the image processing device (1000) can determine at least one image adjustment parameter candidate value among a plurality of image adjustment parameter values ​​that are frequently used.

[0145] Additionally, for example, the image processing device (1000) can determine the average value of a plurality of image adjustment parameter values ​​as at least one image adjustment parameter candidate value.

[0146] In step S1040, the image processing device (1000) can display at least one adjustment image corresponding to at least one image adjustment parameter candidate value.

[0147] The image processing device (1000) can generate at least one adjusted image by performing image adjustment on a preprocessed image of a second image device based on at least one candidate value for image adjustment parameters. The image processing device (1000) can display the generated at least one adjusted image.

[0148] By displaying at least one adjustment image with an image processing device (1000), the user can directly check the image when at least one image adjustment parameter candidate value is applied to the preprocessed image.

[0149] In step S1050, the image processing device (1000) can determine an image adjustment parameter value corresponding to the selected adjustment image as an image adjustment parameter value of the second image device based on receiving a user input selecting one of at least one adjustment image.

[0150] FIG. 11 illustrates a method in which an image processing device (1000) sets image adjustment parameter set values ​​based on a plurality of adjustment images according to one embodiment of the present disclosure.

[0151] Referring to FIG. 11, the image processing device (1000) can display a plurality of adjustment images.

[0152] The image processing device (1000) can input a plurality of images of a first image device obtained from a storage device or server into an artificial intelligence model and obtain a plurality of image adjustment parameter set values ​​corresponding to the plurality of images from the artificial intelligence model.

[0153] The image processing device (1000) can determine candidate values ​​for image adjustment parameter sets based on the acquired multiple image adjustment parameter set values.

[0154] For example, the image processing device (1000) can determine candidate image adjustment parameter set values ​​based on the frequency of use of a plurality of image adjustment parameter set values. Since the image adjustment parameter set value with the highest frequency is the image adjustment parameter set value most frequently used by the user, it may be the image adjustment parameter set value desired by the user.

[0155] The image processing device (1000) can display a first adjusted image (110a) by performing image adjustment on a preprocessed image (910) based on the image adjustment parameter set value with the highest usage frequency. Additionally, the image processing device (1000) can display a second adjusted image (110b) by performing image adjustment on the preprocessed image (910) based on the image adjustment parameter set value with the second highest usage frequency. Additionally, the image processing device (1000) can display a third adjusted image (110c) by performing image adjustment on the preprocessed image (910) based on the image adjustment parameter set value with the third highest usage frequency.

[0156] The image processing device (1000) can receive user input selecting one of a plurality of adjustment images (110a, 110b, 110c). The image processing device (1000) can display a user interface (930) that indicates an image adjustment parameter set value corresponding to the selected adjustment image.

[0157] The image processing device (1000) can store image adjustment parameter set values ​​corresponding to the selected adjustment image based on receiving a user input selecting a storage interface (970) for storing image adjustment parameter values. The image processing device (1000) can store image adjustment parameter set values ​​corresponding to the selected adjustment image as image adjustment parameter set values ​​of the second image device in correspondence with the selected shooting area and input identification information.

[0158] FIG. 12 illustrates a block diagram of an image processing device (1000) according to one embodiment of the present disclosure.

[0159] Referring to FIG. 12, the image processing device (1000) may include a processor (1100), a communication module (1300), a memory (1400), an input interface (1500), an output module (1600), an X-ray irradiation unit (1800), and an X-ray detector (1900). The same reference numerals are used for configurations identical to those shown in FIG. 3.

[0160] Not all of the illustrated components are essential components of the image processing device (1000). The image processing device (1000) may be implemented with more components than those illustrated in FIG. 12, or the image processing device (1000) may be implemented with fewer components than those illustrated in FIG. 12.

[0161] The processor (1100) can control the overall operation of the image processing device (1000). The processor (1100) can control the communication module (1300), input interface (1500), output module (1600), X-ray irradiation unit (1800), and X-ray detector (1900) by executing at least one instruction or program stored in memory (1400).

[0162] The memory (1400) stores various information, data, instructions, programs, etc., necessary for the operation of the image processing device (1000). The memory (1400) may include at least one of volatile memory or non-volatile memory, or a combination thereof. The memory (1400) may include at least one type of storage medium among flash memory type, hard disk type, multimedia card micro type, card type memory (e.g., SD or XD memory, etc.), RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk, and optical disk. Additionally, the image processing device (1000) may operate a web storage or cloud server that performs storage functions over the internet.

[0163] At least one processor (1100) and at least one memory (1400) may be included in a single control unit. For example, at least one processor (1100) and at least one memory (1400) may be included in a single MCU (micro controller unit).

[0164] The communication module (1300) can transmit and receive information to and from an external device or an external server according to a protocol based on the control of the processor (1100). The communication module (1300) may include at least one communication module and at least one port for transmitting and receiving data to and from an external device (not shown).

[0165] Additionally, the communication module (1300) can communicate with an external device through at least one wired or wireless communication network. The communication module (1300) may include at least one short-range communication module or a long-range communication module, or a combination thereof. The communication module (1300) may include at least one antenna for wirelessly communicating with another device.

[0166] The short-range communication module may include at least one communication module (not shown) that performs communication according to communication standards such as Bluetooth, Wi-Fi, BLE (Bluetooth Low Energy), NFC / RFID, Wi-Fi Direct, UWB, infrared communication, or ZIGBEE. Additionally, the long-range communication module may include a communication module (not shown) that performs communication through a network for internet communication. Additionally, the long-range communication module may include a mobile communication module that performs communication according to communication standards such as 3G, 4G, 5G, and / or 6G.

[0167] The output module (1600) may include a display (1610 in FIG. 3) and an audio output module (not shown).

[0168] A display (1610 in FIG. 3) can output image data processed by an image processing unit (not shown) through a display panel (not shown) under the control of a processor (1100). The display panel (not shown) may include at least one of a liquid crystal display, a thin film transistor-liquid crystal display, an organic light-emitting diode, a flexible display, a 3D display, and an electrophoretic display.

[0169] The display (1610 in FIG. 3) can display a screen for guiding user input, an X-ray image, a screen showing the status of the image processing device (1000), etc.

[0170] An audio output module (not shown) can output an audio signal to the outside of the image processing device (1000). The audio output module (not shown) may include, for example, a speaker or a receiver. The speaker may be used for general purposes, such as multimedia playback or recording playback.

[0171] The input interface (1500) can receive user input for controlling the image processing device (1000). The input interface (1500) receives user input and transmits it to the processor (1100). The input interface (1500) can receive commands for shooting protocols, shooting conditions, shooting timing, position control of the X-ray irradiation unit (1800), etc.

[0172] The input interface (1500) may include, but is not limited to, a user input electronic device including a touch panel that detects a user's touch, a button that receives a user's push operation, a wheel that receives a user's rotation operation, a keyboard, and a dome switch.

[0173] Additionally, the input interface (1500) may include a voice recognition device for voice recognition. For example, the voice recognition device may be a microphone, and the voice recognition device may receive a user's voice command or voice request. Accordingly, the processor (1100) may control the execution of an action corresponding to the voice command or voice request.

[0174] According to one embodiment of the present disclosure, an input interface (1500) and an output module (1600) may be provided in a workstation (not shown) that receives commands from a user and provides information.

[0175] The X-ray irradiation unit (1800) can generate X-rays and irradiate the target with X-rays.

[0176] The X-ray irradiation unit (1800) may be equipped with an X-ray source that generates X-rays and a collimator that controls the irradiation area of ​​the X-rays generated from the X-ray source.

[0177] The X-ray detector (1900) can detect X-rays that have been irradiated from the X-ray irradiation unit (1800) and have passed through the object.

[0178] The X-ray detector (1900) may include a detection element that detects X-rays and converts them into image data. At least one processor (1100) may generate a preprocessed image using image data received from the X-ray detector (1900). The X-ray detector (1900) may or may not be included as a component of the image processing device (1000). In the latter case, the X-ray detector (1900) may be registered with the image processing device (1000) by a user.

[0179] The image processing device (1000) may include, but is not limited to, a fixed X-ray device connected to the ceiling of the examination room, a C-arm type X-ray device, or a mobile X-ray device.

[0180] At least one processor (1100) can acquire a target-style image generated by a first imaging device through a communication module (1300). The first imaging device is an X-ray device, and the target-style image may be an X-ray image.

[0181] At least one processor (1100) can input a target style image generated by a first image device into an artificial intelligence model. At least one processor (1100) can obtain image adjustment parameter values ​​of an image processing device (1000) from the artificial intelligence model.

[0182] The artificial intelligence model may be trained such that when an external image is input as input to the artificial intelligence model, a set of image adjustment parameters that converts the preprocessed image generated by the image processing device (1000) into the image adjustment style of the external image is output.

[0183] At least one processor (1100) can perform image adjustment on a preprocessed image generated by an image processing device (1000) based on image adjustment parameter values.

[0184] At least one processor (1100) can display, through a display (1610), an adjusted image in which the preprocessed image is adjusted to the image adjustment style of the target style image.

[0185] At least one processor (1100) can receive user input through an input interface (1500) to store an acquired image adjustment parameter set value. Based on the user input, at least one processor (1100) can store the acquired image adjustment parameter set value as an image adjustment parameter set value.

[0186] At least one processor (1100) can receive user input through an input interface (1500) to obtain an image adjustment parameter value of an image processing device (1000) corresponding to a target style image of a first image device.

[0187] At least one processor (1100) can acquire a target style image generated in the first image device from a server or storage device connected to the first image device.

[0188] At least one processor (1100) can display a plurality of images generated by a first image device through a display (1610). At least one processor (1100) can receive user input to select a target style image among the plurality of displayed images through an input interface (1500).

[0189] At least one processor (1100) can receive user input to set a region of interest on an acquired target style image through an input interface (1500). At least one processor (1100) can input image data of the region of interest into an artificial intelligence model and acquire the output of the artificial intelligence model as an image adjustment parameter value of an image processing device (1000).

[0190] At least one processor (1100) can display, through a display (1610), a user interface for changing the acquired image adjustment parameter value, along with the adjustment image and the acquired image adjustment parameter value. At least one processor (1100) can receive user input for changing the acquired image adjustment parameter value through the user interface.

[0191] At least one processor (1100) can acquire a plurality of images generated by a first image device and input the acquired plurality of images into an artificial intelligence model, thereby acquiring a plurality of image adjustment parameter values ​​from the artificial intelligence model.

[0192] At least one processor (1100) can determine one of the acquired image adjustment parameter values ​​as an image adjustment parameter value of the image processing device (1000) based on the frequency of use of the acquired image adjustment parameter values.

[0193] At least one processor (1100) can acquire a plurality of images generated by a first image device and input the acquired plurality of images into an artificial intelligence model, thereby acquiring a plurality of image adjustment parameter values ​​from the artificial intelligence model.

[0194] At least one processor (1100) determines a plurality of candidate image adjustment parameter values ​​among a plurality of image adjustment parameter values ​​and can display a plurality of adjustment images corresponding to the candidate image adjustment parameter values ​​through a display (1610).

[0195] At least one processor (1100) can determine an image adjustment parameter value corresponding to the selected adjustment image as an image adjustment parameter value of the image processing device (1000) based on receiving a user input selecting one of a plurality of adjustment images through an input interface (1500).

[0196] At least one processor (1100) can receive user input through an input interface (1500) for selecting a shooting area to set image adjustment parameter values. At least one processor (1100) can acquire an artificial intelligence model corresponding to the selected shooting area. At least one processor (1100) can store the acquired image adjustment parameter values ​​as image adjustment parameter values ​​corresponding to the shooting area.

[0197] A device-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory storage medium' simply means that it is a tangible device and does not contain a signal (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently and cases where it is stored temporarily. For example, a 'non-transitory storage medium' may include a buffer in which data is stored temporarily.

[0198] According to one embodiment, the method according to the various embodiments disclosed herein may be provided by being included in a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or distributed online (e.g., download or upload) through an application store or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product (e.g., downloadable app) may be temporarily stored or temporarily created on a device-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

Claims

1. In an image processing device (1000), Display (1610); Communication module (1300); Input interface (1500); Memory for storing instructions (1400); and It includes at least one processor (1100) including processing circuitry, and When the above instructions are executed individually or collectively by the at least one processor (1100), the image processing device (1000) is enabled, Through the communication module (1300), a target style image generated by the first imaging device is obtained, and The target style image generated by the first imaging device is input into an artificial intelligence model corresponding to the second imaging device, and image adjustment parameter values ​​of the second imaging device are obtained from the artificial intelligence model, and Based on the above image adjustment parameter values, image adjustment is performed on the preprocessed image generated by the second image device, thereby displaying the adjusted image through the display (1610), in which the preprocessed image is adjusted to the image adjustment style of the target style image. An image processing device (1000) that stores the acquired image adjustment parameter set value as an image adjustment parameter set value corresponding to the second image device based on receiving a user input that stores the acquired image adjustment parameter set value through the input interface (1500).

2. In Paragraph 1, An image processing device in which the artificial intelligence model corresponding to the second image device is trained such that when an external image is input as an input to the artificial intelligence model, a set of image adjustment parameters that converts a preprocessed image generated by the second image device into an image adjustment style of the external image is output.

3. In either Paragraph 1 or Paragraph 2, When the above instructions are executed by the at least one processor, the image processing device, An image processing device that, upon receiving a user input for obtaining an image adjustment parameter value of the second image device corresponding to the target style image of the first image device, obtains a target style image generated by the first image device from a server or storage device connected to the first image device.

4. In any one of paragraphs 1 to 3, When the above instructions are executed by the at least one processor, the image processing device, An image processing device that displays a plurality of images generated by the first image device and receives user input for selecting the target style image among the plurality of displayed images.

5. In any one of paragraphs 1 to 4, When the above instructions are executed by the at least one processor, the image processing device, Receiving user input to set a region of interest on the above-mentioned target style image, and An image processing device that inputs image data of the region of interest into the artificial intelligence model and obtains the output of the artificial intelligence model as an image adjustment parameter value of the second image device.

6. In any one of paragraphs 1 to 5, When the above instructions are executed by the at least one processor, the image processing device, Through the above display, a user interface for changing the acquired image adjustment parameter value is displayed together with the adjustment image and the acquired image adjustment parameter value. An image processing device that receives user input to change the acquired image adjustment parameter value through the above user interface.

7. In any one of paragraphs 1 through 6, When the above instructions are executed by the at least one processor, the image processing device, Acquiring a plurality of images generated by the first imaging device, and By inputting the above-mentioned multiple images into the artificial intelligence model, multiple image adjustment parameter values ​​are obtained from the artificial intelligence model, and An image processing device that determines one of the acquired plurality of image adjustment parameter values ​​as an image adjustment parameter value of the second image device based on the frequency of use of the acquired plurality of image adjustment parameter values.

8. In any one of paragraphs 1 through 7, When the above instructions are executed by the at least one processor, the image processing device, Acquiring a plurality of images generated by the first imaging device, and By inputting the above-mentioned multiple images into the artificial intelligence model, multiple image adjustment parameter values ​​are obtained from the artificial intelligence model, multiple image adjustment parameter candidate values ​​are determined among the multiple image adjustment parameter values, and multiple adjustment images corresponding to the multiple image adjustment parameter candidate values ​​are displayed. An image processing device that, based on receiving a user input selecting one of the plurality of adjustment images, determines an image adjustment parameter value corresponding to the selected adjustment image as an image adjustment parameter value of the second image device.

9. In any one of paragraphs 1 through 8, The above image processing device is a medical image processing device, and When the above instructions are executed by the at least one processor, the image processing device, Receiving user input to select a shooting area to set the above image adjustment parameter values, and Acquire the artificial intelligence model corresponding to the selected shooting area, and An image processing device that stores the above-mentioned image adjustment parameter value as the image adjustment parameter value corresponding to the above-mentioned shooting area.

10. In any one of paragraphs 1 through 9, The above second image device is an image processing device, which is the image processing device.

11. Regarding methods for processing images, A step of acquiring a target style image generated by a first imaging device; A step of inputting the target style image generated by the first imaging device into an artificial intelligence model corresponding to the second imaging device, and obtaining image adjustment parameter values ​​of the second imaging device from the artificial intelligence model; A step of displaying an adjusted image in which the preprocessed image is adjusted to the image adjustment style of the target style image by performing image adjustment on the preprocessed image generated by the second image device based on the image adjustment parameter values ​​above; and An image processing method comprising the step of storing the acquired image adjustment parameter set value as an image adjustment parameter set value corresponding to the second image device, based on receiving a user input that stores the acquired image adjustment parameter set value.

12. In Paragraph 11, An image processing method in which the artificial intelligence model corresponding to the second image device is trained such that when an external image is input as an input to the artificial intelligence model, a set of image adjustment parameters that converts a preprocessed image generated by the second image device into an image adjustment style of the external image is output.

13. In either Article 11 or Article 12, The step of acquiring a target style image generated by the first imaging device is: An image processing method comprising the step of obtaining a target style image generated by the first image device from a server or storage device connected to the first image device, upon receiving a user input for obtaining an image adjustment parameter value of the second image device corresponding to the target style image of the first image device.

14. In any one of paragraphs 11 through 13, The step of acquiring a target style image generated by the first imaging device is: An image processing method that displays a plurality of images generated by the first image device and receives a user input selecting the target style image among the plurality of displayed images.

15. In any one of paragraphs 11 through 14, The step of inputting the target style image generated by the first imaging device into an artificial intelligence model corresponding to the second imaging device, and obtaining image adjustment parameter values ​​of the second imaging device from the artificial intelligence model, A step of receiving user input to set a region of interest on the target style image acquired above; and An image processing method comprising the step of inputting image data of the region of interest into the artificial intelligence model and obtaining the output of the artificial intelligence model as an image adjustment parameter value of the second image device.

Images