System for monitoring changes over time, control method, and program

Abstract

To provide a secular change confirmation system, an imaging apparatus, a control method and a program which can capture a flawless affected portion image without a specific burden on a user.SOLUTION: A medical examination support system 100 (secular change confirmation system) allows a user U to confirm a secular change of an affected portion W of a skin disease of a patient P by using a camera 1, a PC 3 and a server 2. The server 2 acquires from the camera 1 an imaging parameter including an affected portion image and imaging time and date information and imaging object specification information for specifying the affected portion W every time the affected portion W is imaged by the camera 1 and the affected portion image is generated, and issues a warning that notifies the user U to the camera 1 when it is determined that the photographed images generated in the present imaging and the previous imaging by the camera 1 have the same imaging object specification information and a difference in a feature amount S obtained by the server 2 is equal to or greater than a threshold. The camera 1 notifies the user U of the warning when the warning is issued.SELECTED DRAWING: Figure 7

Description

Technical Field

[0001] The present invention relates to a time-change confirmation system , regulation a method, and a program, and particularly to a time-change confirmation system for assisting in the diagnosis and treatment of skin diseases , regulation a method, and a program.

Background Art

[0002] Conventionally, in the medical field, doctors, nurses, etc. (hereinafter simply referred to as "users") take pictures of the affected part of a skin disease such as a trauma, ulcer, or pressure ulcer caused by bedriddenness with a camera a plurality of times at intervals of time in order to confirm the change over time of the affected part.

[0003] For example, the user can easily observe the progress of improvement or deterioration of the affected part by arranging in time series a group of captured images (hereinafter referred to as "affected part image group") including the affected part taken a plurality of times.

[0004] Therefore, such an affected part image group is suitably used for diagnosing skin diseases that change over several days to several years.

[0005] Also, if the affected part image group is accumulated after being subjected to appropriate processing rather than being simply accumulated, it becomes an image that can further assist in the diagnosis and treatment of skin diseases.

[0006] For example, as shown in Fig. 8(a), there is known a system in which first, a captured image of the affected part taken by the user is input into a learned model, and the learned model discriminates and outputs the type of skin disease (see, for example, Patent Document 1). Thereby, the user can perform diagnosis and treatment by referring to the type of skin disease output by the learned model.

[0007] Furthermore, as shown in Figure 8(b), there is a known analysis device that analyzes images of a patient's ulcer (affected area images) taken by the user at the time of the patient's visit (t-1, t) and obtains progression parameters 8 indicating the changes in the size of the ulcer over time from each image (see, for example, Patent Document 2). This analysis device then calculates a progression index 9, which is the difference between the values ​​of the progression parameters 8 at the time of the visit (t-1, t), and compares it with that of past patients. Based on the results of this comparison, the analysis device presents the treatment history, type and dosage of medication, etc., administered to past patients who were determined to have a skin disease similar to the current patient's. This allows the user to refer to the treatment history, type and dosage of medication, etc., of past patients who were determined to have a similar skin disease by the analysis device and provide medical care to the patient. [Prior art documents] [Patent Documents]

[0008] [Patent Document 1] Japanese Patent Publication No. 2021-28808 [Patent Document 2] Special Publication No. 2020-528587 [Overview of the Initiative] [Problems that the invention aims to solve]

[0009] However, while the system described in Patent Document 1 can infer the type of skin disease by inputting images of the affected area into a trained model, it cannot check the characteristics of the images at the time of capture. Specifically, Patent Document 1 cannot check whether the images of the affected area are suitable for the trained model to infer the type of skin disease, whether they are suitable for the user to refer to during medical examination, or whether they are associated with correct patient information.

[0010] Furthermore, the analysis device described in Patent Document 2 can recommend to the patient the treatment history of patients with similar skin diseases, as well as the type and dosage of medication, based on the temporal changes in progression parameters obtained by analyzing images of the affected area. However, similar to Patent Document 1, it is not possible to check the characteristics of the affected area images at the time of shooting. Specifically, Patent Document 2 cannot check whether the affected area images are suitable for analysis by the analysis device, whether they are suitable for the user to refer to during medical treatment, or whether they are linked to correct patient information.

[0011] Therefore, in both Patent Documents 1 and 2, there is a possibility that users may take defective images of the affected area. As a result, after taking the images, for example, at a conference where users gather to discuss treatment plans, they may notice the defects in the images of the affected area and have to take the images again.

[0012] Therefore, the objective of the present invention is a time-series change confirmation system that can acquire a group of images suitable for confirming changes over time in a subject being photographed, without any special burden on the user. , regulation The purpose is to provide methods and programs. [Means for solving the problem]

[0013] To solve the above problems, the time-series change confirmation system according to claim 1 of the present invention comprises: an imaging device that photographs a target object multiple times at intervals according to the user's instructions and generates a group of captured images; a UI for the user to confirm the time-series change of the target object; and an image processing device that performs image processing on each of the group of captured images for display on the UI, wherein the image processing device includes acquisition means for acquiring from the imaging device each time one of the group of captured images is generated by the imaging device the captured image, the shooting parameters including the shooting date and time information, and the target identification information for identifying the target object. A means for identifying the target to be photographed from each of the group of photographed images using the target identification information; a first calculation means for calculating the characteristic quantity of the identified target to be photographed for each of the group of photographed images using the shooting parameters; and a second calculation means for comparing the shooting date and time information and the calculated characteristic quantity for each of the photographed images generated during the current and previous shooting sessions by the imaging device, and calculating the difference in the characteristic quantity resulting from the change over time. before Recorded today The captured images generated during the current and previous shooting sessions have the same acquired target identification information, As a result of the aforementioned changes over time The difference between the aforementioned features is greater than or equal to a threshold. andWhen determined, issuing means for issuing a warning to the imaging device to the user, and in the imaging device, when the warning is issued by the issuing means, notification means for notifying the user of the warning are provided. Encourage reshooting

Advantages of the Invention

[0015] According to the present invention, a group of imaging images suitable for confirming the change over time of the imaging object can be obtained without imposing a special burden on the user.

Brief Description of the Drawings

[0016] [Figure 1] It is a diagram showing the overall configuration of a medical support system as a change-over-time confirmation system. [Figure 2] It is a block diagram showing the hardware configurations of the camera and the server in FIG. 1. [Figure 3] It is a diagram for explaining a lesion image viewer application, which is an application for viewing a lesion image and is executed on the PC in FIG. 1. [Figure 4] It is a flowchart of a lesion image acquisition process executed in the camera and the server. [Figure 5] It is a diagram showing an example of a process of calculating a feature amount from a lesion image in step S410 of FIG. 4. <​​​​​​​​​​​​​​​​​

[0018] As shown in Figure 1, the medical support system 100 includes a camera 1, a server 2, a PC 3, and an identification tag L.

[0019] Camera 1 (imaging device) is a camera used in a medical setting by one or more users (hereinafter simply referred to as "User U"), such as doctors, nurses, and caregivers. User U takes and records images of the affected area W (hereinafter simply referred to as "affected area images") of patient P's skin disease, such as trauma, ulcers, or pressure ulcers caused by being bedridden, multiple times with Camera 1 to confirm the changes in the affected area over time. By viewing the group of affected area images thus taken and recorded side by side, User U can easily observe the progress of improvement or deterioration of the affected area. For this reason, the group of affected area images is suitable for treating affected areas that change over several days to several years.

[0020] While this embodiment describes the use of the time-series change confirmation system in a medical setting, it is not limited to medical settings as long as it is used in any setting where it is necessary to confirm the time-series changes of a subject using captured images. For example, it could be used in a setting where images of a moving subject on a stage are captured by multiple different cameras and displayed in a switching manner, and the change in the subject's position over time needs to be confirmed.

[0021] Server 2 (image processing device) includes a storage 24, a communication unit (not shown) that wirelessly connects to camera 1 and PC 3 to send and receive information including images, and a GPU 22 (Figure 2) that performs appropriate image processing on the images. Note that Server 2 is not limited to the configuration of this embodiment, as long as it can communicate with camera 1 and PC 3. For example, Server 2 may be connected to camera 1 and PC 3 via a wired connection.

[0022] PC3(UI) is a PC with a display and control panel used by user U. In response to user U's operations on the control panel, it acquires a group of images of the affected area captured and recorded by camera 1 via server 2 and displays these images on the display.

[0023] The identification tag L is a wristband with a barcode for identifying patient P, and in this embodiment, it is worn on the left wrist of patient P who has the affected area W.

[0024] Figure 1 illustrates the workflow of medical treatment using the medical support system 100.

[0025] First, user U uses camera 1 to photograph the affected area W and identification tag L of patient P. As a result, camera 1 generates an image of the affected area W and an image of the identification tag L.

[0026] Subsequently, camera 1 transmits the image of the affected area and the identification tag image to server 2.

[0027] Server 2 performs appropriate image processing on the affected area images and identification tag images from camera 1 using the GPU 22 and records them in its built-in storage 24.

[0028] User U can view the images of the affected area by operating the control panel of PC3 and displaying the images recorded in the storage 24 of Server 2 on the display of PC3. This allows User U to diagnose patient P and affected area W, and to decide on the treatment plan by referring to the images.

[0029] Note that although Camera 1, Server 2, and PC 3 are depicted as separate entities in Figure 1, they can be merged or integrated as desired, or their processing can be transferred to one another.

[0030] Figure 2 is a block diagram showing the hardware configuration of Camera 1 and Server 2, respectively.

[0031] First, we will explain the hardware configuration of camera 1 using Figure 2.

[0032] Camera 1 includes a controller 11, an image sensor 12, a group of buttons 13, a touch panel 14, a display 15, storage 16, a zoom lens 17, a shutter 18, and a network interface 19.

[0033] The controller 11 oversees the information processing within the camera 1 and controls other units.

[0034] The image sensor 12 captures light from the subject through the zoom lens, converts it into an electrical signal, and generates an image of the affected area or an identification tag image.

[0035] The button group 13 and the touch panel 14 accept input from user U.

[0036] The display (display means) 15 displays the captured image to the user U. Furthermore, if a warning is issued by the server 2 (described later), the display 15 displays a warning dialog 71 (Figure 7) either alone or together with the captured image. The display 15 may also display shooting parameters, including shooting date and time information, zoom, autofocus, exposure, ISO sensitivity, and ambient light information.

[0037] The captured images are saved to storage 16, either temporarily or permanently.

[0038] The zoom lens 17 adjusts the angle of view and focus during shooting according to the user U's control.

[0039] The shutter 18 controls the light entering the image sensor 12.

[0040] The network interface 19 transmits and receives images and their parameters generated by the image sensor 12 to and from an external device (server 2 in this embodiment).

[0041] Next, we will explain the hardware configuration of Server 2 using Figure 2.

[0042] Server 2 includes a CPU 21, a GPU 22, memory 23, storage 24, and a network interface 25.

[0043] CPU21 oversees information processing within Server 2 and controls other units.

[0044] GPU22 performs image processing such as image quality improvement and image recognition.

[0045] Memory 23 stores temporary information during processing on GPU 22.

[0046] Storage 24 stores images temporarily or permanently.

[0047] The network interface 25 transmits and receives captured images and their parameters to and from external devices (camera 1 and PC 3 in this embodiment).

[0048] Figure 3 is a diagram illustrating the affected area image viewer application 31, which is an application for viewing images of the affected area and runs on PC3.

[0049] Using Figure 3, we will explain how user U can view the images of the affected area taken by camera 1 on PC 3.

[0050] PC3 can receive information, including captured images, from server 2.

[0051] When the affected area image viewer application 31 shown in Figure 3 is launched on PC3, the affected area image viewer application 31 retrieves a group of affected area images from server 2.

[0052] The affected area image viewer application 31 has an address bar 32 for specifying the patient and the affected area. For example, as shown in Figure 3, when user U specifies patient P and affected area W in the address bar 32, the affected area image viewer application 31 extracts images from the group of affected area images acquired from server 2 that are associated with the specified patient P and affected area W.

[0053] The affected area image viewer application 31 has an image display area 33a and an image display area 33b.

[0054] Image display area 33a displays relatively older images of the affected area from the extracted captured images, and image display area 33b displays relatively newer images of the affected area from the extracted captured images.

[0055] The relatively older affected area images displayed in the image display area 33a include images of the older affected area Wa, while the relatively newer affected area images displayed in the image display area 33b include images of the newer affected area Wb.

[0056] By using the affected area image viewer application 31 on PC3, user U can compare and view old affected area Wa and new affected area Wb to confirm the progression of the disease and the effectiveness of treatment, thereby enabling a more accurate diagnosis.

[0057] Figure 4 is a flowchart of the affected area image acquisition process performed by camera 1 and server 2.

[0058] This process is executed on camera 1 by controller 11 reading a program stored in a ROM (not shown) of camera 1. On server 2, it is executed by CPU 21 reading a program stored in server 2's ROM.

[0059] First, in step S401, when the power to camera 1 is turned on, the controller 11 sends a connection request to server 2 via the network interface 19.

[0060] Similarly, in step S402, when the power to server 2 is turned on, the CPU 21 sends a connection request to server 2 via the network interface 25.

[0061] In step S403, when server 2 responds to the connection request in step S401, controller 11 establishes a connection between camera 1 and server 2. Similarly, in step S404, when camera 1 responds to the connection request in step S402, CPU 21 establishes a connection between camera 1 and server 2. This enables camera 1 and server 2 to send and receive information, including captured images, to each other.

[0062] In step S405, when user U inputs patient information (information identifying the target of imaging) for patient P, controller 11 temporarily stores the input patient information in storage 16. Also, when user U takes a picture of patient P's identification tag L with camera 1, controller 11 extracts patient P's patient information from the barcode on the identification tag L from the captured image of the identification tag L and temporarily stores it in storage 16. The patient information obtained here includes the name of patient P, patient P's ID number, and the name of the affected area W, which is imaged in step S406.

[0063] In step S406, when user U captures a picture of patient P's affected area W with camera 1, controller 11 temporarily saves the image of the affected area, including the affected area W obtained from the capture, and the capture parameters used at the time of the capture to storage 16.

[0064] In step S407, the controller 11 (transmission means) transmits to the server 2 the patient information temporarily stored in the storage 16 in step S405, and the affected area image and its imaging parameters temporarily stored in the storage 16 in step S406. The patient information and imaging parameters may be embedded in the affected area image, for example, in Exif format.

[0065] In step S408, the CPU 21 (acquisition means) receives patient information and affected area images transmitted from the camera 1 in step S407 and stores them in the built-in storage 24 of the server 2.

[0066] In step S409, the CPU 21 extracts the affected area W from the affected area image and stores it in the built-in storage 24 of the server 2. Details of the extraction of the affected area W will be described later.

[0067] In step S410, the CPU 21 calculates feature quantities S from the shape and color of the affected area W extracted in step S409 and stores them in the built-in storage 24 of the server 2. Details of the calculation of feature quantities S will be described later.

[0068] In step S411, the CPU 21 refers to the previous feature quantity S stored in the storage 24. The previous feature quantity S is the feature quantity calculated by performing the processing in steps S409 and S410 on the affected area image obtained from taking an image of the affected area W of the same patient P before the current image. Note that in the processing shown in Figure 4, the feature quantity S is calculated each time the affected area W of the same patient P is photographed.

[0069] In step S412, the CPU 21 determines whether the difference between the feature quantities S from the previous image capture and the current image capture is greater than or equal to a threshold. If the difference in feature quantities S is less than the threshold (NO in step S412), the process terminates. On the other hand, if the difference in feature quantities S is greater than or equal to the threshold (YES in step S412), the process proceeds to step S413. Details of the threshold will be described later.

[0070] In step S413, the CPU 21 (issuing means) issues a warning and sends an instruction to camera 1 to display the warning.

[0071] In step S414, after transmitting patient information, an image of the affected area, and its shooting parameters in step S407, the controller 11 receives a warning display instruction in step S413 and displays a warning dialog 71 (Figure 7) on the display 15 (notification means). This allows the controller 11 to notify the user U of the warning. This warning dialog 71 will be described later. Note that this embodiment is not limited to any configuration that allows the camera 1 to notify the user U of a warning issued by the CPU 21. For example, this warning could be notified by the illumination of a lamp (not shown) in the camera 1, or by the emission of a warning sound from a speaker (not shown) in the camera 1.

[0072] Next, using Figure 5, we will explain the process by which the CPU 21 of server 2 calculates feature quantities S from the affected area image (step S410 in Figure 4).

[0073] In step S51, the CPU 21 reads the affected area image received from camera 1 and stored in storage 24 from storage 24. The read affected area image is accompanied by metadata including shooting parameters such as zoom and autofocus information. If camera 1 is equipped with an image plane phase difference sensor or is a multi-camera array, the affected area image consists of multiple images with parallax, and the distance map calculated from these images is also included in the metadata.

[0074] In step S52, the CPU 21 uses the GPU 22 (estimation means) to estimate the region of the affected area W contained in the affected area image read in step S51. The GPU 22 uses either deductive image processing or segmentation using a pre-trained neural network to distinguish between the affected area W and skin from the affected area image. The CPU 21 also calculates the number of pixels (pixel count) that make up the affected area W in the extracted affected area image.

[0075] In step S53, the CPU 21 (calculation means) calculates the actual area (in millimeters square) of each pixel (1 pixel area) that constitutes the affected area W in the affected area image. Specifically, it converts the 1 pixel area into millimeters square using the zoom and autofocus information associated with the affected area image as metadata. If the metadata of the affected area image includes a distance map, the 1 pixel area of ​​any part of the affected area image may be calculated in millimeters using that distance map.

[0076] In step S54, the CPU 21 (calculation means) calculates the actual area of ​​the affected area W using the number of pixels of the affected area W calculated in step S52 and the area of ​​one pixel of the affected area W calculated in step S53.

[0077] The actual area of ​​the affected area W calculated by the process described in Figure 5 above is called the feature quantity S.

[0078] Next, using Figure 6, we will explain the threshold used to determine whether or not the CPU 21 of server 2 issues a warning (step S412 in Figure 4).

[0079] Figure 6(a) shows the horizontal axis as delta_t(s) and the vertical axis as delta_S(mm). 2 This is a graph of ).

[0080] delta_t(s) represents the time difference between the previous and current images. Each affected area image is linked to file creation date and time information, or to the image capture date and time information as metadata. Therefore, this information can be used to calculate the time difference between any two affected area images.

[0081] Delta_S(mm 2 ) is the difference in feature quantity S between the previous image and the current image. The method for calculating feature quantity S from the affected area image is as described above using Figure 5, and the difference in feature quantity S can be calculated between any two affected area images.

[0082] The graph has a line drawn to represent the threshold. The threshold is expressed by the following formula: T = alpha*delta_t T is the threshold. alpha is the slope of the line representing the threshold. The slope alpha will be discussed later.

[0083] From two images of the affected area W of patient P, taken at different times, the time difference delta_t and the feature difference delta_S can be calculated.

[0084] If the plot showing the calculated time difference delta_t and feature difference delta_S (change in the area of ​​the affected area W) is located at point A below the threshold line, as shown in Figure 6(a), then the change in the area of ​​the affected area W is less than the threshold. In this case, CPU21 does not issue a warning.

[0085] On the other hand, if the plot showing the calculated time difference delta_t and feature difference delta_S (change in the area of ​​the affected area W) is located at point B, which is above the line representing the threshold as shown in Figure 6(a), then the change in the area of ​​the affected area W is greater than or equal to the threshold. In this case, the CPU 21 issues a warning.

[0086] This enables the process shown in step S412 of Figure 4, in which, if the change in the feature (the change in the area of ​​the affected area W) exceeds a threshold, the CPU 21 of server 2 issues a warning.

[0087] Figure 6(b) is a table used to determine the slope alpha of the line representing the threshold.

[0088] In the table in Figure 6(b), the rows indicate the types of skin diseases. In this embodiment, the types of skin diseases include pressure ulcers, sores, injuries, burns, etc.

[0089] Furthermore, in the table in Figure 6(b), the columns indicate the progression of the skin disease. In this embodiment, the progression of the skin disease includes the acute phase, chronic phase, and recovery phase.

[0090] Each column in the table in Figure 6(b) stores the value of the slope alpha. For example, if there is a tendency for a large change in area per unit time during the acute phase of trauma, alpha_2a will be set to a relatively large value. Conversely, if there is a tendency for a small change in area per unit time during the chronic phase of pressure ulcers, alpha_0c will be set to a relatively small value.

[0091] In this embodiment, the slope alpha is switched based on the table in Figure 6(b) depending on the type and progression of the skin disease in the affected area. This allows for manipulation of the likelihood of issuing a warning in step S413 of Figure 4.

[0092] Furthermore, the progression of the skin disease in the affected area is determined by the time elapsed between the previous scan and the current scan.

[0093] Figure 7 shows the warning dialog 71 displayed on the camera 1's display 15 in step S414 of Figure 4.

[0094] Figure 7 illustrates the warning dialog 71 that camera 1 warns user U.

[0095] When camera 1 receives the warning display instruction sent from server 2 in step S413 of Figure 4, a warning dialog 71 is displayed on camera 1's display 15 in step S414 of Figure 4.

[0096] Warning dialog 71 includes the message, "The settings / ambient lighting may be different from when you last took a picture."

[0097] For example, the image of the affected area taken in this instance may differ from the image of the affected area that would be expected based on the settings for patient P and affected area W.

[0098] Alternatively, user U might change parameters such as exposure and ISO sensitivity between the previous and current shooting sessions.

[0099] Furthermore, the lighting conditions can differ significantly; for example, the previous photo was taken under LED lighting at night, while this time the photo was taken with the setting sun shining through the window.

[0100] In most cases, these effects manifest as an excessively steep change in the feature vector S of the affected area image (the difference in feature vector S is greater than or equal to a threshold).

[0101] Therefore, if the difference in feature quantities S exceeds a threshold, the CPU 21 of server 2 issues a warning and sends an instruction to camera 1 to display the warning. When camera 1 receives this instruction to display the warning from server 2, the controller 11 notifies the user U of the warning via a warning dialog 71 on the display 15. This prompts user U to check the settings and environment and to retake the image.

[0102] As described above, according to the medical support system 100 of this embodiment, the user U can obtain a group of images of the affected area (group of captured images) suitable for medical treatment, that is, a group of captured images suitable for confirming changes over time in the subject being photographed, without any special burden on the user U.

[0103] Furthermore, although this embodiment describes a medical support system 100, the present invention is applicable to other systems as well. For example, the subject is not limited to a human body, but may be a specific part of a building or device that deteriorates over time. The same subject may be photographed periodically, and if the difference in the feature quantities obtained by image processing exceeds a threshold compared to the previous photograph, a warning may be issued.

[0104] (Other embodiments) In this embodiment, the system can also be implemented by supplying a program that implements one or more functions to a computer of a system or device via a network or storage medium, and the system control unit of that system or device reads and executes the program. The system control unit has one or more processors or circuits and may include a plurality of separate system control units or a network of a plurality of separate processors or circuits in order to read and execute executable instructions.

[0105] A processor or circuit may include a central processing unit (CPU), a microprocessing unit (MPU), a graphics processing unit (GPU), an application-specific integrated circuit (ASIC), or a field-programmable gate array (FPGA). Alternatively, a processor or circuit may include a digital signal processor (DSP), a dataflow processor (DFP), or a neural processing unit (NPU).

[0106] Although preferred embodiments of the present invention have been described above, the present invention is not limited to these embodiments, and various modifications and changes are possible within the scope of its gist. [Explanation of Symbols]

[0107] 100 Medical Support Systems 1 Camera 2 servers 3 PC L identification tag U User P patient W Affected area 11 Controllers 12 Image Sensors 13 Button Groups 14 Touch panel 15 displays 16 storage 17 Zoom Lens 18 Shutter 19 Network Interfaces 21 CPU 22 GPU 23 memory 24 storage 25 Network Interfaces

Claims

1. A time-series change confirmation system comprising: an imaging device that photographs a target object multiple times at intervals according to the user's instructions and generates a group of captured images; a UI for the user to check the time-series changes of the target object; and an image processing device that performs image processing on each of the captured images for display on the UI, In the aforementioned image processing apparatus, Each time the imaging device generates one of the captured images in the group of captured images, an acquisition means acquires the captured image, the shooting parameters including the date and time of shooting, and the target identification information for identifying the target from the imaging device. A means for identifying the subject to be photographed from each of the group of photographed images using the subject identification information, A first calculation means that uses the aforementioned shooting parameters to calculate the characteristic quantities of the identified target for each of the group of captured images, A second calculation means compares the captured images generated during the current and previous captures by the aforementioned imaging device with the respective capture date and time information and the calculated feature quantities to calculate the difference in the feature quantities resulting from the changes over time. The system includes an issuing means for issuing a warning to the imaging device prompting the user to retake the image if it is determined that the captured images generated during the current and previous shooting sessions have the same acquired target identification information, but the difference in the feature quantities due to the changes over time is greater than or equal to a threshold. In the aforementioned imaging device, A time-series change monitoring system characterized by comprising a notification means for notifying the user of the warning when the warning is issued by the issuing means.

2. The notification means is a display means, The time-series change confirmation system according to claim 1, characterized in that the display means displays a warning dialog to notify the user of the warning when the warning is issued to the imaging device by the issuing means.

3. The aforementioned shooting parameters further include information on the zoom and autofocus of the imaging device, The identification means further estimates the region of the identified target from the captured image, The time-series change confirmation system according to claim 1 or 2, characterized in that the first calculation means calculates the actual area of ​​the estimated target area as a feature quantity using the zoom and autofocus information.

4. The shooting parameters further include distance map information, The identification means further estimates the region of the identified target from the captured image, The time-series change confirmation system according to claim 1 or 2, characterized in that the first calculation means calculates the actual area of ​​the estimated target area as a feature quantity using the information of the distance map.

5. The time-series change confirmation system according to any one of claims 1 to 4, characterized in that the threshold is changed according to the elapsed time from the previous shooting to the current shooting.

6. The aforementioned time-series change monitoring system is a system that supports the user in providing medical care for a patient's skin disease, The subject of the aforementioned photography is the affected area of ​​the patient as determined by the user. The aforementioned group of captured images is a group of images of the affected area, including the affected area. The time-series change confirmation system according to any one of claims 1 to 5, characterized in that the aforementioned target identification information is information about the patient and the affected area.

7. The time-dependent change monitoring system according to claim 6, wherein the threshold is changed according to the type of skin disease.

8. Claim 6 or 7 is the time-series change monitoring system according to the present invention, wherein the threshold is changed according to whether the progression of the skin disease is in the acute, chronic, or recovery phase.

9. The time-series change confirmation system according to claim 8, characterized in that the progression of the skin disease is determined according to the elapsed time from the previous imaging to the current imaging.

10. The time-series change confirmation system according to any one of claims 1 to 9, characterized in that the difference in the aforementioned feature quantities appears due to the difference in the shooting parameters during the current and previous shooting sessions.

11. The time-series change confirmation system according to any one of claims 1 to 10, characterized in that the difference in the aforementioned feature quantities appears due to the difference in the lighting environment during the current and previous shooting.

12. The imaging device is The system further comprises a transmission means that, each time one of the captured images in the group of captured images is generated, acquires the captured image, the shooting parameters, and the target identification information, and transmits them to the image processing device. The display means, after transmitting the captured image, the shooting parameters, and the target identification information generated during the current shooting using the transmission means, displays the warning dialog if it receives the warning from the image processing device. The time-series change confirmation system according to claim 2, characterized in that the warning dialog further notifies the user that there is a possibility that the shooting conditions are different between the current and previous shooting.

13. The time-series change confirmation system according to claim 12, characterized in that the warning dialog notifies that the different shooting conditions are the ambient light at the time of the current and previous shooting.

14. The time-series change confirmation system according to claim 12 or 13, characterized in that the warning dialog notifies that the different shooting conditions are the exposure settings used during the current and previous shooting.

15. A control method for a time-series change confirmation system comprising: an imaging device that photographs a target object multiple times at intervals according to user instructions and generates a group of captured images; a UI for the user to check the time-series changes of the target object; and an image processing device that performs image processing on each of the captured images for display on the UI, In the aforementioned image processing apparatus, Each time the imaging device generates one of the captured images in the group of captured images, an acquisition step is taken to acquire the captured image, the shooting parameters including the date and time information of the shooting, and the target identification information for identifying the target being photographed from the imaging device. A selection step in which the subject to be photographed is identified from each of the group of photographed images using the subject identification information, A first calculation step involves using the aforementioned shooting parameters to calculate the characteristic quantities of the identified target for each of the group of captured images, A second calculation step involves comparing the captured images generated during the current and previous captures by the aforementioned imaging device with the respective capture date and time information and the calculated feature quantities to calculate the difference in the feature quantities resulting from the changes over time. If it is determined that the captured images generated during the current and previous shooting sessions have the same acquired target identification information, but the difference in feature quantities due to the change over time exceeds a threshold, the following is an issuing step is performed to issue a warning to the imaging device prompting the user to retake the image: In the aforementioned imaging device, A control method characterized in that, if the warning is issued by the issuance step, a notification step is performed to notify the user of the warning.

16. A computer-executable program that causes a computer to function as one of the means of a time-series change confirmation system described in any one of claims 1 to 14.

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