Image processing device, medical camera device, and storage medium

Abstract

The present application can remove noise from images in which noise levels differ depending on regions within the images at low computational cost, and can perform high-quality processing corresponding to the preferences of an image interpreter. A plurality of image generators (3-1 to 3-N) receive measurement data or captured images obtained by an imaging device (1), and generate different images for the same imaging range. An image selection-synthesis section (5) selects different image regions from a plurality of images (4-1 to 4-N) generated by the image generators (3-1 to 3-N) in accordance with a predetermined region selection pattern, and generates one image (7) by synthesizing the images (5-1 to 5-N) of the selected image regions.

Description

Technical Field

[0001] This invention relates to an apparatus for generating medical images with reduced noise. Background Technology

[0002] In medical imaging devices such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound diagnostic equipment, if a long time is required during imaging to acquire the data (or signals) used to reconstruct the image, it can lead to adverse effects such as increased burden on the patient and the appearance of image artifacts caused by the patient's movements. Therefore, by focusing on imaging methods, high-speed imaging techniques to shorten imaging time have been developed for various diagnostic tools.

[0003] For example, in MRI devices, multiple receiving coils are used to downsample the k-space, thereby shortening the imaging time. High-speed imaging methods (e.g., parallel imaging) that reconstruct the image using calculations based on the sensitivity distribution of the receiving coils have been put into practical use. However, in high-speed imaging methods in MRI devices, less data is used in the image to achieve high speed, resulting in noise and degraded image quality. Furthermore, the noise distribution within the same imaging plane differs from that in the imaging space.

[0004] Furthermore, noise is particularly increased in ultrasonic imaging devices, especially in deep areas where sensitivity is insufficient.

[0005] Several image quality improvement techniques have been developed as a solution to this image quality degradation. For example, Patent Document 1 discloses a technique that, similar to parallel imaging in an MRI device, reduces the overall noise of a reconstructed image containing spatially varying (non-uniform) noise. Specifically, a noise map representing the variance of the noise contained in the reconstructed image is generated, and a locally adapted nonlinear noise filter is generated based on this noise map. The reconstructed image is then processed successively through the generated filter, thereby reducing non-uniform noise from the overall image.

[0006] Prior art literature

[0007] Patent documents

[0008] Patent Document 1: Japanese Patent Publication No. 2007-503903 Summary of the Invention

[0009] The problem that the invention aims to solve

[0010] In the image quality improvement technology of Patent Document 1, a noise map needs to be generated for each image that varies depending on the shooting conditions, the state of the subject, etc., and a nonlinear noise filter is generated based on the noise map. The nonlinear noise filter is then applied to the image sequentially. Therefore, the computational cost for filter generation increases. Furthermore, since the generated nonlinear noise filter is applied to the image sequentially, the computational cost increases even during filter processing. Consequently, the image observed by the image interpreter becomes an image with improved image quality through the generated filter, thus there is a problem that the adjustment of the filter itself may not reflect the image interpreter's preferences.

[0011] The purpose of this invention is to remove noise from images with different noise levels depending on the region within the image at low computational cost, and to perform high-quality enhancement according to the preferences of the image interpreter.

[0012] Technical solutions for solving the problem

[0013] To achieve the above objectives, according to the present invention, an image processing apparatus is provided, comprising: a plurality of image generators that receive measurement data or camera images obtained by a camera device and generate different images for the same camera range; and an image selection-compositing unit that selects different image regions from the plurality of images generated by the plurality of image generators according to a predetermined region selection pattern, and composites the images of the selected image regions to generate an image.

[0014] Invention Effects

[0015] According to the present invention, noise can be removed from images with different noise levels depending on the region within the image, at low computational cost. Furthermore, it becomes possible to achieve high-quality enhancement according to the preferences of the image interpreter. Attached Figure Description

[0016] Figure 1 This is a block diagram showing the structure of the medical camera device according to Embodiment 1.

[0017] Figure 2 This is a block diagram illustrating the structure and processing flow of the medical imaging device according to Embodiment 1.

[0018] Figure 3 This is an explanatory diagram showing the learning data of the image generator (learning model) of the medical camera device according to Embodiment 1.

[0019] Figure 4 This is an explanatory diagram showing a slider as an example of the receiving section in Embodiment 1.

[0020] Figure 5 This is a flowchart illustrating the operation of the image processing device of the medical camera device according to Embodiment 1.

[0021] Figure 6 This is a block diagram showing the structure of an MRI device as a medical imaging device according to Embodiment 1.

[0022] Figure 7 This is a block diagram showing the structure of the medical camera device according to Embodiment 2.

[0023] Figure 8 This is a block diagram illustrating the structure and processing flow of the medical imaging device according to Embodiment 2.

[0024] Figure 9 This is a block diagram showing the structure of an ultrasonic camera device as a camera device in Embodiment 2 of a medical camera device.

[0025] Explanation of reference numerals in the attached figures

[0026] 1: Camera device; 2: Image processing device; 3, 3-1 to 3-N: Image generator; 4: Image storage unit; 4-1 to 4-N: High-quality image; 5: Image selection-compositing unit; 6: Mode setting unit; 7: High-quality image; 40-1 to 40-N: Collection of images of small areas with a given noise level; 8: Mode storage unit; 9: Control unit; 10: User interface (UI); 11: Camera condition acceptance-mode selection unit; 70: Display device. Detailed Implementation

[0027] A medical imaging device according to one embodiment of the present invention will be described.

[0028] Implementation Method 1

[0029] The medical imaging device of Embodiment 1 includes an MRI device as a camera. (Usage) Figures 1-6 The medical imaging device of Embodiment 1 will be described.

[0030] like Figure 1 as well as Figure 2 As shown, the medical imaging device of Embodiment 1 includes an imaging device 1 and an image processing device 2. The imaging device 1 is an MRI device.

[0031] <Structure of Image Processing Device 2>

[0032] The image processing device 2 is configured to include multiple image generators 3-1 to 3-N, an image selection-compositing unit 5, an image storage unit 4, a mode setting unit 6, a receiving unit 10, and a control unit 9.

[0033] Image generators 3-1 to 3-N receive measurement data or captured images (referred to as original images) obtained by the camera device 1, and generate different images 4-1 to 4-N for the same camera range as the original images. Specifically, image generators 3-1 to 3-N are pre-configured to generate high-quality images 4-1 to 4-N from images with a given noise level.

[0034] For example, if the original image obtained by the imaging device 1 is an image captured and reconstructed through parallel imaging using a high-speed imaging method, and the noise level of the spatial region of the image is, for example, three different levels, then image generators 3-1 to 3-3 respectively generate images 4-1 to 4-3 of the highest quality with noise reduced in regions of predetermined noise levels. Specifically, for example, if image generator 3-1 is configured to correspond to noise level 1, which has the lowest noise level, then an image with the same imaging range as the original image is generated, and the noise in the region corresponding to noise level 1 in the original image is reduced to the lowest quality, then a high-quality image 4-1 is generated. In this case, in the high-quality image 4-1, the image quality of the regions corresponding to noise levels 2 and 3 in the original image is not improved to the level of the region corresponding to noise level 1.

[0035] Similarly, when image generator 3-2 is configured to correspond to noise level 2, it generates a high-quality image 4-2 with the same camera range as the original image and with the noise in the region corresponding to noise level 2 in the original image minimized. Furthermore, when image generator 3-3 is configured to correspond to noise level 3, which has the highest noise level, it generates a high-quality image 4-3 with the same camera range as the original image and with the noise in the region corresponding to noise level 3 in the original image minimized.

[0036] In the reconstruction processing of parallel imaging, the noise level of a spatial region of an image can be calculated based on the distribution information of the G-factor (G-factor mapping), which represents the amount of noise propagating. For example, the G-factor value of noise level 1 is below 1.1, noise level 2 is 1.1 to 1.2, noise level 3 is 1.2 to 2.0, and so on. Any range of G-factor values ​​can be used in the noise level classification.

[0037] Such image generators 3-1 to 3-N can be implemented using learned models (e.g., neural networks). Specifically, as... Figure 3As shown, the image obtained by the camera device 1 through high-speed imaging such as parallel imaging is segmented into tiny regions of predetermined size by a data classifier. As described above, the noise level of the tiny regions is calculated according to the G-factor mapping, and images 40-1 to 40-N are generated to extract and classify the tiny regions according to each noise level 1 to N. Images 40-1 to 40-N are sets of tiny region images with noise levels 1 to N. Images 40-1 to 40-N are used as input data to the learning model (image generators 3-1 to 3-N).

[0038] For example, image 40-1, a set of small regions with noise level 1, is used as input to the learning model of image generator 3-1. Similarly, image 40-2, a set of small regions with noise level 2, is used as input to the learning model of image generator 3-2. Image 40-3, a set of small regions with noise level 3, is used as input to the learning model of image generator 3-3.

[0039] On the other hand, the teacher data (forward solution data) used as the learning model are images obtained by a low-speed imaging method (e.g., a full-sampling imaging method) that obtains high-quality images in the imaging device 1.

[0040] Using these input data and teacher data, the learning model learns and the weights of the nodes within the neural network are set. This allows the generation of image generators 3-1 to 3-N.

[0041] The image generators 3-1 to 3-N generated in this way can generate images 4-1 to 4-N with different regions of the highest image quality by taking the same camera image as input.

[0042] The image storage unit 4 stores images 4-1 to 4-N generated by the image generators 3-1 to 3-N respectively.

[0043] The image selection-compositing unit 5 selects different image regions 5-1 to 5-M (here, M=3) from images 4-1 to 4-N according to the region selection mode set by the mode setting unit 6, and composites the images of the selected image regions 5-1 to 5-M to generate a high-quality image 7. The image regions 5-1 to 5-M selected by the image selection-compositing unit 5 are the regions with the highest image quality of the generated images 4-1 to 4-N from the image generators 3-1 to 3-3. Therefore, by selecting the regions with the highest image quality from the generated images 4-1 to 4-N and compositing them, the image selection-compositing unit 5 can generate an image 7 with high overall image quality. The image selection-compositing unit 5 displays the generated high-quality image 7 on the display device 70.

[0044] Thus, according to Embodiment 1, noise can be removed from MRI images containing noise of varying levels depending on the region with low computational cost.

[0045] Furthermore, the number M of regions selected by the image selection-compositing unit 5 is the number of noise level levels set in the region selection mode, and is the same as or less than N. The number M of selected regions can also be set to different numbers depending on the region selection mode.

[0046] The mode setting unit 6 includes a mode storage unit 8 that pre-stores various region selection modes. The mode setting unit 6 may also be configured to receive the G-factor mapping of the captured image (original image) from the imaging device 1, select the region selection mode corresponding to the G-factor mapping from the mode storage unit 8, and set it in the image selection-compositing unit 5.

[0047] Furthermore, the receiving unit 10 can also receive a selection instruction from the user's receiving area selection mode. In this case, the mode setting unit 6 receives the selection instruction via the control unit 9 and selects the indicated area selection mode. As a result, a high-quality image 7 that matches the user's preferences can be generated.

[0048] After the image selection-compositing unit 5 generates a high-quality image 7 according to the set region selection mode, and the receiving unit 10 selects the region selection mode from the user's received region selection mode and the mode setting unit 6 sets it in the image selection-compositing unit 5, the image selection-compositing unit 5 uses images 4-1 to 4-N stored in the image storage unit 4 to generate images according to the set region selection mode. Therefore, the image generators 3-1 to 3-N do not need to regenerate images 4-1 to 4-N from the captured images again, and can repeatedly generate high-quality images 7 according to different region selection modes.

[0049] In the image selection-compositing unit 5, a binarized mapping of each region selection mode is generated, and the image received from the image storage unit 4 is multiplied to generate image regions 5-1 to 5-3. By overlaying them with the same coordinates (adding them), an image with noise removed from the original image as a whole can be generated. At this time, due to differences in noise removal performance, unnatural discontinuities may occur in the image at the boundaries of image regions 5-1 to 5-3. To avoid this, the region selection modes are preset to overlap each other with a given width. In the image selection-compositing unit 5, in order to make the boundaries smooth during compositing, the images of each image region 5-1 to 5-3 are weighted in the overlapping area of ​​the given width, and the weighted image regions 5-1 to 5-3 are added together. In addition, the weighting function is preferably a function in which all the imaging areas of the subject are 1, and the raised cosine function, etc., can be used.

[0050] In addition, such as Figure 4 In this way, the receiving unit 10 can also have region selection modes C-1, C-2, and C-3 pre-established with corresponding sliders 31 for each sliding position. The mode setting unit 6 sets the region selection mode corresponding to the position of the slider 31 slid by the user in the image selection-compositing unit 5. Thus, the user slides the slider 31 and observes the image 7 displayed on the display device 70, stopping the slider 31 at the desired position, thereby enabling the display of the desired high-quality image 7.

[0051] In addition, such as Figure 4 As shown, the region selection mode corresponding to the position of each slider 31 can also be set so that the number of noise level levels (distinction level: for example, noise level 1 and 2) does not change based on the slider position, but only the shape of the region changes. In this case, as Figure 4 As shown, by means of the operation unit 32 which has the number of noise levels (divisions) that the user can select, multiple region selection modes can be selected by means of a slider 31.

[0052] However, this implementation is not limited to a method in which the number of noise level levels (discrimination points) does not change according to the sliding position, but can also establish a correspondence between the discrimination points and different modes according to the sliding position.

[0053] Furthermore, the user interface for selecting a region is not limited to a slider. Any operation unit that allows the user to adjust the degree of change based on their actions, such as a physical slider, a rotary knob, or a touch panel for selecting or inputting values, can be used.

[0054] <Operation of Image Processing Device 2>

[0055] Next, use Figure 5 The flowchart explains the operation of the image processing device 2.

[0056] Furthermore, the image processing device 2 is configured as a computer equipped with processors such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), and memory. The CPU reads and executes the program stored in memory, thereby implementing the functions of the image generators 3-1 to 3-N, the image selection and compositing unit 5, and the mode setting unit 6 through software. Alternatively, some or all of the image generators 3-1 to 3-N, the image selection and compositing unit 5, and the mode setting unit 6 can be implemented in hardware. For example, the circuit design can be performed using a custom IC such as an ASIC (Application Specific Integrated Circuit) or a programmable IC such as a FPGA (Field-Programmable Gate Array) to implement the functions of the image generators 3-1 to 3-N, the image selection and compositing unit 5, and the mode setting unit 6.

[0057] Image generator 3 receives the captured image from camera device 1 (step S501). Image generator 3 inputs the received captured image into image generators 3-1 to 3-N. Image generators 3-1 to 3-N generate high-quality images 4-1 to 4-N according to the corresponding noise level and store them in image storage unit 4 (step S502).

[0058] The mode setting unit 6 receives the G-factor mapping from the camera device 1 and selects the corresponding region selection mode, and sets the selected region selection mode in the image selection and compositing unit 5 (step S503).

[0059] The image selection-compositing unit 5 selects (extracts) regions of each noise level from the high-quality images 4-1 to 4-N corresponding to the noise level, according to the set region selection mode. Thus, different regions can be selected from the high-quality images 4-1 to 4-N respectively (step S504).

[0060] The image selection-compositing unit 5 composites the area selected in step S504 and generates a high-quality image 7 (step S505).

[0061] The image selection-compositing unit 5 displays the high-quality image generated in step S505 on the display device 70.

[0062] When the receiving unit 10 receives a request from the user to set (change) the region selection mode, the mode setting unit 6 returns to step S504 and selects a region according to the set (changed) region selection mode (step S507).

[0063] As described above, according to the present embodiment, noise can be removed from an image including noise with different levels according to regions at a low computational cost. Moreover, the region selection mode can be changed according to the preference of the user (image interpreter), and a high-quality image can be repeatedly generated.

[0064] <Overall Structure of MRI Device>

[0065] Next, Figure 6 The overall structure of the imaging device (MRI device) 1 according to the present embodiment will be described.

[0066] As Figure 6 shown, the MRI device 1 includes a static magnetic field magnet (static magnetic field generating unit) 110, a gradient magnetic field coil (gradient magnetic field generating unit) 131, a transmit RF coil 151, a receive RF coil 161, a gradient magnetic field power supply 132, a shim coil 121, a shim power supply 122, an RF magnetic field generator 152, a receiver 162, a magnetic coupling prevention circuit driving device 180, a computer (image reconstruction unit) 170, and a sequencer 140. Additionally, 102 is a workbench for placing the subject (subject) 103 at the imaging site in the imaging space.

[0067] The static magnetic field magnet 11 generates a static magnetic field in the imaging space. The static magnetic field magnet 110 can be a tunnel magnet that generates a static magnetic field in the horizontal direction through a solenoid coil, or a static magnetic field magnet 110 that generates a static magnetic field in the vertical direction can be used.

[0068] The gradient magnetic field coil 131 is connected to the gradient magnetic field power supply 132 and generates a gradient magnetic field in the imaging space. The shim coil 121 is connected to the shim power supply 122 to adjust the uniformity of the static magnetic field.

[0069] The transmit RF coil 151 is connected to the RF magnetic field generator 152 and irradiates (transmits) an RF magnetic field to the subject 103. The frequency of the RF magnetic field is set to the nuclear magnetic resonance frequency of the nuclei (such as protons) of the nuclide that excites the subject 103 to be imaged. As the transmit RF coil 151, any structure of coil can be used, for example, a birdcage-type RF coil can be used.

[0070] The receive RF coil 161 is connected to the receiver 162 and receives the nuclear magnetic resonance signal from the subject 103. Here, as the receive RF coil 161 according to the present embodiment, a multi-channel RF coil (array coil) including a plurality of coil units is used. Thereby, high-speed imaging can be performed by parallel imaging.

[0071] The sequencer 140 sends commands to the tilted magnetic field power supply 132 and the RF magnetic field generator 152, causing them to operate respectively. The commands are sent according to instructions from the computer 170. Furthermore, the sequencer 140 sets the magnetic resonance frequency for the receiver 162 as a reference for detection, according to instructions from the computer 170. Specifically, during imaging, according to commands from the sequencer 140, the tilted magnetic field and the RF magnetic field are irradiated onto the object under test 103 from the tilted magnetic field coil 131 and the transmitting RF coil 151 at given timings. The nuclear magnetic resonance signal generated by the object under test 103 is detected by the receiving RF coil 161 and detected in the receiver 162. Thus, an imaging pulse sequence implementing the given imaging method is executed.

[0072] The computer 170 controls the overall operation of the MRI device 1 and performs various signal processing. For example, it receives signals detected by the receiver 162 via an A / D conversion circuit (not shown) and performs signal processing such as image reconstruction.

[0073] The detected signal and measurement conditions can be stored in a storage medium as needed. Furthermore, the computer 170 sends commands to the sequencer 140, causing each device to operate at pre-programmed timings and intensities. Moreover, when it is necessary to adjust the static magnetic field uniformity, the computer 170 sends commands to the shimming power supply 122 via the sequencer 140, causing it to adjust the static magnetic field uniformity through the shimming coil 121.

[0074] In parallel imaging, phase encoding is extracted at equal intervals, alternating rows, to shorten the execution time of the imaging pulse sequence and enable high-speed imaging. During image reconstruction, sensitivity mapping of the array coils is used for image reconstruction.

[0075] Implementation Method 2

[0076] As an embodiment 2, the medical imaging device includes an ultrasonic camera as camera device 1. Figures 7-9 The medical imaging device of Embodiment 2 will be described.

[0077] <Structure of Image Processing Device 2>

[0078] like Figure 7 , Figure 8 As shown, the structure of image processing device 2 is the same as that of image processing device 2 in embodiment 1. Since the noise level of the ultrasonic imaging image is greater in the deeper region of the detected object, in the region selection mode, the region with noise level 1 is the shallowest region, and the mode selects the deeper region as the noise level increases.

[0079] Furthermore, the image processing apparatus 2 in Embodiment 2 includes a camera condition receiving-mode selection unit 11. The camera condition receiving-mode selection unit 11 receives camera conditions from the camera apparatus 1, selects a region selection mode suitable for the received camera conditions based on a predetermined relationship between the camera conditions and the type of region selection mode, and outputs it to the mode setting unit 6. The mode setting unit 6 sets the region selection mode received from the camera condition receiving-mode selection unit 11 in the image selection-compositing unit 5.

[0080] This allows for the setting of a region selection mode that is suitable for the camera conditions.

[0081] Furthermore, the camera condition reception-mode selection unit 11 can also be configured such that, in addition to storing the predetermined relationship between camera conditions and types of area selection modes, when the user selects an area selection mode from the reception unit 10, it also stores the relationship between the selected area selection mode and the current camera conditions. Therefore, when a camera image is subsequently received from the ultrasonic camera under the same camera conditions, the previously selected area selection mode can be selected, thus enabling the display of a high-quality image that matches the user's preferences.

[0082] The other structures, operations, and effects of the image processing device 2 are the same as those of Embodiment 1, so the description is omitted.

[0083] Furthermore, the camera condition acceptance-mode selection unit 11 of Embodiment 2 can also be configured in the image processing device 2 of Embodiment 1.

[0084] <Overall Structure of the Ultrasonic Camera Device>

[0085] Next, use Figure 9 The overall structure of the camera device (ultrasonic camera device) 1 in Embodiment 2 will be described.

[0086] The ultrasonic imaging device comprises a transmitting unit 211, a receiving unit 212, an image generating unit 213, and a transceiver separation unit 216. The transmitting unit 211 outputs a transmission signal to the ultrasonic probe 222 via the transceiver separation unit 216. Thus, the ultrasonic probe 222 transmits ultrasonic waves 223 to the object being inspected 220. The echo from the object being inspected 220 is received by the ultrasonic probe 222, which outputs a received signal. The receiving unit 212, which receives the received signal from the ultrasonic probe via the transceiver separation unit 216, performs beamforming on the received signal along a predetermined receiving scan line. The image generating unit 213 processes the beamformed received signal to generate an ultrasonic image. The image processing device 2 receives the image generated by the image generating unit 213 and processes it as a camera image.

[0087] exist Figure 9In this configuration, the image processing device 2 is positioned after the image generation unit 213, but time-series data prior to image generation can also be used as input to the image generators 1 to N. In the time-series data, since time corresponds to the depth of the non-detected object, noise levels are determined based on the time of the data.

Claims

1. An image processing apparatus characterized by comprising: having: a plurality of image generators that take in measurement data or captured images obtained by an imaging device and generate different images for the same imaging range; an image selection-synthesis section that selects different image regions in accordance with a predetermined region selection pattern and synthesizes the images of the selected image regions to generate one image from a plurality of images generated by the plurality of image generators; and a reception section that receives a selection instruction of the region selection pattern from a user, in a case where the reception section receives a change in the region selection pattern, the image selection-synthesis section selects and synthesizes regions of each noise level of the set region selection pattern, the plurality of image generators are respectively configured in advance to generate images from which noise is removed from images of different noise levels.

2. The image processing apparatus according to claim 1, wherein the measurement data or captured images obtained by the imaging device are different in noise level according to a spatial region, the region selection pattern corresponds to a pattern of the regions that are different in noise level.

3. The image processing apparatus according to claim 1, wherein the plurality of image generators are respectively a learned model that has been learned.

4. The image processing apparatus according to claim 3, wherein the learned model is a model that is learned in advance by taking in, as input data, an image in a region of a given noise level in a captured image that is obtained by a given imaging method by the imaging device and is different in noise level according to a spatial region, and taking in, as teacher data, a captured image that is obtained by the imaging device by an imaging method that reduces noise more than the imaging method.

5. The image processing apparatus according to claim 4, wherein the imaging method by which the image of the input data is obtained is a given high-speed imaging method, and the imaging method by which the image of the teacher data is obtained is a lower-speed imaging method than the high-speed imaging method.

6. The image processing apparatus according to claim 1, further having: a pattern storage section that stores a plurality of the predetermined region selection patterns used for selection by the image selection-synthesis section; and a pattern setting section that selects the region selection pattern from the pattern storage section and sets it in the image selection-synthesis section, the image selection-synthesis section generates an image in accordance with the region selection pattern set by the pattern setting section.

7. The image processing apparatus according to claim 6, further having: an image storage section that stores the images generated by the plurality of image generators.

8. The image processing apparatus according to claim 7, wherein ​ ​ After the image selection-synthesis section generates the one image in accordance with the region selection mode, the reception section receives selection of the region selection mode from the user, and the mode setting section sets the region selection mode in the image selection-synthesis section, the image selection-synthesis section generates an image in accordance with the set region selection mode using the image stored in the image storage section.

9. The image processing apparatus according to claim 1, wherein the region selection mode is set so that boundaries of the regions overlap each other with a given width, the image selection-synthesis section, when synthesizing the images of the selected image regions, weights and adds the images of the respective image regions in the regions where the boundaries overlap with the given width.

10. The image processing apparatus according to claim 7, wherein the reception section is capable of receiving a degree of change from the user in a manner, and the region selection mode is previously set in correspondence with the degree of change, the mode setting section sets the region selection mode in correspondence with the degree of change set by the user operating the reception section in the image selection-synthesis section.

11. The image processing apparatus according to claim 6, wherein the image processing apparatus further has: a photographing condition reception-mode selection section that receives a photographing condition from the image pickup apparatus, and selects a region selection mode corresponding to the received photographing condition according to a predetermined relationship between photographing conditions and region selection modes, the mode setting section sets the region selection mode selected by the photographing condition reception-mode selection section in the image selection-synthesis section.

12. The image processing apparatus according to claim 7, wherein the image processing apparatus further has: a photographing condition reception-mode selection section that receives a photographing condition from the image pickup apparatus, and stores the photographing condition in correspondence with the region selection mode selected by the user from the reception section, and next time, selects the stored region selection mode in the case where the photographing condition is set to the image pickup apparatus, the mode setting section sets the region selection mode selected by the photographing condition reception-mode selection section in the image selection-synthesis section.

13. A medical camera device, characterized by an image pickup apparatus; and the image processing apparatus according to any one of claims 1 to 12.

14. The medical image pickup apparatus according to claim 13, wherein the image pickup apparatus is a magnetic resonance imaging apparatus that has a static magnetic field generating section that applies a static magnetic field to an object, a gradient magnetic field generating section that applies a gradient magnetic field to the object, a transmission coil that irradiates a high-frequency magnetic field to the object, a reception coil that measures a nuclear magnetic resonance signal generated by the object, and an image reconstruction section that reconstructs an image from a signal measured by the reception coil, the image processing apparatus receives the signal measured by the reception coil or the image reconstructed by the image reconstruction section as the measurement data or the image pickup image. ​ 15. The medical imaging apparatus according to claim 14, wherein the magnetic resonance imaging apparatus reconstructs the image by parallel imaging, the region selection mode of the image processing apparatus sets the image region according to a G-factor map of the parallel imaging.

16. The medical imaging apparatus according to claim 13, wherein the imaging apparatus is an ultrasonic imaging apparatus including a transmission unit that transmits ultrasonic waves from an ultrasonic probe to an object to be examined, a reception unit that receives a reception signal from the ultrasonic probe that receives the ultrasonic waves from the object to be examined and performs reception beamforming, and an image generation unit that processes the reception signal after the reception beamforming and generates an image, the image processing apparatus receives the image generated by the image generation unit as the imaging image.

17. The medical imaging apparatus according to claim 16, wherein the region selection mode of the image processing apparatus sets a plurality of the image regions in a depth direction of the object to be examined.

18. A storage medium storing an image processing program, wherein the image processing program causes a computer to function as: a plurality of image generation units that receive measurement data or an imaging image obtained by an imaging apparatus and generate different images for the same imaging range, an image selection-combining unit that selects different image regions from a plurality of images generated by the plurality of image generation units in accordance with a predetermined region selection mode and combines the images of the selected image regions to generate one image, and a reception unit that receives a selection instruction of the region selection mode from a user, in a case where the reception unit receives a change of the region selection mode, the image selection-combining unit selects and combines regions of each noise level of the set region selection mode, the plurality of image generation units are respectively configured to generate an image from which noise is removed from an image of which the noise level is different. ​

Images