Medical image tuning method

Abstract

The invention relates to a medical image tuning method, and belongs to the technical field of image processing. According to the invention, a medical image tuning device is adopted; a core board fusion algorithm module automatically identifies the number and pixels of connected cameras, automatically matches a built-in image processing algorithm module, and performs preprocessing through an exposure algorithm module, a denoising algorithm module and a contrast enhancement algorithm module to obtain a single or multiple images as a state 1; extracting image features of the state 1, automatically selecting a single-image algorithm module or a multi-image fusion algorithm module, and obtaining a state 2 through the fusion algorithm module; low-delay and high-pixel transmission is met, image information is completely reserved, a camera host can better complete image processing, and the final image quality is guaranteed; the problem of image information loss caused by image processing at the camera module end is reduced, and the problems of module temperature rise, component damage, influence on imaging quality and the like caused by image processing at the camera module end are solved.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to a method for optimizing medical endoscope images. Background Art

[0002] An endoscope usually inserts a flexible insertion portion in a slender shape into the body to be examined, illuminates the illumination light supplied by a light source device from the front end of the insertion portion, and captures an in-vivo image by receiving the reflected light of the illumination light through an imaging end.

[0003] Published patent: Invention title, GPU-based real-time image processing method and system for endoscopes, Publication number: CN117593437A, Publication date: February 23, 2024. The system includes: a data acquisition module for acquiring real-time endoscope images and endoscope-related parameter information, where the related parameter information at least includes endoscope position, moving direction, moving speed, jitter offset value, and specification parameters, and the real-time endoscope images include a plurality of narrowband images; an image processing module for fusing the plurality of narrowband images to obtain first image information, and constructing a corresponding three-dimensional scene based on the first image information; a correction module for determining a target parameter evaluation value based on the related parameter information, and correcting the three-dimensional scene according to the target parameter evaluation value to obtain second image information; an information push module for, when it is detected that the second image information meets a preset standard, using the GPU to render the second image information and pushing the rendered second image information to a display terminal.

[0004] The technical deficiencies of the above published patent are: image processing is not performed simultaneously, and the first image and the second image are obtained sequentially; the system can only be used for endoscope image systems; Published patent: Invention title: A 3D fluorescence endoscope, imaging method, device and debugging Method, Publication Number: CN117398043A, Publication Date of Application: January 16, 2024. Disclosed is a 3D fluorescence endoscope, an imaging method, a device, and a debugging method, belonging to the field of endoscopes. The endoscope includes an endoscope sleeve, an objective lens and a photosensitive element arranged inside the endoscope sleeve. A beam splitter is arranged between the objective lens and the photosensitive element. The beam splitter divides the light beam passing through the objective lens into a left visible light beam, a left fluorescence beam, a right visible light beam, and a right fluorescence beam that are incident on the photosensitive element. A left focusing lens that makes the optical paths of the left visible light beam and the left fluorescence beam consistent and a right focusing lens that makes the optical paths of the right visible light beam and the right fluorescence beam consistent are arranged between the beam splitter and the photosensitive element. This endoscope can simultaneously image the four beams of light on the photosensitive element, with no time difference between the left and right images, and no time difference between the visible light image and the fluorescence image. The entire endoscope only uses one photosensitive element, and can achieve consistency in time and focal plane in a relatively small space, which is conducive to the miniaturization and high-sensitivity display of the 3D fluorescence endoscope. The unknown technical points of the above-mentioned disclosed patent are: there are a total of 4 photosensitive elements, and the image obtained by the light reflected twice on the photosensitive element is the mirror image of the real object, and the direction is the same as the light directly passing through the dichroic mirror 21, which is convenient for image processing operations during imaging; this system can only be used for 3D imaging endoscope systems; it does not involve image optimization and only processes image signals.

[0005] It is very necessary to research and develop a method for optimizing medical endoscope images to improve image quality and accuracy. Summary of the Invention

[0006] To overcome the deficiencies of the existing medical endoscope imaging technology, a medical image optimization method is invented.

[0007] A medical image optimization method uses a medical image optimization device; The medical image optimization device includes an illumination element, and also includes a camera front end, a camera host, and a backend device; The camera front end includes: an optical rigid endoscope, a camera, an optical bayonet, a filter, a photosensitive element, a gyroscope, a keypad, a camera main board, a serial board, and a camera cable; The gyroscope is connected to the keypad; the keypad is connected to the camera main board; The filter is arranged in front of the photosensitive element; the gyroscope is built into the camera keypad; The filter filters out interfering miscellaneous colors and transmits the filtered image to the photosensitive element; The photosensitive element transmits image data to the input interface of the camera host through the camera cable, and the gyroscope also transmits status information to the camera host. The camera host synthesizes the gyroscope information and processes the image; The described camera host includes: a main board, a video output board, a core board, a hard disk, a deserialization board, a touch screen, a host power supply, a fan, a video expansion board, a network port, a USB port, an RS232 port, and a signal output port; The described backend device includes a display, a signal connection cable, a signal input interface, or a remote receiving device; The described camera cable refers to a GMSL3 data cable; The described photosensitive element is used to collect the images obtained by the camera; The described main board includes: a core board; the core board includes a fusion algorithm module, and the fusion algorithm module includes an acquisition module, a DPC module, a BLC module, a WB module, a Demosaic module, a Gamma correct module, a CCM module, a Contrast / Brightness Enhancement module, an NR module, an Edge Enhancement module, an output module, an AE Stat module, and an AE Control module; the main board is respectively connected to the touch screen, the host power supply, the fan, the video expansion board, the signal output interface, or other host interfaces; The described output module includes a video output board, a video expansion board, and a signal output terminal; the signal output terminal includes a signal output port, a network port, a USB port, and an RS232 port; The main board is respectively connected to the touch screen, the video expansion board, and the signal output terminal; The hardware connection relationship of the described medical image optimization device is as follows: the lens of the optical rigid endoscope collects the image information of the target area, the optical rigid endoscope is adaptively connected to the optical bayonet, and the collected image information is optically transmitted to the filter, and the photosensitive element is connected to the camera main board; the gyroscope is connected to the button board; the button board is connected to the camera main board; the camera main board is connected to the serial board; the serial board is connected to the deserialization board through the camera cable, and the deserialization board is connected to the core board, and the core board is simultaneously connected to the hard disk and the video output board; the video output board is connected to the video expansion board, the video expansion board is connected to the signal output port, and the signal output port is connected to the signal input interface on the display.

[0008] The image data transmission relationship of the medical image optimization method is as follows: The camera converts the collected optical signal into an electrical signal through a photosensitive element and transmits it to the camera main board. The gyroscope collects the status information of the photosensitive element and outputs it to the camera main board. The serial board converts the data of the camera main board into serial data and transmits it to the deserialization board at high speed through a camera cable. The deserialization board converts the serial data into parallel data and inputs it into the camera host. The camera host processes the image by integrating the gyroscope information. The fusion algorithm module set on the core board of the camera host reads the number of connected cameras and pixels, automatically matches the built-in image processing algorithm module, and performs preprocessing through the exposure algorithm module, noise reduction algorithm module, and contrast enhancement algorithm module to obtain a single or multiple images, which is regarded as State 1. Extract the image features of State 1, automatically select a single-image algorithm module or a multi-image fusion algorithm module, and obtain State 2 through the fusion algorithm module. Distribute the images in State 2, identify them as single images, and after processing, respectively output the spectrally filtered images and normal images. The core board is connected to the image ISP processing module, the ISP processing module is connected to the host main board, the host main board outputs the image data to the signal output port, the signal output port outputs to the display, and the display shows the optimized image, or transmits it to a remote device through a wired or wireless network via the signal output port.

[0009] The described camera captures images of a single channel, or a dual channel, or a 4-channel, or a 6-channel camera; A single-channel camera refers to one camera sensor chip; A dual-channel camera refers to a dual-channel camera sensor chip; A 4-channel camera refers to a 4-channel camera sensor chip; A 6-channel camera refers to a 6-channel sensor chip; The described cameras are divided into left cameras and right cameras.

[0010] The described optical filter is used to filter stray light and only allows light with a wavelength of 400 - 650 nm to enter the photosensitive element.

[0011] The described photosensitive element is any one of a CMOS, a CCD, or an infrared sensor chip.

[0012] The described image information includes any one of the original medical image data of a medical endoscope, ultrasound, X-ray machine, CT / ET.

[0013] The described camera refers to reading the original medical image data of 1 channel, or 2 channels, or 4 channels, or 6 channels with a resolution of 4K and a frame rate of 60 frames.

[0014] The method steps of medical image optimization are as follows: Step 1: Set the optical filter in front of the photosensitive element to filter stray light and avoid image interference; Step 2: In order to ensure the integrity and high pixel of the image information, after the photosensitive element captures the image, the parallel data will be serially processed through the serial board to achieve high-speed transmission; Step 3: Adopt the GMSL3 transmission mode to obtain a transmission rate of 6 Gbps. After sending the image to the deserialization board, convert the serial signal into a parallel signal; and transmit the parallel signal to the core board; During this process, the gyroscope transmits the position information of the photosensitive element to the core board in real time to correct the image; Step 4: The fusion algorithm module on the core board automatically identifies the number and pixels of the connected cameras, automatically matches the built-in image processing algorithm module, and performs preprocessing, color restoration, image fusion, etc. through the exposure algorithm module, noise reduction algorithm module, and contrast enhancement algorithm module to obtain a single or multiple images as State 1; Step 5: Extract the image features of State 1, automatically select the single-image algorithm module or the multi-image fusion algorithm module, and obtain State 2 through the fusion algorithm module; Step 6: Distribute the State 2 image, identify it as a single image or multiple images, and respectively output the spectrally filtered image and the normal image after processing; Step 7: The image ISP processing module continues to process the single image or multiple images; Step 8: The core board transmits the processed image signal to the signal output port, outputs the processed video signal to the display, and displays the camera image in real time; Step 9: According to the instructions issued by the serial port control, the video signal processed by the core board can be stored on the hard disk or transmitted through the network port.

[0015] The fusion algorithm module in the above Step 4 includes the following algorithms: The formula of the BLC algorithm is:

[0016] The formula of the WB algorithm is:

[0017] The formula of the Demosaic algorithm is:

[0018] The formula of the Gamma Correction algorithm is:

[0019] The formula of the CCM algorithm is:

[0020] The formula of the brightness adjustment algorithm is: Brightness adjustment:

[0021] The formula of the contrast adjustment algorithm is as follows: Contrast adjustment:

[0022] The formula of the NR algorithm is as follows: Gaussian filtering:

[0023] Median filtering: , where S is a neighborhood window defined around (x, y), such as an n*n window, (i, j) represents the offset relative to (x, y) within the window, and median represents the operation of taking the median value; The formula of the Edge Enhancement algorithm:

[0024] The described Output: Conversion from RGB image format to YUV420 output; The formula of the Gamma Correction algorithm is as follows:

[0025] The formula of the Output algorithm is as follows: .

[0026] Noun definitions and explanations in this patent: Optical rigid endoscope: Briefly referred to as a lens, there is a type of lens used in endoscopes called an optical rigid endoscope. An optical rigid endoscope is a type of endoscope, characterized in that its optical components are made of columnar glass, the outer tube is a metal structure, and the outer tube is non-bendable. It mainly consists of three parts: a mechanical system, an optical system, and a light guide system.

[0027] Gyroscope: A device that realizes angular velocity detection based on the inertial principle is called a gyroscope; in the prior art device, bmi088, the position information of the photosensitive element is clearly defined.

[0028] GMSL3: High-speed data transmission reaches 6 Gbps.

[0029] MIPI is short for Mobile Industry Processor Interface, developed by the MIPI Alliance. It is a high-performance, low-power, and low-cost serial communication interface, aiming to standardize the interfaces inside the device, such as the camera, display interface, radio frequency / baseband interface, etc., so as to reduce the complexity of device design and increase design flexibility.

[0030] Sensor: The photosensitive element is a type of sensor; photosensitive elements are mainly used in endoscopes. A photosensitive element (such as a CCD or CMOS chip) is actually a special sensor dedicated to converting optical signals into electrical signals.

[0031] Sensor input: The sensor captures an image and inputs it into the ISP pipeline.

[0032] Image sensor: Used to capture images, with a pixel count reaching 4K.

[0033] Filter: Used to filter out stray light and ensure that natural light in the range of 400 - 650 nm enters the photosensitive element.

[0034] Chip used in the serial board: MAX96789.

[0035] Chip used in the deserialization board: MAX96752.

[0036] 4K medical endoscope: A imaging system in which the sensor captures image signals and outputs them in real time to a 4K monitor for medical staff to view during surgery.

[0037] The Image ISP (Image Signal Processor) processing module is a dedicated processor or hardware module for real-time processing and optimization of image or video signals.

[0038] DPC (Dead Pixel Correction): Image dead pixel detection: Detect dead pixels existing in the sensor and correct these dead pixels. Dead pixels are white dots in the output image in a completely black environment and black dots in the output image in a highly illuminated environment.

[0039] BLC (Black Level Compensation): Black level compensation: From the characteristics of the sensor, the lowest output voltage of the sensor is the black level voltage. By calibrating the black level, the influence of the black level on the image is eliminated.

[0040] WB (White Balance): White balance: Correct color deviations that occur in the sensor under different color temperatures. Calculate the RGB three-channel gain values through a white cardboard to complete white balance correction.

[0041] Demosaic: Debayering: The image captured by a single CMOS only contains one of the RGB colors. Interpolation processing is used to restore the other missing colors of the image.

[0042] Gamma Correction: Also known as gamma correction or gamma non - linearization, it is a non - linear operation or inverse operation technology used to adjust image or video signals. It is used to encode and decode linear luminance or RGB values to match the non - linear characteristics of display devices. In addition, gamma correction can expand or compress the dynamic range of images.

[0043] CCM (Color Correction Matrix): Color correction matrix: Due to the non - ideal spectral response of the sensor and the different spectral distributions of ambient light sources, there will be a large color difference between the sensor image and the actual scene. By calculating the color correction matrix, the image color can be adjusted to make the image color closer to the actual color.

[0044] Contrast / Brightness Enhancement: By enhancing the image contrast and brightness, the brightness distribution of the image under non - uniform illumination becomes more uniform, and the image has a stronger sense of transparency.

[0045] NR (Noise Reduction): Noise reduction: Reduce image noise through Gaussian, median and other low - pass filters.

[0046] Edge Enhancement: Edge contour enhancement: Use edge detection technology to extract image edges, enhance the image edges, and improve the image sharpness.

[0047] Gamma Correction: Also known as gamma correction or gamma non - linearization, it is a non - linear operation or inverse operation technology used to adjust image or video signals. It is used to encode and decode linear luminance or RGB values to match the non - linear characteristics of display devices. In addition, gamma correction can expand or compress the dynamic range of images.

[0048] Output: Convert the RGB image format to YUV420 output.

[0049] AE Stat: Automatic exposure information statistics: Statistic information such as image luminance, variance, histogram, etc., to evaluate whether the current image is underexposed or overexposed.

[0050] AE Control: Evaluate the current exposure situation according to the exposure statistical information, calculate the exposure parameters of the next frame, and send the exposure parameters of the next frame back to the sensor to complete automatic exposure control.

[0051] ‌Contrast Enhancement‌ refers to adjusting the contrast of an image to make its details clearer and enhance the visual effect of the image. Contrast refers to the degree of difference between the brightest and darkest parts of an image. By increasing this difference, the details of the image can be made more prominent.

[0052] Brightness Enhancement adjusts the brightness level of an image to make it brighter or darker. Brightness refers to the average light intensity of an image. By adjusting the brightness, the light and dark levels of the image can be changed, thus affecting the visual effect.

[0053] CCD: Charge Coupled Device, a semiconductor device used for image acquisition.

[0054] CMOS: Abbreviation for Complementary Metal Oxide Semiconductor; it is an integrated technology that fabricates components such as transistors, resistors, capacitors, and diodes on the same silicon wafer using standard processes. CMOS chips have advantages such as low power consumption, high integration, and strong anti-interference ability, and are widely used in fields such as microprocessors, digital signal processors, memories, and image sensors.

[0055] GMSL3: The third-generation Gigabit Multimedia Serial Link technology; High data transfer rate: GMSL3 supports a data transfer rate of up to 12 Gbps, meeting the bandwidth requirements of high-definition cameras and other high-speed data transfer devices; Low latency: Achieves low-latency video data transfer, ensuring the real-time performance of the system.

[0056] Core board: Uses a customized NVIDIA AGX Orin 64G as the core board to process the image information received by the camera.

[0057] The significant progress and creative technical features of the present invention are: The technical route adopted is: The filter filters out interfering miscellaneous colors, allowing only light with a wavelength of 400 - 650 nm to pass through and transmit it to the photosensitive element; The photosensitive element transmits the image data to the input interface of the camera host through the GMSL3 data cable, and the gyroscope also transmits the status information to the backend at high speed. The host synthesizes the gyroscope information, processes and fuses the image to obtain an ideal medical image; Other patents do not specify the use of a filter, which may cause the image quality to be affected by stray light. Low-latency endoscope camera module: Captures high-pixel images up to 3840*2160@60Hz and transmits the images to the backend without processing; mainly to overcome the problem of image information loss caused by image processing at the camera module end, and to overcome the problems such as increased module temperature, component damage, and impact on imaging quality that may also be caused by image processing at the camera module end. Technical Objectives Achieved: The main objective is to meet the requirements of low-latency and high-pixel transmission, while ensuring the complete preservation of image information. A powerful host with excellent backend performance can better process the images, thereby guaranteeing the final image quality. Other patents perform image processing at the front end of the camera, which may lead to the loss of image information. This patent adopts a backend processing method to preserve the image information; Technical Effects Achieved: The optimized video data is transmitted to the display screen for real-time display and playback for medical staff to view. The optimized video data can also be processed in the GPU chip processor, and the processed results can be superimposed on the optimized video data and transmitted to the display screen for real-time display; The optimized video data can also be copied to an external storage device, such as a USB flash drive, for backup or transmitted over the network to a remote location. Description of the Drawings

[0058] Figure 1 is a schematic structural diagram of the present invention.

[0059] Figure 2 is Figure 1 a partial enlarged view of A in

[0060] Figure 3 is Figure 1 a partial enlarged view of B in

[0061] Figure 4 is a schematic structural diagram of the optical rigid endoscope to the host in the present invention.

[0062] Figure 5 is a schematic module structure diagram of the present invention.

[0063] Figure 6 is a schematic module flowchart diagram of the present invention.

[0064] Figure 7 is a processing flowchart of image state 1 to image state 2 in the present invention.

[0065] Figure 8 is a gamma correction diagram of the image algorithm in the present invention.

[0066] In the figure: 4K camera system 1, optical rigid endoscope 1-1, camera 1-2, optical bayonet 1-2-1, filter 1-2-2, photosensitive element 1-2-3, gyroscope 1-2-4, keypad 1-2-5, camera main board 1-2-6, serial board 1-2-7, camera cable 1-3, camera host 1-4, host main board 1-4-1, video output board 1-4-1-1, core board 1-4-1-2, hard disk 1-4-1-3, deserialization board 1-4-1-4, touch screen 1-4-2, host power supply 1-4-3, video expansion board 1-4-4, network port 1-4-5, USB port 1-4-6, RS232 port 1-4-7, signal output port 1-4-8, display 1-5, signal connection cable 1-5-1, signal input interface 1-5-2. Detailed implementation mode

[0067] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0068] Embodiment 1: A medical image optimization method, using a medical image optimization device; The method steps of medical image optimization are: Step 1: Set the filter 1-2-2 in front of the photosensitive element 1-2-3 to filter out stray light and avoid image interference; Step 2: In order to ensure the integrity and high pixel of the image information, after the photosensitive element 1-2-3 captures the image, the parallel data will be serially processed through the serial board 1-2-7 to achieve high-speed transmission; Step 3: Adopt the GMSL3 transmission method to obtain a transmission rate of 6Gbps. After sending the image to the deserialization board 1-4-1-4, convert the serial signal into a parallel signal; and transmit the parallel signal to the core board 1-4-1-2; In this process, the gyroscope 1-4-1-4 transmits the position information of the photosensitive element 1-2-3 to the core board 1-4-1-2 in real time to correct the image; Step 4: The core board fusion algorithm module automatically identifies the number and pixels of the connected cameras, automatically matches the built-in image processing algorithm module, and performs preprocessing, color restoration, image fusion, etc. through the exposure algorithm module, noise reduction algorithm module, and contrast enhancement algorithm module to obtain a single or multiple images as state 1; Step 5: Extract the image features of State 1, automatically select a single-image algorithm module or a multi-image fusion algorithm module, and obtain State 2 through the fusion algorithm module; Step 6: Distribute the State 2 image, identify it as a single image or multiple images, and respectively output the spectrally filtered image and the normal image after processing; Step 7: The image ISP processing module continues to process the single image or multiple images; Step 8: The core board 1-4-1-2 transmits the processed image signal to the signal output port 1-4-8, outputs the processed video signal to the monitor 1-5, and displays the camera image in real time; Step 9: According to the instructions issued by the serial port control, the video signal processed by the core board 1-4-1-2 can be stored on the hard disk or transmitted through the network port 1-4-5.

[0069] The fusion algorithm module in the above-mentioned Step 4 includes the following algorithms: The formula of the BLC algorithm is:

[0070] The formula of the WB algorithm is:

[0071] The formula of the Demosaic algorithm is:

[0072] The formula of the Gamma Correction algorithm is:

[0073] The formula of the CCM algorithm is:

[0074] The formula of the algorithm is: Brightness adjustment:

[0075] Contrast adjustment:

[0076] The formula of the NR algorithm is: Gaussian filtering:

[0077] Median filtering: , where S is a neighborhood window defined around (x, y), such as an n*n window, (i, j) represents the offset relative to (x, y) within the window, and median represents the median operation; The formula of the Edge Enhancement algorithm is as follows: The described Output: The RGB image format is converted to YUV420 for output; The formula of the Gamma Correction algorithm is:

[0078] The formula of the described Output algorithm is: .

[0079] The specific embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the scope of knowledge possessed by those of ordinary skill in the art.

Claims

1. A medical image tuning method, using a medical image tuning device, characterized in that: The method steps of medical image tuning are: Step 1: Place the filter in front of the photosensitive element to filter stray light and avoid image interference; Step 2: In order to ensure the integrity of image information and high pixel, after the photosensitive element collects the image, the parallel data will be processed serially through the serial board to achieve high-speed transmission; Step 3: Use GMSL3 transmission mode to obtain a transmission rate of 6Gbps. After sending the image to the deserialization board, convert the serial signal into a parallel signal; and transmit the parallel signal to the core board; In this process, the gyroscope transmits the position information of the photosensitive element to the core board in real time to correct the image; Step 4: The core board fusion algorithm module automatically identifies the number and pixels of the connected cameras, automatically matches the built-in image processing algorithm module, and performs preprocessing, color restoration, image fusion and other processing through the exposure algorithm module, denoising algorithm module, and contrast enhancement algorithm module to obtain a single or multiple images as state 1; Step 5: Extract the image features of state 1, automatically select a single-image algorithm module, or a multi-image fusion algorithm module, and obtain state 2 through the fusion algorithm module; Step 6: Distribute the state 2 image and identify it as a single image or multiple images, and output the spectrally filtered image and the normal image respectively after processing; Step 7: The image ISP processing module continues to process the single image or multiple images; Step 8: The core board transmits the processed image signal to the signal output port, outputs the processed video signal to the display, and displays the camera image in real time; Step 9: According to the instructions sent by the serial port control, the video signal processed by the core board can be stored on the hard disk or transmitted through the network port; The fusion algorithm module in step 4 includes the following algorithms: The BLC algorithm formula is: The WB algorithm formula is: The Demosaic algorithm formula is: The Gamma Correction algorithm formula is: The CCM algorithm formula is: The brightness adjustment algorithm formula is: Brightness adjustment: The contrast adjustment algorithm formula is: Contrast adjustment: The NR algorithm formula is: Gaussian filtering: Median filter: , where S is a neighborhood window defined around (x, y), such as an n*n window, (i, j) represents the offset relative to (x, y) within the window, and median represents the median operation; The Gamma Correction algorithm formula is: The Edge Enhancement algorithm formula is: The Output: RGB image format is converted to YUV420 output; The Output algorithm formula is: 。

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