CN113283552A - Image classification method and device, storage medium and electronic equipment - Google Patents
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Abstract
The application discloses a method and a device for classifying images, a storage medium and electronic equipment. The image classification method comprises the steps of obtaining a medical image to be processed; preprocessing the medical image to be processed to obtain a processed medical image; acquiring feature points of the processed medical image, and comparing the processed medical image with the reference medical image based on the feature points to obtain a comparison result; determining the category of the medical image according to the comparison result; classifying the medical image based on the category to which the medical image belongs. The scheme can improve the classification efficiency of the medical images.
Description
Technical Field
The present application relates to the field of image data processing technologies, and in particular, to a method and an apparatus for classifying images, a storage medium, and an electronic device.
Background
Medical imaging is currently widely used as an important reference for medical diagnosis. For example, molybdenum target imaging for breast examinations, DR (direct Digital flat panel X-ray imaging system) imaging for breast examinations, and the like.
Generally, medical images obtained by these imaging methods need to be screened and classified by doctors in person, and the efficiency is low.
Disclosure of Invention
The embodiment of the application provides an image classification method, an image classification device, a storage medium and electronic equipment, which can improve the classification efficiency of medical images.
In a first aspect, an embodiment of the present application provides a method for classifying an image, including:
acquiring a medical image to be processed;
preprocessing the medical image to be processed to obtain a processed medical image;
acquiring feature points of the processed medical image, and comparing the processed medical image with the reference medical image based on the feature points to obtain a comparison result;
determining the category of the medical image according to the comparison result;
classifying the medical image based on the category to which the medical image belongs.
In the method for classifying images provided in the embodiment of the present application, the comparing the processed medical image and the reference medical image based on the feature point to obtain a comparison result includes:
triangulation is carried out on the characteristic points by utilizing a triangulation technology to obtain a first triangulation image;
and comparing the first triangulation image with the reference medical image to obtain a comparison result.
In the method for classifying an image provided by the embodiment of the present application, the comparing the first triangulation image with the reference medical image to obtain a comparison result includes:
acquiring a second triangulation image of the reference medical image;
and comparing the first triangular split image with the second triangular split image to obtain a comparison result.
In the image classification method provided in the embodiment of the present application, the preprocessing the medical image to be processed to obtain a processed medical image includes:
and carrying out sharpening processing, edge compensation processing or noise reduction processing on the medical image to be processed.
In the image classification method provided by the embodiment of the application, the sharpening process includes a non-linear transformation or a linear transformation; the method adopted by the noise reduction processing comprises a Gaussian filtering method, a median filtering method or a high-low pass filtering method.
In a second aspect, an embodiment of the present application provides an apparatus for classifying an image, including:
an image acquisition unit for acquiring a medical image to be processed;
the image processing unit is used for preprocessing the medical image to be processed to obtain a processed medical image;
the image comparison unit is used for acquiring the feature points of the processed medical image, and comparing the processed medical image with the reference medical image based on the feature points to obtain a comparison result;
the category determining unit is used for determining the category of the medical image according to the comparison result;
and the image classification unit is used for classifying the medical image based on the category of the medical image.
In the image classification device provided in the embodiment of the present application, the image comparison unit is configured to:
acquiring characteristic points of the processed medical image;
triangulation is carried out on the characteristic points by utilizing a triangulation technology to obtain a first triangulation image;
and comparing the first triangulation image with the reference medical image to obtain a comparison result.
In the apparatus for classifying an image provided in the embodiment of the present application, the image processing unit is specifically configured to:
and carrying out sharpening processing, edge compensation processing or noise reduction processing on the medical image to be processed to obtain a processed medical image.
In a third aspect, an embodiment of the present application provides a storage medium, where a plurality of instructions are stored, and the instructions are suitable for being loaded by a processor to perform the method described in the foregoing embodiment.
In a fourth aspect, the present application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to the foregoing embodiments.
The image classification method provided by the embodiment of the application acquires a medical image to be processed; preprocessing the medical image to be processed to obtain a processed medical image; comparing the processed medical image with a reference medical image to obtain a comparison result; determining the category of the medical image according to the comparison result; classifying the medical image based on the category to which the medical image belongs. The scheme can improve the classification efficiency of the medical images.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flowchart of a classification method for an image according to an embodiment of the present application.
Fig. 2 is a schematic structural diagram of an image classification apparatus according to an embodiment of the present application.
Fig. 3 is a schematic structural diagram of a server according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first" and "second", etc. in this application are used to distinguish between different objects and not to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or modules is not limited to the listed steps or modules but may alternatively include other steps or modules not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The embodiment of the application provides an image classification method and device, a storage medium and electronic equipment. The image classification method provided by the embodiment of the present application may be integrated into an image classification device.
The following detailed description will be made separately, and the description sequence of each embodiment below does not limit the specific implementation sequence.
101. A medical image to be processed is acquired.
In the classification of the image, a medical image to be processed may be obtained first, and the medical image to be processed may be an image obtained by a medical Imaging system, for example, a PET (Positron Emission Tomography) image, a CT (Computed Tomography) image, an MRI (Magnetic Resonance Imaging) image, or the like, and the type is not particularly limited herein. In acquiring the medical image, the medical image obtained by real-time scanning by the medical imaging system may be acquired directly, or the generated medical image stored in a server, a storage medium, or the like may be acquired by a network access, data reading, or the like.
It is understood that a medical image does not only refer to a single image, but also refers to an image set, for example, an image set composed of a plurality of medical images with different imaging scan parameters (different scan sequences, different modalities, different shooting positions, etc.) corresponding to the same object under examination.
102. And preprocessing the medical image to be processed to obtain a processed medical image.
Specifically, the medical image to be processed may be subjected to sharpening processing, edge compensation processing, or noise reduction processing.
In some embodiments, the sharpening process may include a non-linear transform or a histogram equalization transform, or the like, to enhance the local or global contrast of the medical image to be processed.
In some embodiments, the noise reduction process may employ a method including gaussian filtering, median filtering, or high-low pass filtering.
Specifically, the gaussian filtering method may refer to a process of performing weighted average on pixel values of the whole medical image to be processed, and for the pixel value of each pixel point, the pixel value of the pixel point and other pixel values in the neighborhood may be obtained by weighted average. The median filtering method may be to set the gray value of each pixel point in the medical image to be processed as the median of the gray values of all pixel points in a neighborhood window of the pixel point. The high-low pass filtering method may refer to including at least one of high-pass filtering and low-pass filtering. Here, the high-pass filtering may refer to removing high-frequency components in the medical image to be processed, leaving low-frequency components. Low-pass filtering may refer to removing low-frequency components from the medical image to be processed, leaving high-frequency components. The high-frequency component may refer to a portion of the medical image to be processed where intensity (brightness/gradation) changes relatively gently. The low frequency component may refer to a portion of the medical image to be processed where intensity (brightness/gray scale) changes relatively strongly.
Wherein the edge compensation process may enhance the contrast of the edges of the medical image to be processed.
103. And acquiring the characteristic points of the processed medical image, and comparing the processed medical image with the reference medical image based on the characteristic points to obtain a comparison result.
Specifically, triangulation technology can be used for triangulating the feature points to obtain the first triangulation image; and then comparing the first triangulation image with the reference medical image to obtain a comparison result.
It will be appreciated that prior to comparison, triangulation of the reference medical images is required. That is, the step of comparing the first triangulation image with the reference medical image to obtain the comparison result may include:
acquiring a second triangulation image of the reference medical image;
and comparing the first triangular split image with the second triangular split image to obtain a comparison result.
It should be noted that the reference medical image may include, but is not limited to, one type, and the reference medical image may include reference medical images of various diseases.
Wherein, when comparing the processed medical image with the reference medical image, the processed medical image can be matched with the reference medical images of various diseases according to the similarity.
Specifically, the Similarity matching processing results of the medical image and the reference medical image can be obtained by a Similarity network model (Similarity Learning architecture Or Models). The similarity network model is a model which can be used for detecting the similarity of two or more things, in the step, the similarity network model is specifically used for carrying out similarity matching processing on the medical image and the reference medical image, and the corresponding matching processing results comprise matching between the medical image and the reference medical image, mismatching between the medical image and the reference medical image and the like.
104. And determining the category of the medical image according to the comparison result.
Specifically, the reference medical image corresponding to the medical image may be determined according to the comparison result, so as to determine the category to which the medical image belongs.
105. Classifying the medical image based on the category to which the medical image belongs.
The image classification method provided by the embodiment of the application acquires a medical image to be processed; preprocessing the medical image to be processed to obtain a processed medical image; comparing the processed medical image with a reference medical image to obtain a comparison result; determining the category of the medical image according to the comparison result; classifying the medical image based on the category to which the medical image belongs. The medical image classification method and the medical image classification device can process the medical image to be processed, so that the category of the medical image is obtained, and the classification efficiency of the medical image is improved.
In order to better implement the above image classification method, correspondingly, the embodiment of the present application further provides an image classification device, where the image classification device may be integrated in an electronic device or a server. The meaning of the noun is the same as that in the above image classification method, and details of implementation can be referred to the description in the method embodiment.
Referring to fig. 2, fig. 2 is a schematic structural diagram of an image classification device according to an embodiment of the present application. The image classification apparatus 200 may include:
an image acquisition unit 201 for acquiring a medical image to be processed;
the image processing unit 202 is configured to pre-process the medical image to be processed to obtain a processed medical image;
an image comparison unit 203, configured to acquire feature points of the processed medical image; then, comparing the processed medical image with the reference medical image based on the characteristic points to obtain a comparison result;
a category determining unit 204, configured to determine a disease corresponding to the medical image according to the comparison result;
an image classification unit 205, configured to classify the medical image based on the category to which the medical image belongs.
In a specific implementation, the image comparing unit 203 may be configured to:
acquiring characteristic points of the processed medical image;
triangulation is carried out on the characteristic points by utilizing a triangulation technology to obtain a first triangulation image;
and comparing the first triangulation image with the reference medical image to obtain a comparison result.
In a specific implementation process, the image processing unit 202 may specifically be configured to:
and carrying out sharpening processing, edge compensation processing or noise reduction processing on the medical image to be processed to obtain a processed medical image.
The image classification device 200 provided by the embodiment of the application can process the medical image to be processed, so that the category of the medical image is obtained, and the classification efficiency of the medical image is improved.
The embodiment of the present application further provides a server, as shown in fig. 3, which shows a schematic structural diagram of the server according to the embodiment of the present application, specifically:
the server may include components such as a processor 301 of one or more processing cores, memory 302 of one or more computer-readable storage media, a power supply 303, and an input unit 304. Those skilled in the art will appreciate that the server architecture shown in FIG. 3 is not meant to be limiting, and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components. Wherein:
the processor 301 is a control center of the server, connects various parts of the entire server using various interfaces and lines, and performs various functions of the server and processes data by running or executing software programs and/or modules stored in the memory 302 and calling data stored in the memory 302, thereby performing overall monitoring of the server. Optionally, processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 301.
The memory 302 may be used to store software programs and modules, and the processor 301 executes various functional applications and data processing by operating the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to the use of the server, and the like. Further, the memory 302 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.
The server further includes a power supply 303 for supplying power to the various components, and preferably, the power supply 303 may be logically connected to the processor 301 through a power management system, so as to implement functions of managing charging, discharging, and power consumption through the power management system. The power supply 303 may also include any component of one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, and the like.
The server may also include an input unit 304, the input unit 304 being operable to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.
Although not shown, the server may further include a display unit and the like, which will not be described in detail herein. Specifically, in this embodiment, the processor 301 in the server loads the executable file corresponding to the process of one or more application programs into the memory 302 according to the following instructions, and the processor 301 runs the application programs stored in the memory 302, thereby implementing various functions as follows:
acquiring a medical image to be processed;
preprocessing the medical image to be processed to obtain a processed medical image;
acquiring feature points of the processed medical image, and comparing the processed medical image with the reference medical image based on the feature points to obtain a comparison result;
determining the category of the medical image according to the comparison result;
classifying the medical image based on the category to which the medical image belongs.
The above operations can be specifically referred to the previous embodiments, and are not described herein.
Accordingly, an electronic device according to an embodiment of the present disclosure may include, as shown in fig. 4, a Radio Frequency (RF) circuit 401, a memory 402 including one or more computer-readable storage media, an input unit 403, a display unit 404, a sensor 405, an audio circuit 406, a Wireless Fidelity (WiFi) module 407, a processor 408 including one or more processing cores, and a power supply 409. Those skilled in the art will appreciate that the electronic device configuration shown in fig. 4 does not constitute a limitation of the electronic device and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components. Wherein:
the RF circuit 401 may be used for receiving and transmitting signals during a message transmission or communication process, and in particular, for receiving downlink information of a base station and then sending the received downlink information to the one or more processors 408 for processing; in addition, data relating to uplink is transmitted to the base station. In general, the RF circuitry 401 includes, but is not limited to, an antenna, at least one Amplifier, a tuner, one or more oscillators, a Subscriber Identity Module (SIM) card, a transceiver, a coupler, a Low Noise Amplifier (LNA), a duplexer, and the like. In addition, the RF circuitry 401 may also communicate with networks and other devices via wireless communications. The wireless communication may use any communication standard or protocol, including but not limited to Global System for Mobile communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Message Service (SMS), and the like.
The memory 402 may be used to store software programs and modules, and the processor 408 executes various functional applications and data processing by operating the software programs and modules stored in the memory 402. The memory 402 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the electronic device, and the like. Further, the memory 402 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory 402 may also include a memory controller to provide the processor 408 and the input unit 403 access to the memory 402.
The input unit 403 may be used to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control. In particular, in a particular embodiment, the input unit 403 may include a touch-sensitive surface as well as other input devices. The touch-sensitive surface, also referred to as a touch display screen or a touch pad, may collect touch operations by a user (e.g., operations by a user on or near the touch-sensitive surface using a finger, a stylus, or any other suitable object or attachment) thereon or nearby, and drive the corresponding connection device according to a predetermined program. Alternatively, the touch sensitive surface may comprise two parts, a touch detection means and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts it to touch point coordinates, and sends the touch point coordinates to the processor 408, and can receive and execute commands from the processor 408. In addition, touch sensitive surfaces may be implemented using various types of resistive, capacitive, infrared, and surface acoustic waves. The input unit 403 may include other input devices in addition to the touch-sensitive surface. In particular, other input devices may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like.
The display unit 404 may be used to display information input by or provided to a user and various graphical user interfaces of the electronic device, which may be made up of graphics, text, icons, video, and any combination thereof. The Display unit 404 may include a Display panel, and optionally, the Display panel may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like. Further, the touch-sensitive surface may overlay the display panel, and when a touch operation is detected on or near the touch-sensitive surface, the touch operation is transmitted to the processor 408 to determine the type of touch event, and then the processor 408 provides a corresponding visual output on the display panel according to the type of touch event. Although in FIG. 4 the touch-sensitive surface and the display panel are shown as two separate components to implement input and output functions, in some embodiments the touch-sensitive surface may be integrated with the display panel to implement input and output functions.
The electronic device may also include at least one sensor 405, such as a light sensor, motion sensor, and other sensors. In particular, the light sensor may include an ambient light sensor that may adjust the brightness of the display panel according to the brightness of ambient light, and a proximity sensor that may turn off the display panel and/or the backlight when the electronic device is moved to the ear. As one of the motion sensors, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (generally, three axes), can detect the magnitude and direction of gravity when the mobile phone is stationary, can be used for applications for recognizing the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer and tapping), and other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor and the like which can be configured for the electronic device, and are not described herein again.
WiFi belongs to short distance wireless transmission technology, and the electronic device can help the user send and receive e-mail, browse web page and access streaming media, etc. through the WiFi module 407, which provides wireless broadband internet access for the user. Although fig. 4 shows the WiFi module 407, it is understood that it does not belong to the essential constitution of the electronic device, and may be omitted entirely as needed within the scope not changing the essence of the invention.
The processor 408 is a control center of the electronic device, connects various parts of the entire mobile phone by using various interfaces and lines, and performs various functions of the electronic device and processes data by operating or executing software programs and/or modules stored in the memory 402 and calling data stored in the memory 402, thereby monitoring the mobile phone as a whole. Optionally, processor 408 may include one or more processing cores; preferably, the processor 408 may integrate an application processor, which handles primarily the operating system, user interface, applications, etc., and a modem processor, which handles primarily the wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 408.
The electronic device may also include a power source 409 (e.g., a battery) for powering the various components, which may preferably be logically coupled to the processor 408 via a power management system to manage charging, discharging, and power consumption management functions via the power management system. The power supply 409 may also include any component of one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, and the like.
Although not shown, the electronic device may further include a camera, a bluetooth module, and the like, which are not described in detail herein. Specifically, in this embodiment, the processor 408 in the electronic device loads the executable file corresponding to the process of one or more application programs into the memory 402 according to the following instructions, and the processor 408 runs the application programs stored in the memory 402, thereby implementing various functions:
acquiring a medical image to be processed;
preprocessing the medical image to be processed to obtain a processed medical image;
acquiring feature points of the processed medical image, and comparing the processed medical image with the reference medical image based on the feature points to obtain a comparison result;
determining the category of the medical image according to the comparison result;
classifying the medical image based on the category to which the medical image belongs.
The above operations can be specifically referred to the previous embodiments, and are not described herein.
It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor.
To this end, the present application provides a storage medium, in which a plurality of instructions are stored, where the instructions can be loaded by a processor to execute the steps in any one of the image classification methods provided in the present application. For example, the instructions may perform the steps of:
acquiring a medical image to be processed;
preprocessing the medical image to be processed to obtain a processed medical image;
acquiring feature points of the processed medical image, and comparing the processed medical image with the reference medical image based on the feature points to obtain a comparison result;
determining the category of the medical image according to the comparison result;
classifying the medical image based on the category to which the medical image belongs.
The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.
Wherein the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
Since the instructions stored in the storage medium can execute the steps in the method for classifying any image provided in the embodiment of the present application, the beneficial effects that can be achieved by the method for classifying any image provided in the embodiment of the present application can be achieved, which are detailed in the foregoing embodiments and will not be described again here.
The foregoing detailed description is directed to a method, an apparatus, and a storage medium for classifying images provided by embodiments of the present application, and specific examples are applied herein to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A method of classifying an image, comprising:
acquiring a medical image to be processed;
preprocessing the medical image to be processed to obtain a processed medical image;
acquiring characteristic points of the processed medical image, and comparing the processed medical image with a reference medical image based on the characteristic points to obtain a comparison result;
determining the category of the medical image according to the comparison result;
classifying the medical image based on the category to which the medical image belongs.
2. The method for classifying an image according to claim 1, wherein said comparing said processed medical image and said reference medical image based on said feature points to obtain a comparison result comprises:
triangulation is carried out on the characteristic points by utilizing a triangulation technology to obtain a first triangulation image;
and comparing the first triangulation image with the reference medical image to obtain a comparison result.
3. The method for classifying an image according to claim 2, wherein said comparing said first triangulated image with said reference medical image to obtain a comparison result comprises:
acquiring a second triangulation image of the reference medical image;
and comparing the first triangular split image with the second triangular split image to obtain a comparison result.
4. The method for classifying an image according to any one of claims 1 to 3, wherein the preprocessing the medical image to be processed to obtain a processed medical image comprises:
and carrying out sharpening processing, edge compensation processing or noise reduction processing on the medical image to be processed to obtain a processed medical image.
5. The method of classifying an image according to claim 4, wherein the sharpening process includes a non-linear transformation or a linear transformation; the method adopted by the noise reduction processing comprises a Gaussian filtering method, a median filtering method or a high-low pass filtering method.
6. An apparatus for classifying an image, comprising:
an image acquisition unit for acquiring a medical image to be processed;
the image processing unit is used for preprocessing the medical image to be processed to obtain a processed medical image;
the image comparison unit is used for acquiring the characteristic points of the processed medical image; comparing the processed medical image with a reference medical image based on the characteristic point to obtain a comparison result;
the category determining unit is used for determining the category of the medical image according to the comparison result;
and the image classification unit is used for classifying the medical image based on the category of the medical image.
7. The apparatus for classifying an image according to claim 6, wherein the image comparing unit is specifically configured to:
acquiring characteristic points of the processed medical image;
triangulation is carried out on the characteristic points by utilizing a triangulation technology to obtain a first triangulation image;
and comparing the first triangulation image with the reference medical image to obtain a comparison result.
8. The apparatus for classifying an image according to claim 6, wherein the image processing unit is specifically configured to:
and carrying out sharpening processing, edge compensation processing or noise reduction processing on the medical image to be processed to obtain a processed medical image.
9. A storage medium storing a plurality of instructions adapted to be loaded by a processor to perform the method of any of claims 1 to 5.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1-5 when executing the computer program.
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