CN111383211A - Bone case identification method, device, server and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses a bone case identification method, a bone case identification device, a server and a storage medium, wherein the method comprises the following steps: acquiring a first image of the whole body bone of a current user and extracting a first gray image of the first image; matching the first grey-white image in a preset database to obtain a corresponding second image; and generating a recognition result according to the second image and the case description. According to the bone case identification method provided by the embodiment of the invention, the condition of the user is determined and the identification result is returned to the user by uploading the image picture of the whole body bone by the user and automatically matching and identifying the database, so that the problem that the identification error exists due to the fact that the bone image is easily influenced by subjective factors when being manually identified in the prior art is solved, the accurate online identification of the bone image and the recommendation of the treatment method are realized, and the user experience is improved.
Description
Technical Field
The embodiment of the invention relates to a bone image technology, in particular to a bone case identification method, a bone case identification device, a server and a storage medium.
Background
Single Photon Emission Computed Tomography (SPECT) is one of the important imaging techniques of nuclear medicine, and has been widely used in clinical applications since the 80 s of the 20 th century. Different from the traditional imaging technologies such as CT, MRI and the like, SPECT uses a radionuclide labeled imaging agent to carry out in-vitro imaging, can display the functions and metabolic changes of human tissues and organs, and provides clinical diagnostic information in the aspect of functional metabolism. The radioactive developer used for the nuclide examination has very small chemical dose, does not interfere the physiological process of an organism, has few adverse reactions, has lower radiation absorption dose than the X-ray examination of the same part, and is safe and noninvasive for the organism.
The whole-body bone SPECT imaging artificial image analysis method mainly has the following defects: 1. affecting lesion detection. At present, the image analysis of bone imaging is mainly carried out by manual identification according to the intake comparison of focus and normal tissue, and a reader identifies possible abnormalities by depending on theory and clinical experience. Some lesions with small contrast differences or small volumes from the surrounding tissue may be missed due to visual identification. In addition, due to the limitation of methodology, the whole-body bone SPECT imaging definition and spatial resolution are low, the image information amount is limited, so that the image analysis and interpretation are more easily influenced by subjective factors, and the accuracy among different film readers and the repeatability of the same film reader are different. 2. Affecting the positioning accuracy. The images obtained by the SPECT scanning of the whole body bone are overlapped images similar to X-ray radiography, and the left and right of the front and back images are opposite, so that the requirements on the clinical experience, the work delicacy and the responsibility of a reader are higher, and the accuracy of the focus positioning is easily influenced. 3. The workload increases. The whole body bone imaging accounts for more than 60% of SPECT examination items in China, and 128.5 thousands of cases/year are achieved in 2018. Medical imaging data continues to grow at a disproportionate rate and imaging physicians' workload increases rapidly compared to the limited number of trained nuclear medicine physicians available. In addition, due to the wide imaging range of the whole-body bone SPECT scanning, the workload of image analysis with a plurality of suspected lesions is further increased, and adverse effects caused by human factors such as visual fatigue and working omission are inevitable.
Disclosure of Invention
The invention provides a bone case identification method, a bone case identification device, a bone case identification server and a storage medium, so that accurate online identification of bone images and treatment method recommendation are realized, and user experience is improved.
In a first aspect, an embodiment of the present invention provides a bone case identification method, including:
acquiring a first image of the whole body bone of a current user and extracting a first gray image of the first image;
matching the first grey-white image in a preset database to obtain a corresponding second image;
and generating a recognition result according to the second image.
Optionally, before the acquiring the first image of the whole body bone of the current user and the acquiring the first gray-white image of the first image, the method further includes: and generating a preset database according to the sample data of the historical user.
Optionally, the generating a preset database according to the sample data of the historical user includes:
acquiring sample data of a historical user, wherein the sample data comprises a second image and case description;
carrying out gray processing on the second image to obtain a second gray-white image;
and generating a preset database according to the second grey-white image and the case description.
Optionally, the generating a preset database according to the second gray image and the case description includes:
performing linear registration and nonlinear registration according to the second grey-white image and the standard image, unifying resolution and correcting distinguishing characteristics;
obtaining the gray distribution of each pixel point in the second gray white image by using a model analysis method for the second gray white image after the linear registration and the nonlinear registration;
and generating a preset database according to the gray distribution and the case description of each pixel point.
Optionally, the model analysis method includes: normal model distribution and non-normal model distribution.
Optionally, the matching the first gray-white image in a preset database to obtain a corresponding second image includes: and matching each pixel point in the first grey-white image in corresponding grey distribution in a preset database to obtain a corresponding second image.
Optionally, the first image of the user's entire bone comprises a SPECT image of the user's entire bone.
In a second aspect, an embodiment of the present invention further provides a bone case identification apparatus, where the apparatus includes:
the data acquisition module is used for acquiring a first image of the whole body bone of a current user and extracting a first gray image of the first image;
the image matching module is used for matching the first grey-white image in a preset database to obtain a corresponding second image;
and the image identification module is used for generating an identification result according to the second image and recommending the identification result to the current user.
In a third aspect, an embodiment of the present invention further provides a server, where the server includes:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a bone case identification method as in any one of the above.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the bone case identification method as described in any one of the above.
The embodiment of the invention discloses a bone case identification method, a bone case identification device, a server and a storage medium, wherein the method comprises the following steps: acquiring a first image of the whole body bone of a current user and extracting a first gray image of the first image; matching the first grey-white image in a preset database to obtain a corresponding second image; and generating an identification result according to the second image and recommending the identification result to the current user. According to the bone case identification method provided by the embodiment of the invention, the condition of the user is determined and the identification result is returned to the user by uploading the image picture of the whole body bone by the user and automatically matching and identifying the database, so that the problem that the identification error exists due to the fact that the bone image is easily influenced by subjective factors when being manually identified in the prior art is solved, the accurate online identification of the bone image and the recommendation of the treatment method are realized, and the user experience is improved.
Drawings
Fig. 1 is a flowchart of a bone case identification method according to an embodiment of the present invention;
fig. 2 is a flowchart of a bone case identification method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a bone case identification apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. A process may be terminated when its operations are completed, but may have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.
Furthermore, the terms "first," "second," and the like may be used herein to describe various orientations, actions, steps, elements, or the like, but the orientations, actions, steps, or elements are not limited by these terms. These terms are only used to distinguish one direction, action, step or element from another direction, action, step or element. For example, the first gray-white image may be referred to as a second gray-white image, and similarly, the second gray-white image may be referred to as a first gray-white image, without departing from the scope of the present application. The first gray image and the second gray image are both gray images, but are not the same gray image. The terms "first", "second", etc. are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Example one
Fig. 1 is a flowchart of a bone case identification method according to an embodiment of the present invention, where the embodiment is applicable to a system for automatically identifying whether a bone image of a user's whole body is normal, and specifically includes:
In the present embodiment, the first image of the user's entire bone is a SPECT image of the user's entire bone. The whole body bone imaging is a main item of clinical application of nuclear medicine imaging for many years, and accounts for more than 60% of SPECT examination items in China. Radioactivity commonly used in bone imaging99mTc-labelled bisphosphonates: (99mTc-MDP) is an imaging agent, and has the advantages of high and rapid bone uptake, rapid clearance of blood and soft tissue, and the likeAnd (4) point. About 50-60% of radioactivity is gathered in the bone 2-3 hours after the imaging agent is injected into the vein, the rest is discharged by the urinary system, the ratio of the radioactivity of the bone/soft tissue is higher, and the bone imaging quality is good. The conventional bone imaging adopts a whole-body static plane acquisition mode, the dual probes simultaneously carry out front and back position acquisition, and the whole-body scanning can be completed in about 10-20 minutes. It not only can display the skeleton shape of the whole body, but also can reflect the information of the skeleton, the blood flow, the metabolism and the like of pathological changes. When the skeleton is changed by tumor, inflammation, injury repair and the like, abnormal radioactive concentration or defect focus can appear on the bone imaging image of the part. Because the change of blood flow and metabolism is usually earlier than the change of morphological structure, the bone metastasis can be detected by bone imaging 3-6 months earlier than X-ray examination. The whole-body bone SPECT imaging has the advantages of early diagnosis of skeletal lesions, low cost, wide exploration range and the like, and becomes the first choice screening examination for diagnosing bone metastases of malignant tumor patients. In the embodiment, the gray-white image of the SPECT image of the whole body bone of the user is obtained, so that comparison and identification are facilitated, and the identification accuracy is ensured.
And step 110, matching the first gray-white image in a preset database to obtain a corresponding second image.
In this embodiment, the gray images of the entire body bone of the user obtained in step 100 are matched in a preset database, where the preset database includes a plurality of sample gray images for comparison and identification. And obtaining a sample gray-white image closest to the gray-white image of the whole body bone of the user by comparing the plurality of sample gray-white images in the gray-white image database of the whole body bone of the user.
And 120, generating a recognition result according to the second image.
In this embodiment, if the sample obtained in step 110 is a normal sample, the whole body bone image recognition result of the user is normal, and subsequent manual continuous recognition is not needed. If the sample obtained in step 110 is an abnormal sample, the identification result is abnormal, and a part with a larger difference, which is a suspected lesion, exists in the gray image of the user, the system automatically marks and prompts the suspected lesion according to the part of the suspected case, illustratively, the system includes marking or circling, and reminds a doctor or the user to manually review the suspected lesion, so as to confirm whether the related suspected lesion of the user has a health problem. After the doctor makes a diagnosis manually, the result can be returned to the user, and the user can see a diagnosis according to the actual condition of the user.
The embodiment discloses a bone case identification method, which comprises the following steps: acquiring a first image of the whole body bone of a current user and extracting a first gray image of the first image; matching the first grey-white image in a preset database to obtain a corresponding second image; and generating a recognition result according to the second image and the case description. According to the bone case identification method provided by the embodiment of the invention, the condition of the user is determined and the identification result is returned to the user by uploading the image picture of the whole bone by the user and automatically matching and identifying the database, so that the problem that the identification error exists due to the fact that the orthopedics image is easily influenced by subjective factors when the orthopedics image is manually identified in the prior art is solved, the accurate on-line identification of the orthopedics image and the recommendation of the treatment method are realized, and the user experience is improved.
Example two
Fig. 2 is a flowchart of a bone case identification method according to a second embodiment of the present invention, where the present embodiment is applicable to a system for automatically identifying whether a bone image of a user is normal, and specifically, the method includes:
and 200, generating a preset database according to the sample data of the historical user.
In the embodiment, according to a plurality of whole-body bone SPECT sample images of historical users, a preset database is generated according to the sample images, and comparison with the whole-body bone SPECT images of sick users is facilitated. In this embodiment, specifically, step 200 includes:
step 201, sample data of a historical user is obtained, wherein the sample data comprises a second image and a case description.
In this embodiment, the sample data may include a sample SPECT image and a case description, wherein the case description includes: case descriptions include diagnostic procedures and treatment methods, among others. If the image input by the user is highly similar to the image of a certain sample, the case description of the sample can be used as a diagnosis reference, so that a doctor can conveniently and quickly diagnose, and the diagnosis efficiency is improved.
Step 202, performing gray processing on the second image to obtain the second gray-white image.
In this embodiment, all sample images are subjected to gray processing, so that the sample images and the whole body bone image of the user are ensured to have the same format, and rapid image recognition is facilitated.
And 203, generating a preset database according to the second grey-white image and the case description.
In this embodiment, the sample gray-white images and the corresponding case descriptions are unified to generate a preset database, the preset database comprises a plurality of sample gray-white images, and the preset database can be continuously updated in the actual use process, so that the database resources are continuously expanded, and the identification accuracy is improved.
In this embodiment, step 203 further includes:
step 2031, performing linear registration and nonlinear registration according to the second gray white image and the standard image, unifying resolution and correcting distinguishing features.
In this embodiment, n negative SPECT image samples are taken, along with one template image. The n images are linearly registered one by one to the template image. Namely:
A∈R2×2,b∈R2
Ai,bi＝argmin E(A,b)
wherein E is a reference amount, in linear registration, I is a template image, I isiFor each training image. Finding the optimal Ai,biThen, note I'iFor the images after linear registration:
I'i＝Ii(Aix+bi)
in performing the non-linear registration, the registration is performed,
wherein I is a template image, I'iIs the image after linear registration.
Note the book
Then JiFor each matched image. The rightmost "SPECT image 1 matched to template image" to "SPECT image n matched to template image" in FIG. 3 is J after the match is goodiAnd (4) an image.
Step 2032, obtaining the gray level distribution of each pixel point in the second gray white image by using a model analysis method for the second gray white image after the linear registration and the nonlinear registration.
In this example, we calculate the gray scale range for each point in the matched image x ∈ R for each point2，Ji(x) A gray scale sample is provided. Using these n samples, we can find the gray distribution at that point. Here, we can employ normal model-based parameter estimation, and non-parametric estimation-based.
In this embodiment, the model analysis method includes: normal model distribution and non-normal model distribution.
Wherein, under the assumption of normal model, we are x ∈ R for each point2Is provided with
Under the assumption of a non-normal model, for each point x ∈ R2Taking kernel function k: R by using kernel density estimation method2→R+∪ {0} satisfies
Constructing the probability density function p (x, z) for this point:
step 2033, generating a preset database according to the gray level distribution and the case description of each pixel point.
In the embodiment, the preset database is uniformly produced according to the gray distribution of each pixel point and the case description of the sample, whether the pixel points in the SPECT image of the whole body image of the user exceed the preset gray distribution range can be accurately determined through the gray distribution, and the fault tolerance rate of identification is improved.
And step 220, matching each pixel point in the first gray-white image in corresponding gray distribution in a preset database to obtain a corresponding second image.
In this embodiment, when there is a new image I to be analyzed, we first use a linear and non-linear registration method similar to the first step to register I to the same space as the template, and record the registered image as J.
Next, we analyze J using the model trained in the first step.
Under the assumption of a normal model, we can calculate the likelihood of each point anomaly l in the J-diagram:
alternatively, under the assumption of a non-normal model, l is calculated:
l:R2→R+；l(x)＝1-p(x,J(x))
consider l as an image where the likelihood value is high is where the lesion is suspected.
And step 230, generating a recognition result according to the second image.
In this embodiment, if there is no place with a high likelihood value in step 220, the recognition result is normal, and the image does not need to be continuously recognized by a subsequent person. If there is a place with a high likelihood value in step 220, the identification result is abnormal, and a place with a large difference from the gray distribution in the user gray-white image is a suspected focus part, the system automatically marks and prompts according to the suspected case part, illustratively, marks or circles, and reminds a doctor or the user to manually review the suspected focus and confirm whether the related suspected focus of the user has a health problem. After the doctor makes a diagnosis manually, the result can be returned to the user, and the user can see a diagnosis according to the actual condition of the user.
The embodiment discloses a bone case identification method, which comprises the following steps: generating a preset database according to sample data of a historical user; acquiring a first image of the whole body bone of a current user and extracting a first gray image of the first image; matching the first grey-white image in a preset database to obtain a corresponding second image; and generating a recognition result according to the second image. According to the bone case identification method provided by the embodiment of the invention, the condition of the user is determined and the identification result is returned to the user by uploading the image picture of the whole bone by the user and automatically matching and identifying the database, so that the problem that the identification error exists due to the fact that the orthopedics image is easily influenced by subjective factors when the orthopedics image is manually identified in the prior art is solved, the accurate on-line identification of the orthopedics image and the recommendation of the treatment method are realized, and the user experience is improved.
EXAMPLE III
The bone case identification device provided by the embodiment of the invention can implement the bone case identification method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. Fig. 3 is a schematic structural diagram of a bone case identification apparatus 300 according to an embodiment of the present invention. Referring to fig. 3, a bone case identification apparatus 300 provided in an embodiment of the present invention may specifically include:
the data acquisition module 310 is configured to acquire a first image of a whole body bone of a current user and extract a first gray-white image of the first image;
the image matching module 320 is configured to match the first off-white image in a preset database to obtain a corresponding second image;
and the image identification module 330 is configured to generate an identification result according to the second image and recommend the identification result to the current user.
Further, before the acquiring the first image of the whole body bone of the current user and the acquiring the first gray-white image of the first image, the method further includes: and generating a preset database according to the sample data of the historical user.
Further, the generating a preset database according to the sample data of the historical user includes:
acquiring sample data of a historical user, wherein the sample data comprises a second image and case description;
carrying out gray processing on the second image to obtain a second gray-white image;
and generating a preset database according to the second grey-white image and the case description.
Further, the generating a preset database according to the second gray-white image and the case description includes:
performing linear registration and nonlinear registration according to the second grey-white image and the standard image, unifying resolution and correcting distinguishing characteristics;
obtaining the gray distribution of each pixel point in the second gray white image by using a model analysis method for the second gray white image after the linear registration and the nonlinear registration;
and generating a preset database according to the gray distribution and the case description of each pixel point.
Further, the model analysis method comprises the following steps: normal model distribution and non-normal model distribution.
Further, the matching the first gray-white image in a preset database to obtain a corresponding second image includes: and matching each pixel point in the first grey-white image in corresponding grey distribution in a preset database to obtain a corresponding second image.
Further, the first image of the user's entire bone includes a SPECT image of the user's entire bone.
The embodiment discloses a bone case recognition device, including: the data acquisition module is used for acquiring a first image of the whole body bone of a current user and extracting a first gray image of the first image; the image matching module is used for matching the first grey-white image in a preset database to obtain a corresponding second image; and the image identification module is used for generating an identification result according to the second image and recommending the identification result to the current user. According to the bone case identification method provided by the embodiment of the invention, the condition of the user is determined and the identification result is returned to the user by uploading the image picture of the whole bone by the user and automatically matching and identifying the database, so that the problem that the identification error exists due to the fact that the orthopedics image is easily influenced by subjective factors when the orthopedics image is manually identified in the prior art is solved, the accurate on-line identification of the orthopedics image and the recommendation of the treatment method are realized, and the user experience is improved.
Example four
Fig. 4 is a schematic structural diagram of a computer server according to an embodiment of the present invention, as shown in fig. 4, the computer server includes a memory 410 and a processor 420, the number of the processors 420 in the computer server may be one or more, and one processor 420 is taken as an example in fig. 4; the memory 410 and the processor 420 in the device may be connected by a bus or other means, and fig. 4 illustrates the connection by a bus as an example.
The memory 410 is used as a computer-readable storage medium for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the bone case identification method in the embodiment of the present invention (e.g., the data acquisition module 310, the image matching module 320, and the image identification module 330 in the bone case identification apparatus 300), and the processor 420 executes various functional applications and data processing of the device/terminal/equipment by operating the software programs, instructions, and modules stored in the memory 410, so as to implement the bone case identification method.
Wherein the processor 420 is configured to run the computer program stored in the memory 410, and implement the following steps:
acquiring a first image of the whole body bone of a current user and extracting a first gray image of the first image;
matching the first grey-white image in a preset database to obtain a corresponding second image;
and generating a recognition result according to the second image.
In one embodiment, the computer program of the computer device provided by the embodiment of the present invention is not limited to the above method operations, and may also perform related operations in the bone case identification method provided by any embodiment of the present invention.
The memory 410 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 410 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 non-volatile solid state storage device. In some examples, the memory 410 may further include memory located remotely from the processor 420, which may be connected to devices/terminals/devices through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The embodiment discloses a server, which is used for executing the following method, comprising the following steps: acquiring a first image of the whole body bone of a current user and extracting a first gray image of the first image; matching the first grey-white image in a preset database to obtain a corresponding second image; and generating a recognition result according to the second image and the case description. According to the bone case identification method provided by the embodiment of the invention, the condition of the user is determined and the identification result is returned to the user by uploading the image picture of the whole bone by the user and automatically matching and identifying the database, so that the problem that the identification error exists due to the fact that the orthopedics image is easily influenced by subjective factors when the orthopedics image is manually identified in the prior art is solved, the accurate on-line identification of the orthopedics image and the recommendation of the treatment method are realized, and the user experience is improved.
EXAMPLE five
An embodiment of the present invention further provides a storage medium containing computer-executable instructions, which when executed by a computer processor, perform a bone case identification method, including:
acquiring a first image of the whole body bone of a current user and extracting a first gray image of the first image;
matching the first grey-white image in a preset database to obtain a corresponding second image;
and generating a recognition result according to the second image.
Of course, the storage medium provided by the embodiment of the present invention contains computer-executable instructions, and the computer-executable instructions are not limited to the method operations described above, and may also perform related operations in a bone case identification method provided by any embodiment of the present invention.
The computer-readable storage media of embodiments of the invention may take any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or terminal. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The embodiment discloses a storage medium for executing the following method, comprising: acquiring a first image of the whole body bone of a current user and extracting a first gray image of the first image; matching the first grey-white image in a preset database to obtain a corresponding second image; and generating a recognition result according to the second image and the case description. According to the bone case identification method provided by the embodiment of the invention, the condition of the user is determined and the identification result is returned to the user by uploading the image picture of the whole bone by the user and automatically matching and identifying the database, so that the problem that the identification error exists due to the fact that the orthopedics image is easily influenced by subjective factors when the orthopedics image is manually identified in the prior art is solved, the accurate on-line identification of the orthopedics image and the recommendation of the treatment method are realized, and the user experience is improved.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A bone case identification method is characterized by comprising the following steps:
acquiring a first image of the whole body bone of a current user and extracting a first gray image of the first image;
matching the first grey-white image in a preset database to obtain a corresponding second image;
and generating a recognition result according to the second image.
2. A bone case identification method as in claim 1, wherein said obtaining a first image of the current user's entire bone and obtaining a first gray-white image of said first image further comprises: and generating a preset database according to the sample data of the historical user.
3. The method for identifying bone cases as claimed in claim 2, wherein the generating the preset database according to the sample data of the historical users comprises:
acquiring sample data of a historical user, wherein the sample data comprises a second image and case description;
carrying out gray processing on the second image to obtain a second gray-white image;
and generating a preset database according to the second grey-white image and the case description.
4. A bone case identification method as in claim 3 wherein said generating a pre-defined database from said second gray image and said case description comprises:
performing linear registration and nonlinear registration according to the second grey-white image and the standard image, unifying resolution and correcting distinguishing characteristics;
obtaining the gray distribution of each pixel point in the second gray white image by using a model analysis method for the second gray white image after the linear registration and the nonlinear registration;
and generating a preset database according to the gray distribution and the case description of each pixel point.
5. A bone case identification method as claimed in claim 4, characterized in that said model analysis method comprises: normal model distribution and non-normal model distribution.
6. A bone case identification method as claimed in claim 5, wherein said matching said first gray image in a predetermined database to obtain a corresponding second image comprises: and matching each pixel point in the first grey-white image in corresponding grey distribution in a preset database to obtain a corresponding second image.
7. A bone case identification method as in claim 1 wherein the first image of the user's entire bone comprises a SPECT image of the user's entire bone.
8. A bone case recognition apparatus, comprising:
the data acquisition module is used for acquiring a first image of the whole body bone of a current user and extracting a first gray image of the first image;
the image matching module is used for matching the first grey-white image in a preset database to obtain a corresponding second image;
and the image identification module is used for generating an identification result according to the second image and recommending the identification result to the current user.
9. A server, characterized in that the server comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the bone case identification method as recited in any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a bone case identification method as claimed in any one of claims 1 to 7.
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