CN111028228A - Matching processing method of medical image system based on big data - Google Patents
Matching processing method of medical image system based on big data Download PDFInfo
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Abstract
The invention belongs to the technical field of medical images, and discloses a matching processing method of a medical image system based on big data, which comprises the steps of image acquisition, image segmentation and recombination, and characteristic data extraction and quantification; the medical imaging equipment acquires medical images of users in an operating room, the medical images are sent to an application program of the workstation, the application program receives the medical images of the users acquired by the medical imaging equipment, standardization and structurization of data elements in the images are completed, segmentation and recombination of the images are realized, and characteristic data are extracted and quantized; establishing a medical image database and sharing data; identifying to obtain characteristic data and quantitative information; and searching and matching with the images existing in the medical image database. The invention realizes the automatic matching of the medical imaging equipment and the specified object, and improves the matching efficiency; the medical information inquiry system is convenient for ordinary personnel to inquire the required medical related information and also convenient for medical professionals to inquire the related medical information in a targeted manner.
Description
Technical Field
The invention belongs to the technical field of medical images, and particularly relates to a matching processing method of a medical image system based on big data.
Background
Currently, the closest prior art:
medical imaging equipment belongs to indispensable equipment of hospitals, and is widely applied to important scenes such as radiology departments, operating rooms and the like of the hospitals. However, since the medical imaging device has a certain specificity, it needs to be matched with a designated object to complete the corresponding work.
For example, some medical imaging devices are expensive, and usually a hospital only purchases one or a few devices, so the medical imaging devices need to be recycled in each operating room, and are applied to different users in different operating rooms to acquire medical images of different users, and at this time, the medical imaging devices need to be matched with the operating rooms, so that the medical images of the users in the operating rooms can be stored in the databases corresponding to the users. For another example, some medical imaging devices are large in size and need to be remotely controlled by a remote control device, so that the medical imaging device needs to be matched with the remote control device to operate the medical imaging device through the remote control device.
However, in the prior art, the matching process of the medical imaging device and the designated object is related to manual matching, which is cumbersome to operate and results in low matching efficiency.
"big data (bigdata)" is a concept widely mentioned in the information and business fields in the last decade, and is derived from the remarkable improvement of data storage and calculation capability and statistical analysis capability in recent years, and the capability of data processing and benefit obtaining is provided. The big data concept is developed along with the development of information technology and statistical technology, shows a certain application value in the fields of business and social science, and is preliminarily approved in the field of health. However, due to the professional characteristics of medical imaging, the application of big data is still in the preliminary exploration stage.
In summary, the problems of the prior art are as follows:
(1) in the prior art, the matching process of the medical imaging equipment and the specified object is related operations of manual matching, and the matching efficiency is low due to complex operation.
(2) Due to the professional characteristics of medical imaging, the application of big data is still in the preliminary exploration stage.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a matching processing method of a medical image system based on big data.
The invention is realized in such a way, and discloses a matching processing method of a medical image system based on big data. The matching processing method of the medical image system based on big data comprises the following steps:
the method comprises the steps of collecting images, segmenting and recombining the images, and extracting and quantifying feature data. The medical imaging equipment collects medical images of users in an operating room, the medical images are sent to the application program of the workstation, the application program receives the medical images of the users collected by the medical imaging equipment, standardization and structuralization of data elements in the images are completed, segmentation and recombination of the images are achieved, and feature data are extracted and quantized.
And step two, establishing a medical image database and sharing data. And storing the extracted feature data and the corresponding medical image of the user into a corresponding medical image database, manually supplementing mark information of the medical image at a later stage, and storing the mark information into the medical image database to complete the updating and data sharing of the database.
And step three, the medical imaging equipment collects medical images of users in an operating room, automatically or semi-automatically diagnoses the cross enhancement marks to extract the information of medical image files to be matched, and characteristic data and quantitative information are obtained through recognition.
And step four, searching and matching the images in the medical image database according to the obtained characteristic data and the quantitative information.
Further, the third step specifically includes:
firstly, medical imaging equipment acquires medical images of users in an operating room;
secondly, reading patient information related to the medical image to be matched from a medical image database;
thirdly, extracting all information in the DICOM file header from the medical image to be matched;
fourthly, analyzing the extracted DICOM file header information, and performing case or series level identification on the medical image to be matched;
fifthly, preprocessing the medical image to be matched based on case or series hierarchical identification;
sixthly, analyzing the extracted DICOM file header information, and performing image level identification and preprocessing on the medical image to be matched;
and seventhly, extracting the medical image characteristics of the preprocessed medical image to be matched.
Further, step four, searching and matching the images in the medical image database according to the obtained feature data and the quantitative information, specifically comprising:
firstly, a medical image cloud server generates a random number sequence matrix R, wherein R is { Rmn }, (1 is less than or equal to m and less than or equal to N), and (1 is less than or equal to N and less than or equal to N); the medical image cloud server calculates a Hash Hash matrix H according to the R and sends the H to a medical image database; the medical image cloud server randomly distributes M multiplied by n random numbers to the users in M operating rooms according to the row unit R, the random number sequence set obtained by the users SUj in the operating rooms is recorded with Rj (j is more than or equal to 1 and less than or equal to M), and the second step is carried out;
secondly, SUj sends encrypted channel application information EBSj (Rj1, t) to the station BSj in the located area Cj through the control channel, where t is time.
Further, BSj collects all EBSj received at the time t and decrypts the received sequence string to obtain a series of secondary user information for R times; BSj marks each R times and then encrypts the R times again, and the label of each R times and the corresponding random number information form encryption information; BSj sends the encrypted information for each R times to the medical image database.
Further, after decrypting the random number in the encrypted information, the medical image database performs Hash calculation to obtain a Hash value of the random number, and after the Hash value of the random number is matched with the Hash value of H, the user corresponding to the label passes verification; the medical image database encrypts the verified label and the available frequency spectrum information in the Cj to form available channel information, and sends the available channel information to the BSj, and the medical image database deletes the matched Hash value in the H;
BSj allocates channels for the verified users in the operating room according to the available channel information;
BSj registering channel use information in a medical image database;
the formula for calculating the Hash matrix H by the medical image cloud server is as follows:
in the third step, the BSj marks each R times and then encrypts again, and the label of each R times and the corresponding random number information form encrypted information, which specifically comprises the following steps: BSj selects a label tagj1 to label the random number, and the encryption information is
When the Hash sequence of the users in the operating room is used completely or new users are added and the Hash sequence string needs to be applied to the medical image cloud server again, the medical image cloud server updates the Hash matrix and sends the Hash matrix to the medical image database.
Another object of the present invention is to provide a big data based medical imaging system for implementing the matching processing method of the big data based medical imaging system, the big data based medical imaging system comprising:
the medical image equipment is used for acquiring medical images of users, segmenting and recombining the images and extracting and quantizing feature data; sending the medical image to an application program of a workstation;
the workstation receives the medical image of a user by using an application program, completes standardization and structurization of data elements in the image, realizes segmentation and recombination of the image, extracts characteristic data and quantizes the characteristic data;
the medical image database receives a user medical image acquired by the medical image equipment and stores the user medical image into a database corresponding to the user;
the medical image database and shared data construction module is used for storing the extracted feature data and the corresponding user medical image into a corresponding medical image database, manually supplementing mark information of the medical image at the later stage and storing the mark information into the medical image database to complete the updating and data sharing of the database;
the characteristic data and quantitative information identification module is used for acquiring medical images of users in an operating room for medical imaging equipment, automatically or semi-automatically diagnosing cross enhancement marks to extract medical image file information to be matched, and identifying to obtain characteristic data and quantitative information;
and the image searching and matching module is used for searching and matching the images in the medical image database according to the obtained characteristic data and the quantitative information.
Further, the medical imaging device comprises an electronic computed tomography CT device or an X-ray machine.
Another object of the present invention is to provide a matching processing program of a medical imaging system based on big data, which is applied to a computer, and the matching processing program of the medical imaging system based on big data implements the matching processing method of the medical imaging system based on big data.
Another object of the present invention is to provide a terminal, which carries a processor for implementing the matching processing method of the medical image system based on big data.
Another object of the present invention is to provide a computer-readable storage medium, which includes instructions that, when executed on a computer, cause the computer to execute the matching processing method of the big data based medical image system.
In summary, the advantages and positive effects of the invention are:
according to the matching processing method of the medical image system based on the big data, provided by the embodiment of the invention, the characteristic data is extracted and quantified through image acquisition, image segmentation and recombination, and a medical image database is established and shared. The medical image equipment collects medical images of users in an operating room, automatically or semi-automatically diagnoses the cross enhancement marks to extract the information of medical image files to be matched, identifies to obtain characteristic data and quantitative information, and searches and matches the images existing in a medical image database. The automatic matching of the medical imaging equipment and the specified object can be realized, and the matching efficiency is improved. The medical information inquiry system is convenient for ordinary personnel to inquire the required medical related information and also convenient for medical professionals to inquire the related medical information in a targeted manner.
According to the method, searching and matching are carried out on the images in a medical image database according to the obtained feature data and the quantitative information, a medical image cloud server generates a random number sequence matrix R, wherein R is { Rmn }, (m is more than or equal to 1 and less than or equal to N), and (N is more than or equal to 1 and less than or equal to N); the medical image cloud server calculates a Hash Hash matrix H according to the R and sends the H to a medical image database; the medical image cloud server randomly distributes M multiplied by n random numbers to users in M operating rooms according to the row unit R, Rj (j is more than or equal to 1 and less than or equal to M) is recorded in a random number sequence set obtained by the users SUj in the operating rooms, SUj sends encrypted channel application information EBSj (Rj1, t) to a workstation BSj in the located area Cj through a control channel, and t is time. BSj collects all EBSj received at the time t and decrypts the received sequence string to obtain a series of secondary user information for R times; BSj marks each R times and then encrypts the R times again, and the label of each R times and the corresponding random number information form encryption information; BSj sends the encrypted information for each R times to the medical image database. The medical image database decrypts the random number in the encrypted information, performs Hash calculation to obtain a Hash value of the random number, and after the Hash value of the random number is matched with the Hash value of H, the user corresponding to the label passes verification; the medical image database encrypts the verified label and the available frequency spectrum information in the Cj to form available channel information, and sends the available channel information to the BSj, and the medical image database deletes the matched Hash value in the H; BSj allocates channels for the verified users in the operating room according to the available channel information; BSj registering channel use information in a medical image database; matching of images can be achieved.
Drawings
Fig. 1 is a flowchart of a matching processing method of a medical imaging system based on big data according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for acquiring medical images of a user in an operating room by a medical imaging device, extracting information of medical image files to be matched by an automatic or semi-automatic diagnosis cross enhancement identifier, and identifying and obtaining feature data and quantitative information according to an embodiment of the present invention.
Fig. 3 is a diagram of a medical imaging system based on big data according to an embodiment of the present invention.
In the figure: 1. a medical imaging device; 2. a workstation; 3. a medical image database; 4. a medical image database and shared data construction module; 5. a characteristic data and quantization information identification module; 6. and the image searching and matching module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
As shown in fig. 1, a matching processing method of a medical image system based on big data according to an embodiment of the present invention includes:
s101: the method comprises the steps of image acquisition, image segmentation and recombination, and feature data extraction and quantification. The medical imaging equipment collects medical images of users in an operating room, the medical images are sent to the application program of the workstation, the application program receives the medical images of the users collected by the medical imaging equipment, standardization and structuralization of data elements in the images are completed, segmentation and recombination of the images are achieved, and feature data are extracted and quantized.
S102: a medical image database is established and data is shared. And storing the extracted feature data and the corresponding medical image of the user into a corresponding medical image database, manually supplementing mark information of the medical image at a later stage, and storing the mark information into the medical image database to complete the updating and data sharing of the database.
S103: the medical imaging equipment collects medical images of users in an operating room, automatically or semi-automatically diagnoses the cross enhancement marks to extract the information of medical image files to be matched, and identifies to obtain characteristic data and quantitative information.
S104: and searching and matching the images in the medical image database according to the obtained feature data and the quantitative information.
As shown in fig. 2, the step S103 specifically includes:
s201: the medical image equipment acquires medical images of users in an operating room;
s202: reading patient information related to medical images to be matched from a medical image database;
s203: extracting all information in a DICOM file header from a medical image to be matched;
s204: analyzing the extracted DICOM file header information, and performing case or series level identification on the medical image to be matched;
s205: preprocessing the medical image to be matched based on case or series hierarchical identification;
s206: analyzing the extracted DICOM file header information, and performing image level identification and preprocessing on the medical image to be matched;
s207: and extracting the medical image characteristics of the preprocessed medical image to be matched.
In the embodiment of the present invention, searching and matching with an existing image in a medical image database according to the obtained feature data and the quantization information specifically include:
firstly, a medical image cloud server generates a random number sequence matrix R, wherein R is { Rmn }, (1 is less than or equal to m and less than or equal to N), and (1 is less than or equal to N and less than or equal to N); the medical image cloud server calculates a Hash Hash matrix H according to the R and sends the H to a medical image database; the medical image cloud server randomly distributes M multiplied by n random numbers to the users in M operating rooms according to the row unit R, the random number sequence set obtained by the users SUj in the operating rooms is recorded with Rj (j is more than or equal to 1 and less than or equal to M), and the second step is carried out;
secondly, SUj sends encrypted channel application information EBSj (Rj1, t) to the station BSj in the located area Cj through the control channel, where t is time.
Further, BSj collects all EBSj received at the time t and decrypts the received sequence string to obtain a series of secondary user information for R times; BSj marks each R times and then encrypts the R times again, and the label of each R times and the corresponding random number information form encryption information; BSj sends the encrypted information for each R times to the medical image database.
The medical image database decrypts the random number in the encrypted information, performs Hash calculation to obtain a Hash value of the random number, and after the Hash value of the random number is matched with the Hash value of H, the user corresponding to the label passes verification; the medical image database encrypts the verified label and the available frequency spectrum information in the Cj to form available channel information, and sends the available channel information to the BSj, and the medical image database deletes the matched Hash value in the H;
BSj allocates channels for the verified users in the operating room according to the available channel information;
BSj registering channel use information in a medical image database;
the formula for calculating the Hash matrix H by the medical image cloud server is as follows:
in the third step, the BSj marks each R times and then encrypts again, and the label of each R times and the corresponding random number information form encrypted information, which specifically comprises the following steps: BSj selects a label tagj1 to label the random number, and the encryption information is
When the Hash sequence of the users in the operating room is used completely or new users are added and the Hash sequence string needs to be applied to the medical image cloud server again, the medical image cloud server updates the Hash matrix and sends the Hash matrix to the medical image database.
As shown in fig. 3, the present invention provides a medical imaging system based on big data, comprising:
the medical image equipment 1 is used for acquiring medical images of users, segmenting and recombining the images and extracting and quantizing feature data; sending the medical image to an application program of a workstation;
the workstation 2 receives the medical image of the user by using an application program, completes standardization and structurization of data elements in the image, realizes segmentation and recombination of the image, extracts characteristic data and quantizes the characteristic data;
the medical image database 3 is used for receiving the medical images of the user collected by the medical image equipment and storing the medical images of the user into a database corresponding to the user;
the medical image database and shared data construction module 4 is used for storing the extracted feature data and the corresponding user medical image into a corresponding medical image database, manually supplementing mark information of the medical image at a later stage and storing the mark information into the medical image database to complete the updating and data sharing of the database;
the characteristic data and quantitative information identification module 5 is used for acquiring medical images of users in an operating room for medical imaging equipment, automatically or semi-automatically diagnosing cross enhancement marks to extract medical image file information to be matched, and identifying to obtain characteristic data and quantitative information;
and the image searching and matching module 6 is used for searching and matching the images in the medical image database according to the obtained feature data and the quantitative information.
The medical imaging device comprises an electronic computed tomography CT device or an X-ray machine.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When used in whole or in part, can be implemented in a computer program product that includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A matching processing method of a medical image system based on big data is characterized in that the matching processing method of the medical image system based on big data comprises the following steps:
the method comprises the following steps of firstly, collecting images, segmenting and recombining the images, and extracting and quantizing feature data; the medical imaging equipment acquires medical images of users in an operating room, the medical images are sent to an application program of the workstation, the application program receives the medical images of the users acquired by the medical imaging equipment, standardization and structurization of data elements in the images are completed, segmentation and recombination of the images are realized, and characteristic data are extracted and quantized;
step two, establishing a medical image database and sharing data; storing the extracted feature data and the corresponding medical image of the user into a corresponding medical image database, manually supplementing mark information of the medical image at the later stage and storing the mark information into the medical image database to complete the updating and data sharing of the database;
step three, the medical imaging equipment collects medical images of users in an operating room, automatically or semi-automatically diagnoses the cross enhancement mark to extract the information of medical image files to be matched, and characteristic data and quantitative information are obtained through recognition;
and step four, searching and matching the images in the medical image database according to the obtained characteristic data and the quantitative information.
2. The matching processing method for medical image system based on big data as claimed in claim 1, wherein the third step specifically comprises:
firstly, medical imaging equipment acquires medical images of users in an operating room;
secondly, reading patient information related to the medical image to be matched from a medical image database;
thirdly, extracting all information in the DICOM file header from the medical image to be matched;
fourthly, analyzing the extracted DICOM file header information, and performing case or series level identification on the medical image to be matched;
fifthly, preprocessing the medical image to be matched based on case or series hierarchical identification;
sixthly, analyzing the extracted DICOM file header information, and performing image level identification and preprocessing on the medical image to be matched;
and seventhly, extracting the medical image characteristics of the preprocessed medical image to be matched.
3. The matching processing method of the big data based medical image system as claimed in claim 1, wherein the step four, according to the obtained feature data and the quantitative information, searching and matching with the existing images in the medical image database, specifically comprises:
firstly, a medical image cloud server generates a random number sequence matrix R, wherein R is { Rmn }, (1 is less than or equal to m and less than or equal to N), and (1 is less than or equal to N and less than or equal to N); the medical image cloud server calculates a Hash Hash matrix H according to the R and sends the H to a medical image database; the medical image cloud server randomly distributes M multiplied by n random numbers to the users in M operating rooms according to the row unit R, the random number sequence set obtained by the users SUj in the operating rooms is recorded with Rj (j is more than or equal to 1 and less than or equal to M), and the second step is carried out;
secondly, SUj sends encrypted channel application information EBSj (Rj1, t) to the station BSj in the located area Cj through the control channel, where t is time.
4. The matching processing method of medical image system based on big data as claimed in claim 3, wherein BSj collects all EBSj received at time t and decrypts the received sequence string to obtain a series of secondary user information R times; BSj marks each R times and then encrypts the R times again, and the label of each R times and the corresponding random number information form encryption information; BSj sends the encrypted information for each R times to the medical image database.
5. The matching processing method of the medical image system based on big data as claimed in claim 4, wherein the medical image database decrypts the random number in the encrypted information, and performs Hash calculation to obtain the Hash value of the random number, and after the Hash value of the random number is matched with the Hash value of H, the user corresponding to the tag passes verification; the medical image database encrypts the verified label and the available frequency spectrum information in the Cj to form available channel information, and sends the available channel information to the BSj, and the medical image database deletes the matched Hash value in the H;
BSj allocates channels for the verified users in the operating room according to the available channel information;
BSj registering channel use information in a medical image database;
the formula for calculating the Hash matrix H by the medical image cloud server is as follows:
in the third step, the BSj marks each R times and then encrypts again, and the label of each R times and the corresponding random number information form encrypted information, which specifically comprises the following steps: BSjSelecting a tag tagj1 to label the random number, wherein the encryption information is
When the Hash sequence of the users in the operating room is used completely or new users are added and the Hash sequence string needs to be applied to the medical image cloud server again, the medical image cloud server updates the Hash matrix and sends the Hash matrix to the medical image database.
6. A big-data based medical imaging system implementing the matching processing method of the big-data based medical imaging system according to claim 1, wherein the big-data based medical imaging system comprises:
the medical image equipment is used for acquiring medical images of users, segmenting and recombining the images and extracting and quantizing feature data; sending the medical image to an application program of a workstation;
the workstation receives the medical image of a user by using an application program, completes standardization and structurization of data elements in the image, realizes segmentation and recombination of the image, extracts characteristic data and quantizes the characteristic data;
the medical image database receives a user medical image acquired by the medical image equipment and stores the user medical image into a database corresponding to the user;
the medical image database and shared data construction module is used for storing the extracted feature data and the corresponding user medical image into a corresponding medical image database, manually supplementing mark information of the medical image at the later stage and storing the mark information into the medical image database to complete the updating and data sharing of the database;
the characteristic data and quantitative information identification module is used for acquiring medical images of users in an operating room for medical imaging equipment, automatically or semi-automatically diagnosing cross enhancement marks to extract medical image file information to be matched, and identifying to obtain characteristic data and quantitative information;
and the image searching and matching module is used for searching and matching the images in the medical image database according to the obtained characteristic data and the quantitative information.
7. The big-data based medical imaging system of claim 6, wherein the medical imaging device comprises an electronic Computed Tomography (CT) device or an X-ray machine.
8. A matching processing program of a medical image system based on big data, which is applied to a computer, and is characterized in that the matching processing program of the medical image system based on big data realizes the matching processing method of the medical image system based on big data according to any one of claims 1 to 5.
9. A terminal, characterized in that the terminal is provided with a processor for realizing the matching processing method of the medical image system based on big data according to any claim 1-5.
10. A computer-readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the matching processing method of the big-data based medical imaging system according to any one of claims 1 to 5.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111540444A (en) * | 2020-05-13 | 2020-08-14 | 江苏医药职业学院 | Medicine image storage system based on thing networking |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102436539A (en) * | 2010-08-27 | 2012-05-02 | 通用电气公司 | System and method for analyzing and visualizing local clinical features |
| CN103116626A (en) * | 2013-01-31 | 2013-05-22 | 张粤新 | Matching method of medical image |
| CN104540130A (en) * | 2015-01-06 | 2015-04-22 | 西安电子科技大学 | Self coexistence channel selection method based on Hash matching in cognitive radio network |
-
2019
- 2019-12-19 CN CN201911314537.4A patent/CN111028228A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102436539A (en) * | 2010-08-27 | 2012-05-02 | 通用电气公司 | System and method for analyzing and visualizing local clinical features |
| CN103116626A (en) * | 2013-01-31 | 2013-05-22 | 张粤新 | Matching method of medical image |
| CN104540130A (en) * | 2015-01-06 | 2015-04-22 | 西安电子科技大学 | Self coexistence channel selection method based on Hash matching in cognitive radio network |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111540444A (en) * | 2020-05-13 | 2020-08-14 | 江苏医药职业学院 | Medicine image storage system based on thing networking |
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