Cryptosporidium identification embedded method, device and readable storage medium in instrument scanning to slice generation

Abstract

The application provides a method and device for embedding cryptospore identification in instrument scanning-to-slicing generation and a readable storage medium thereof. By embedding a unique encrypted identifier in the slicing data, the tracking, verification and protection of the data are realized. Specifically, the method includes generating original image data by instrument scanning, dividing and compressing the data into multiple slices, judging whether there is a cryptospore and generating a cryptospore identifier using an AES encryption algorithm, and embedding the identifier into the metadata of the slice file. The application also provides a cryptospore judgment method, which judges by calculating characteristic parameters such as the circumference, area, circularity and width-height ratio of the contour. Meanwhile, the application also includes a data integrity verification method. The application has beneficial effects such as data integrity and authenticity guarantee, efficient data tracing and management, automation and efficiency improvement, security enhancement and privacy compliance, and support for scientific research and clinical collaboration.

Description

Technical Field

[0001] The present invention relates to the technical field of medical image processing, and in particular to a method and device for embedding a cryptosporidiosis marker in the process of scanning a film with an instrument and generating a slice, and a readable storage medium thereof. Background Art

[0002] Factors such as cyberattacks and storage media failures pose the risk of data tampering, loss, or leakage. For example, in a telemedicine consultation scenario, malicious manipulation of imaging data during transmission could lead to incorrect diagnoses, seriously impacting patient care. Furthermore, the management and retrieval of large-scale medical imaging data is inefficient. Due to the lack of effective identification and indexing mechanisms, searching for specific case images often consumes significant time and effort, making it difficult to meet the demands of rapid clinical diagnosis and scientific research big data analysis.

[0003] Traditional manual testing methods for Cryptosporidiosis detection have numerous drawbacks. Manual testing relies on professionals observing samples under a microscope, which is not only inefficient and prone to fatigue, but also susceptible to subjective factors, making accuracy difficult to guarantee. For example, different testers may have different criteria for judging Cryptosporidiosis morphology, leading to biased test results. Furthermore, with the increasing number of Cryptosporidiosis infections, the burden of manual testing has become increasingly heavy, failing to meet the clinical demand for rapid and accurate testing.

[0004] To address data security and cryptospore detection issues, some technologies have introduced identifier embedding and encryption methods, but existing technologies still have significant shortcomings. Regarding identifier embedding, the embedding scheme lacks broad compatibility with different slice formats, which may result in the inability to properly embed or read identifier information in files of certain formats. For example, some identifier embedding methods designed for specific formats may fail to embed or affect the normal function of files in other formats. In the application of encryption technology, the selection and use of encryption algorithms are not perfect, making it difficult to fully guarantee the security of data at all stages. For example, some encryption algorithms cannot effectively prevent data from being cracked and tampered with when faced with complex attack methods. Summary of the Invention

[0005] The embodiments of the present invention provide a method, device, and readable storage medium for embedding cryptosporidiosis markers in the process of scanning an instrument and generating a slice. These methods address the shortcomings of current medical image processing technologies, such as poor data security, low management and retrieval efficiency, error-prone and inefficient manual cryptosporidiosis detection, insufficient compatibility of marker embedding, and imperfect encryption technology.

[0006] The core technology of this invention is to use the AES encryption algorithm to generate cryptosporidiosis identification and embed it into the slice file metadata during the process from instrument scanning to slice generation. It combines the cryptosporidiosis identification method based on contour feature parameters and various data integrity verification and secure transmission methods to improve data management, detection efficiency and security.

[0007] In a first aspect, the present invention provides a method for embedding a Cryptosporine marker during instrument scanning and slice generation, the method comprising the following steps:

[0008] S1. Scan the sample using a medical imaging scanner to generate raw image data;

[0009] S2, dividing the preprocessed raw image data into multiple slices, each slice representing a specific layer of the sample, and compressing the slice data as needed;

[0010] S3. Determine whether cryptospores are present. If so, use an encryption algorithm to generate a cryptospore identifier and embed the identifier into the metadata of the slice data.

[0011] Furthermore, in step S2, the step of embedding the Cryptosporine identifier into the metadata of the slice data includes:

[0012] Obtain accurate Cryptosporidium identification information and determine the extended field structure of metadata;

[0013] According to the determined extended field structure, the sorted Cryptosporidium identification information is sequentially embedded;

[0014] After embedding is complete, the metadata is verified for integrity.

[0015] Furthermore, in step S3, the encryption algorithm is the AES encryption algorithm, and the specific steps include:

[0016] Use the SHA-256 algorithm to calculate the hash value of the slice data;

[0017] Prepare for AES encryption, select CBC mode and PKCS7 padding, and generate the AES encryption key and initialization vector;

[0018] Use AES to encrypt the hash value, and use the encrypted result as the Cryptosporine identifier.

[0019] Furthermore, the S3 step also includes compatibility design for different slice formats:

[0020] For image slice formats, the extensibility of image metadata is utilized to embed Cryptosporidium identifiers in specific areas of the metadata;

[0021] For the document slice format, use the corresponding library to add custom attributes in the metadata of the document to embed the Cryptosporidium identifier;

[0022] For certain slice formats other than images and documents, the Cryptosporidium identifier is embedded in the metadata header information;

[0023] For different slice formats, select the corresponding software for testing, check whether the metadata is displayed normally, whether the identification information is complete, and whether the normal function of the file is affected, and adjust the embedding plan according to the test results.

[0024] Furthermore, step S3 includes the following steps:

[0025] Extract the target contour from the slice data and calculate its area, perimeter and circularity;

[0026] Filters the contours that meet the following criteria:

[0027] a. The area is between 100 and 500 pixels;

[0028] b. Circularity greater than 0.7;

[0029] Perform ellipse fitting on the filtered contour and calculate the aspect ratio;

[0030] If the aspect ratio is between 0.8 and 1.2, the outline is determined to be a cryptosporidium.

[0031] Furthermore, in step S3, the arcLength function is used to calculate the contour perimeter, the contour area is calculated using the contourArea function, and the circularity is calculated as 4πA / P 2 , where A is the contour area and P is the contour perimeter.

[0032] Furthermore, the method also includes step S4, respectively using a message authentication code combined with AES and / or a hash value and AES encryption method to verify data integrity, using AES encryption in the SSL / TLS protocol for data transmission, and using AES-based key exchange and binding to prevent man-in-the-middle attacks.

[0033] In a second aspect, the present invention provides a cryptosporidial marker embedding device for scanning an instrument and generating a slice, comprising:

[0034] The scanning module calls the medical imaging scanner to scan the sample and generate the original image data;

[0035] The segmentation module divides the preprocessed raw image data into multiple slices, each slice represents a specific layer of the sample, and compresses the slice data as needed;

[0036] The processing module determines whether cryptospores are present. If so, an encryption algorithm is used to generate a cryptospore identifier, and the identifier is embedded into the metadata of the slice data.

[0037] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the above-mentioned method for embedding cryptosporidioides markers in the process of scanning a slide and generating a slice.

[0038] In a fourth aspect, the present invention provides a readable storage medium having a computer program stored therein, the computer program including program code for controlling a process to execute the process, the process including the cryptosporidial marker embedding method in the above-mentioned instrument scanning to slice generation.

[0039] The main contributions and innovations of the present invention are as follows:

[0040] 1. Data integrity and authenticity assurance

[0041] Tamper-proof mechanism: A powerful tamper-proof system is built by combining AES encryption and digital signature technology. During data transmission and storage, any illegal tampering of the slide data will cause a hash value mismatch, triggering an alarm. This ensures that the integrity of the data is effectively maintained throughout the entire process, from generation to use, preventing the risk of malicious data tampering and providing a solid guarantee for the reliability of medical imaging data.

[0042] Authenticity Verification: Users can easily verify the originality and authenticity of data by recalculating the hash value and quickly comparing it with the embedded Cryptosporidium identifier. This feature is particularly important in the fields of medical diagnosis and scientific research, effectively avoiding misdiagnosis or biased scientific conclusions caused by data contamination, thereby improving the accuracy of medical and scientific research.

[0043] 2. Efficient data traceability and management

[0044] Unique Identification and Tracking: Each slide is embedded with a unique Cryptosporidium identifier, significantly improving the efficiency of retrieval of large-scale medical imaging data. This identifier allows users to quickly and accurately locate specific slide data, eliminating the need for tedious searches within massive amounts of data. This saves time and effort, and improves work efficiency.

[0045] Full-process auditing: This system records the generation, embedding, and verification of cryptosporidial identifiers, forming a complete audit trail. This allows for traceability of data operations, facilitating rapid identification of potential issues when they arise. This provides strong support for data security management and quality control, and helps standardize medical imaging data management.

[0046] 3. Automation and efficiency improvement

[0047] Automated Verification Process: The generation, embedding, and verification of identifiers are all automated through algorithms, significantly reducing manual intervention. In traditional data processing processes, manual operations are not only time-consuming and labor-intensive, but also prone to human error. The automated process of this invention not only improves diagnostic efficiency but also reduces the error rate. Furthermore, this process can be further combined with AI training models for deeper data analysis, providing strong support for intelligent diagnosis and analysis of medical images and promoting the development of medical technology.

[0048] 4. Security Enhancement and Privacy Compliance

[0049] End-to-end encryption: This invention utilizes the AES encryption algorithm during data transmission and storage, achieving end-to-end encryption protection. This measure effectively prevents unauthorized access or data leakage, ensuring patient privacy. In today's digital healthcare environment, patient privacy protection is crucial. This feature of this invention complies with relevant laws and regulations and meets the data security expectations of medical institutions and patients.

[0050] 5. Support scientific research and clinical collaboration

[0051] Trusted Data Sharing: Cryptosporine identification provides data with "identity proof," enabling cross-institutional sharing without the risk of data tampering. Data sharing between institutions is a common requirement in multi-center research collaborations, but ensuring data authenticity and integrity is often a challenge. This feature of the invention facilitates trusted data sharing, providing strong support for cross-institutional scientific research collaborations, helping to integrate resources and advance medical research.

[0052] Standardized data management: A unified identification embedding and verification process provides a standardized framework for the long-term archiving and reuse of medical imaging data. This not only reduces data management costs but also improves data utilization efficiency. In clinical practice, standardized data management helps medical institutions better manage and utilize patient imaging data, providing stronger support for clinical diagnosis and treatment. In scientific research, standardized data also facilitates data comparison and integration across different studies, promoting the translation and application of scientific research results.

[0053] The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below so that other features, objects, and advantages of the invention are more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0055] Figure 1 Flowchart of a method for embedding a Cryptosporine marker in the process of scanning a slide with an instrument and generating a slide according to an embodiment of the present invention;

[0056] Figure 2 FIG. 4 is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The implementations described in the following exemplary embodiments are not intended to represent all implementations consistent with one or more embodiments of this specification. Rather, they are merely examples of apparatuses and methods consistent with certain aspects of one or more embodiments of this specification, as detailed in the appended claims.

[0058] It should be noted that in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the method may include more or fewer steps than those described in this specification. In addition, a single step described in this specification may be broken down into multiple steps for description in other embodiments, and multiple steps described in this specification may be combined into a single step for description in other embodiments.

[0059] Example 1

[0060] The present invention aims to propose a method for embedding cryptosporidiosis markers in the process of scanning a slide and generating a slice. Specifically, Figure 1 , the method comprising:

[0061] S1. Instrument Scanning: Scanning a sample using a medical imaging scanner. For example, in clinical testing, a stool sample from a patient suspected of Cryptosporidium infection is scanned using a high-resolution microscope imaging scanner to obtain a microscopic image of the sample and generate raw image data. This raw image data contains information about various microscopic objects in the sample and serves as the basis for subsequent processing.

[0062] S2. Generate slice format

[0063] Slice generation: Segmenting preprocessed image data into multiple slices. For example, in digital pathology, whole slide image (WSI) processing uses specialized image processing software to segment high-resolution WSIs into multiple small slices according to specific rules. Each slice represents a specific layer of the specimen. These slices are formatted in common medical image formats, such as TIFF, to facilitate subsequent storage and processing.

[0064] Data compression: Slice data is compressed based on actual needs. For example, in telemedicine scenarios, to reduce data transmission bandwidth, lossless compression algorithms (such as the JPEG-LS algorithm) are used to compress TIFF-format slice data. This effectively reduces data volume and improves transmission efficiency while ensuring image quality.

[0065] The slice format can be common medical image formats such as TIFF, JPEG, PDF, DOCX, SDPC, and SVS. During the segmentation process, appropriate slice thickness and resolution are determined based on actual needs and sample characteristics to ensure that the slice data accurately reflects the sample's structural information. Slice data can be compressed as needed to reduce storage space and transmission bandwidth. An appropriate compression algorithm is selected to maximize compression efficiency while ensuring data quality. For example, for slices in TIFF format, compression algorithms such as LZW and JPEG can be used. For slices in PDF format, the built-in compression function can be used to compress image and text data.

[0066] S3. Determine whether there are cryptospores. If so, insert a cryptospore identifier. If not, the slice generation is completed.

[0067] (1) Cryptospore identification generation: Cryptospore identification is generated using an encryption algorithm (AES). First, the hash value of the slice data is calculated. Taking a specific slice data as an example, the SHA-256 algorithm is used to calculate it to obtain a unique hash value. Then, the AES encryption mode is selected as CBC mode, the padding method is PKCS7 padding, and a 128-bit AES encryption key and a 16-byte initialization vector (IV) are generated. The calculated hash value is used as plaintext and encrypted using the AES key and IV generated above. Finally, the encrypted binary data is converted into a hexadecimal string to obtain the cryptospore identification.

[0068] (2) Logo insertion

[0069] Data preparation: Extract relevant Cryptosporidium data from laboratory test reports, such as the species, testing date, and sample source. Assuming the test report lists the species as "Cryptosporidium parvum," the testing date as "2024-10-01," and the sample source as "fecal sample from patient Zhang San," format this data to meet embedding requirements. Simultaneously, analyze the metadata structure of the slice file (e.g., TIFF format) and determine that the Cryptosporidium identification information should be embedded as an extended field in its custom tag area.

[0070] Embedding phase: Embed the organized Cryptosporidium identification information sequentially according to the determined expanded field structure. First, embed the key information "Cryptosporidium parvum," followed by information such as the detection time and sample source.

[0071] Verification and preservation: After embedding, the metadata is verified for integrity. The embedded information length and data format are checked to ensure the completeness of the Cryptosporidium identification information, without omissions or errors. Once verified, the slice file containing the Cryptosporidium identification is saved.

[0072] (3) Compatibility design for different slice formats

[0073] 1) Embedded solution design

[0074] Image slice formats (such as TIFF and JPEG): Using the TIFF format as an example, use its custom tag area to store Cryptosporidium identification information. According to the TIFF format specifications, write the Cryptosporidium identification data under a specific tag number.

[0075] Document slicing formats (such as PDF and DOCX): For PDF documents, use the PyPDF2 library to add custom attributes to the document's metadata to embed the Cryptosporidium identifier. For example, use the PyPDF2 library's related functions to add a new key-value pair to the PDF document's " / Metadata" dictionary, with the key being "CryptosporidiumIdentifier" and the value being the corresponding Cryptosporidium identifier information.

[0076] Specific slice formats (such as SDPC and SVS): For SDPC files, the cryptosporin identifier is embedded in the metadata header. According to the SDPC file format definition, the cryptosporin identifier data is written to a specific location in the header.

[0077] 2) Compatibility testing and verification

[0078] Test tool selection: Choose the corresponding mainstream software for different slice formats. For image slicing (such as TIFF format), use software such as Adobe Photoshop and IrfanView; for PDF document slicing, use Adobe Acrobat; for SDPC files, use Digital Pathology Slide Viewing Software.

[0079] Test content: Open a slice file with an embedded Cryptosporidium logo and check whether the metadata is displayed properly, the logo information is complete, and whether the normal function of the file is affected. For example, open a TIFF image with an embedded Cryptosporidium logo in Adobe Photoshop to check whether the Cryptosporidium logo information is correctly displayed in the metadata and whether the image display quality is affected. Open a PDF document with an embedded logo in Adobe Acrobat to check whether the document editing function is normal and whether the logo information in the metadata is displayed completely.

[0080] Verification Results: Any issues encountered during testing were documented. For example, if a certain imaging software failed to recognize the custom metadata tags, the tag format would need to be modified or a more universal metadata storage method would be used. After multiple tests and adjustments, the Cryptosporine ID embedding solution was ensured to be compatible with various mainstream slice formats and related software.

[0081] In this embodiment, in step S3, the specific steps of the cryptospore identification method are as follows:

[0082] Contour detection: Use functions in the OpenCV image processing library (a cross-platform computer vision and machine learning software library released under the Apache 2.0 license (open source)) to extract the contours of target objects from an image. For example, for an image of a slide containing Cryptosporidium, use OpenCV's cv2.findContours function to extract all contours in the image by setting appropriate thresholds and contour search modes.

[0083] Calculate the perimeter and area of ​​the contour: Use the cv2.arcLength function to calculate the perimeter of the contour, which is based on the cumulative distance between adjacent points on the contour; use the cv2.contourArea function to calculate the area of ​​the contour, which calculates the area enclosed by the contour based on Green's formula.

[0084] Screening contours: Screen contours that meet the requirements based on the conditions of circularity greater than 0.7 and area greater than 100 and less than 500. The circularity calculation formula is: 4πA / P 2, where A is the contour area and P is the contour perimeter. By calculating the circularity and area of ​​each contour, the contours that meet the conditions are screened out.

[0085] Fit an ellipse and calculate the aspect ratio of the ellipse: Fit an ellipse to the filtered contour, use the cv2.fitEllipse function to get the fitting result of the ellipse, and then calculate the aspect ratio of the ellipse, that is, the ratio of the minor axis length to the major axis length.

[0086] Determining whether it is likely to be a Cryptosporidium: The calculated characteristic parameters are compared with pre-set thresholds. For example, for area, the calculation is performed to determine whether it is within the range of 100-500; for circularity, whether it is greater than 0.7; and for aspect ratio, whether it is between 0.8-1.2. Only when all characteristic parameters meet the corresponding threshold conditions is the contour considered to be similar in shape to Cryptosporidium. Through multi-dimensional quantitative evaluation, contours of objects similar in shape to Cryptosporidium are screened, providing a basis for auxiliary identification of Cryptosporidium.

[0087] Preferably, the applicability and limitations of the threshold values ​​of each characteristic parameter in different situations are as follows:

[0088] (1) Different hosts

[0089] 1) Applicability: The previously established area range (greater than 100 and less than 500), circularity threshold (greater than 0.7), and aspect ratio range (0.8-1.2) have demonstrated limited applicability in most common hosts, such as humans, cattle, sheep, and other mammals. This is because Cryptosporidium maintains relatively stable basic morphological characteristics throughout its evolution. Regardless of the common host in which it parasitizes, it maintains similar cellular structures and functions to complete its life cycle, resulting in a relatively concentrated range of morphological parameters. For example, in long-term testing of human and cattle fecal samples, the majority of Cryptosporidium morphological parameters fell within the aforementioned threshold ranges, making the threshold-based approach effective for identifying Cryptosporidium.

[0090] 2) Limitations: These thresholds may need to be adjusted when considering specific hosts. Different host physiological environments, immune responses, and other factors can influence the growth and morphology of Cryptosporidium. For example, the body temperature and digestive system pH of some avian hosts differ significantly from those of mammals, potentially enabling Cryptosporidium to adapt to morphological changes within these hosts. Studies have found that Cryptosporidium in some avian hosts may be slightly smaller and less circular than in mammals.

[0091] 3) Adjustment Methods: For specific hosts, a large number of new samples need to be collected to measure morphological parameters and conduct statistical analysis. Stratified sampling can be used to obtain samples from different types of specific hosts to ensure representativeness. Statistical analysis should be performed on the collected data, such as calculating new means and standard deviations. Based on the analysis results, the threshold range applicable to the specific host should be determined. For example, if new statistical results show that the mean area of ​​Cryptosporidium in a certain type of bird host is 80 and the standard deviation is 20, then the lower limit of the area threshold can be adjusted to 60 (mean - standard deviation) and the upper limit to 100 (mean + standard deviation). At the same time, these two thresholds can be adjusted accordingly based on changes in circularity and aspect ratio.

[0092] (2) Different stages of infection

[0093] 1) Applicability: The above thresholds are generally effective in identifying Cryptosporidium in the early stages of infection. This is because Cryptosporidium is primarily in the process of invading host cells and beginning to multiply. Its morphology has not yet undergone significant changes, retaining relatively typical features. Therefore, the established thresholds are suitable for identification. For example, in samples collected within 1-2 days after infection, the morphology of Cryptosporidium generally falls within the established threshold range.

[0094] 2) Limitations: As the infection enters the middle and late stages, cryptospores multiply rapidly within the host, potentially resulting in the appearance of some morphologically abnormal individuals. Furthermore, the host's immune response can affect the morphology of cryptospores. For example, under attack by immune cells, the cell wall of cryptospores may be damaged, resulting in morphological changes. At this point, the original threshold may not accurately identify all cryptospores. For example, in the late stages of infection, some cryptospores may appear larger and less circular, exceeding the original threshold range.

[0095] 3) Adjustment Methods: A dynamic threshold adjustment mechanism should be established for different infection stages. Samples should be collected regularly at different infection stages to analyze the temporal changes in Cryptosporidial morphological parameters. Methods such as time series analysis can be used to predict the changing trends of Cryptosporidial morphological parameters at different infection stages, allowing for preemptive threshold adjustments. For example, if analysis reveals that the area of ​​Cryptosporidial morphology increases by an average of 20% five days after infection, the upper limit of the area threshold could be increased by 20% for samples tested five days after infection and beyond. Simultaneously, these thresholds could be adjusted in tandem based on changes in circularity and aspect ratio.

[0096] (3) Different testing conditions

[0097] 1) Applicability: The threshold value can accurately determine the cryptospores under standard detection conditions, such as appropriate temperature, pH value, and correct calibration of detection instruments. Because these standard conditions can ensure that the morphology of cryptospores remains in a natural state, the accuracy of the detection instrument can also ensure the reliability of the measurement data. For example, when using a calibrated high-resolution microscope, the threshold value can effectively function under the condition of a temperature of 25℃ and a pH value of 7.2.

[0098] 2) Limitations: The accuracy of the threshold value may be affected when the detection conditions change. Differences in the precision of detection instruments can lead to errors in measurement data, and changes in environmental temperature and pH value can cause changes in the morphology of cryptospores. For example, when using a low-precision microscope, there may be large errors in the measured area and aspect ratio of cryptospores; under high temperature or extreme pH value conditions, cryptospores may appear to be shrunk or swollen, causing morphological parameters to deviate from the normal range.

[0099] 3) Adjustment method: For different detection conditions, a correlation model between detection conditions and threshold values needs to be established. Analyze the data collected under different detection conditions to find the relationship between changes in detection conditions and changes in cryptospore morphology parameters. For example, through experiments, it is found that when the detection temperature increases by 10℃, the area of cryptospores will decrease by 5%, so under high temperature detection conditions, the area threshold value can be adjusted according to the magnitude of temperature increase. At the same time, for detection instruments of different precision, establish the relationship between instrument calibration coefficients and threshold value adjustment, and modify the threshold value according to the calibration of the instrument.

[0100] S4, data integrity verification process

[0101] Message Authentication Code (MAC) combined with AES: The sender uses the AES key and the data to be verified to generate a message authentication code (MAC) through the HMAC-AES algorithm. After receiving the data and MAC, the receiver uses the same AES key and algorithm to calculate the MAC and compares it with the received MAC. If they are the same, the data has not been tampered with; otherwise, the data may be counterfeit. For example, in a hospital's internal image data transmission system, the server acts as the sender, uses the AES key and the slice data to be transmitted to calculate the MAC, and sends the data and MAC to the terminal device (such as a doctor's workstation), which receives the MAC and performs verification to ensure the integrity of the data.

[0102] Hash value and AES encryption: The sender first calculates the hash value of the data (such as using the SHA-256 algorithm) and then encrypts the hash value using the AES encryption key. After receiving the encrypted hash value, the receiver decrypts it using the AES key, recalculates the data's hash value, and compares the results. If they match, the data has not been forged; otherwise, there is a problem with the data. In medical imaging data storage scenarios, when storing slice data, the storage device first calculates the data's hash value and encrypts it for storage. When reading the data, the hash value is decrypted and recalculated for comparison to verify the data's authenticity.

[0103] Preventing man-in-the-middle attacks - AES encryption in the SSL / TLS protocol: The server sends a digital certificate to the client, and the client verifies the certificate's validity, including whether it's issued by a trusted certificate authority (CA) and whether it's expired. The client and server negotiate a shared AES key through a handshake protocol, and all subsequent data transmissions are encrypted using this AES key. For example, in telemedicine data transmission, when a doctor's client device establishes a connection with a hospital's server, key negotiation and data encryption are performed using the SSL / TLS protocol to prevent data tampering by a man-in-the-middle.

[0104] AES-based key exchange and binding: During initial communication, the client and server exchange AES keys securely (e.g., out-of-band, i.e., through an independent secure channel). For example, the AES key can be delivered to the other party via offline email and bound to the other party's identity information (e.g., the server's domain name, the client's unique identifier, etc.). During communication, the corresponding AES key is queried based on the server's domain name and compared with the received key. In practice, this method can be used to prevent man-in-the-middle attacks and ensure data security when sharing imaging data between different hospital departments.

[0105] Example 2

[0106] Based on the same concept, the present invention also proposes a cryptosporidial marker embedding device in the process of scanning a slide and generating a slice, comprising:

[0107] The scanning module calls the medical imaging scanner to scan the sample and generate the original image data;

[0108] The segmentation module divides the preprocessed raw image data into multiple slices, each slice represents a specific layer of the sample, and compresses the slice data as needed;

[0109] The processing module determines whether cryptospores are present. If so, an encryption algorithm is used to generate a cryptospore identifier, and the identifier is embedded into the metadata of the slice data.

[0110] Example 3

[0111] This embodiment also provides an electronic device, referring to Figure 2 , includes a memory 404 and a processor 402, wherein the memory 404 stores a computer program, and the processor 402 is configured to run the computer program to perform the steps in any of the above method embodiments.

[0112] Specifically, the processor 402 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits for implementing the embodiments of the present invention.

[0113] Memory 404 may include a large-capacity memory 404 for data or instructions. By way of example, and not limitation, memory 404 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 404 may include removable or non-removable (or fixed) media. Where appropriate, memory 404 may be internal or external to the data processing device. In certain embodiments, memory 404 is non-volatile memory. In certain embodiments, memory 404 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. In appropriate circumstances, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM may be a fast page mode dynamic random access memory 404 (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0114] The memory 404 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 402 .

[0115] The processor 402 reads and executes computer program instructions stored in the memory 404 to implement any one of the cryptosporidioides identifier embedding methods in the process of scanning a slide and generating a slice in the above-mentioned embodiments.

[0116] Optionally, the electronic device may further include a transmission device 406 and an input / output device 408 , wherein the transmission device 406 is connected to the processor 402 , and the input / output device 408 is connected to the processor 402 .

[0117] Transmission device 406 can be used to receive or transmit data via a network. Specific examples of such networks may include wired or wireless networks provided by the electronic device's communications provider. In one embodiment, the transmission device includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, transmission device 406 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0118] The input / output device 408 is used to input or output information.

[0119] Example 4

[0120] This embodiment also provides a readable storage medium, which stores a computer program. The computer program includes program code for controlling a process to execute the process, including the method for embedding a cryptosporidial marker in the process from instrument scanning to slice generation according to the first embodiment.

[0121] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be repeated here.

[0122] In general, various embodiments may be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects of the invention may be implemented in hardware, while other aspects may be implemented in firmware or software executed by a controller, microprocessor, or other computing device, but the invention is not limited thereto. Although various aspects of the invention may be shown and described as block diagrams, flow charts, or using some other graphical representation, it should be understood that, as non-limiting examples, the blocks, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or a controller or other computing device, or some combination thereof.

[0123] Embodiments of the application can be implemented by computer software executable by a data processor of the mobile device such as in the processor entity, or by hardware, or by a combination of software and hardware. Computer software or program, also called program product, including software routines, applets and / or macros, can be stored in any apparatus-readable data storage medium and they include program instructions to implement certain tasks. The program product can include one or more computer-executable components such as by way of example scripts, programs, routines, objects, components, data structures, procedures, subroutines, etc., for implementing one or more embodiments of the application. The computer-executable components can be stored in computer-readable storage medium. The components can be executed by one or more computers or computing devices. Figure 1

[0124] Those skilled in the art should understand that each technical feature of the above-mentioned embodiments can be combined with any other technical feature, and in order to make the description simple, each technical feature of the above-mentioned embodiments is not described in all possible combinations, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present disclosure.

[0125] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as the limitation of the scope of the present application. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.​

Claims

1. A method for embedding cryptosporidiosis markers in the process of scanning a slide with an instrument and generating a slice, characterized in that: The following steps are involved: S1. Scan the sample using a medical imaging scanner to generate raw image data; S2, dividing the preprocessed raw image data into multiple slices, each slice representing a specific layer of the sample, and compressing the slice data as needed; S3. Determine whether cryptospores are present. If so, generate a cryptospore identifier using an encryption algorithm and embed the identifier into the metadata of the slice data. The S3 step also includes compatibility designs for different slice formats: For image slice formats, the extensibility of image metadata is utilized to embed Cryptosporidium identifiers in specific areas of the metadata; For the document slice format, use the corresponding library to add custom attributes in the metadata of the document to embed the Cryptosporidium identifier; For certain slice formats other than images and documents, the Cryptosporidium identifier is embedded in the metadata header information; For different slice formats, select the corresponding software for testing to check whether the metadata is displayed normally, whether the identification information is complete, and whether the normal function of the file is affected. Adjust the embedding plan based on the test results; The steps for determining whether there are cryptospores are as follows: Extract the target contour from the slice data and calculate its area, perimeter and circularity; Filters the contours that meet the following criteria: a. The area is between 100 and 500 pixels; b. Circularity greater than 0.7; Perform ellipse fitting on the filtered contour and calculate the aspect ratio; If the aspect ratio is between 0.8 and 1.2, the outline is determined to be a cryptosporidium.

2. The method for embedding a Cryptosporine marker in the process of scanning a slide with an instrument and generating a slice according to claim 1, wherein: In step S3, the steps of embedding the Cryptosporidium identifier into the metadata of the slice data include: Obtain accurate Cryptosporidium identification information and determine the extended field structure of metadata; According to the determined extended field structure, the sorted Cryptosporidium identification information is sequentially embedded; After embedding is complete, the metadata is verified for integrity.

3. The method for embedding a Cryptosporine marker in the process of scanning a slide with an instrument and generating a slice according to claim 1, wherein: In step S3, the encryption algorithm is the AES encryption algorithm, and the specific steps include: Use the SHA-256 algorithm to calculate the hash value of the slice data; Prepare for AES encryption, select CBC mode and PKCS7 padding, and generate the AES encryption key and initialization vector; Use AES to encrypt the hash value, and use the encrypted result as the Cryptosporine identifier.

4. The method for embedding a Cryptosporine marker in the process of scanning a slide with an instrument and generating a slice according to claim 1, wherein: In step S3, the arcLength function is used to calculate the contour perimeter, the contour area is calculated using the contourArea function, and the circularity is calculated using the formula 4πA / P 2 , where A is the contour area and P is the contour perimeter.

5. The method for embedding a Cryptosporine marker in the process of scanning a slide with an instrument and generating a slice according to any one of claims 1 to 4, characterized in that: The method further includes step S4, wherein the data integrity is verified by using a message authentication code combined with AES and / or a hash value and AES encryption method, the data is transmitted using AES encryption in the SSL / TLS protocol, and AES-based key exchange and binding are used to prevent man-in-the-middle attacks.

6. A cryptosporidial marker embedding device for scanning a slide and generating a slice, characterized in that: include: The scanning module calls the medical imaging scanner to scan the sample and generate the original image data; The segmentation module divides the preprocessed raw image data into multiple slices, each slice represents a specific layer of the sample, and compresses the slice data as needed; The processing module determines whether cryptospores are present. If so, an encryption algorithm is used to generate a cryptospore identifier, and the identifier is embedded into the metadata of the slice data. The processing module also includes compatibility designs for different slice formats: For image slice formats, the extensibility of image metadata is utilized to embed Cryptosporidium identifiers in specific areas of the metadata; For the document slice format, use the corresponding library to add custom attributes in the metadata of the document to embed the Cryptosporidium identifier; For certain slice formats other than images and documents, the Cryptosporidium identifier is embedded in the metadata header information; For different slice formats, select the corresponding software for testing to check whether the metadata is displayed normally, whether the identification information is complete, and whether the normal function of the file is affected. Adjust the embedding plan based on the test results; The steps for determining whether there are cryptospores are as follows: Extract the target contour from the slice data and calculate its area, perimeter and circularity; Filters the contours that meet the following criteria: a. The area is between 100 and 500 pixels; b. Circularity greater than 0.7; Perform ellipse fitting on the filtered contour and calculate the aspect ratio; If the aspect ratio is between 0.8 and 1.2, the outline is determined to be a cryptosporidium.

7. An electronic device comprising a memory and a processor, characterized in that: The memory stores a computer program, and the processor is configured to run the computer program to execute the method for embedding a cryptosporidial marker in the process of scanning a slide with an instrument and generating a slide according to any one of claims 1 to 5.

8. A readable storage medium, characterized in that: The readable storage medium stores a computer program, which includes program code for controlling a process to execute a process, wherein the process includes the method for embedding a cryptosporidioides marker in the process of scanning a slide with an instrument and generating a slice according to any one of claims 1 to 5.

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