A medical image transmission method and system based on big data

Abstract

The present invention relates to the technical field of medical image transmission methods, and more specifically, to a medical image transmission method and system based on big data, comprising the following steps: obtaining a fragment sequence; obtaining an initial correlation matrix based on the distribution density gradient of each two adjacent fragments; obtaining a normalized correlation matrix; generating an increasing weight matrix through multi-level projection aggregation to obtain increasing weights for each pair of fragments; based on the increasing weight matrix, applying a differential adaptive filtering method to obtain a differential optimization matrix; based on the differential optimization matrix, applying a power-law distribution model to generate a final allocation matrix to obtain the transmission priority of each fragment; determining the transmission order of the fragments based on the final allocation matrix to achieve orderly transmission of medical image data, and based on the density changes of data information within the fragments, being able to quickly identify areas containing key diagnostic information and assign them a higher priority, thereby giving priority to important data during data transmission.

Description

Technical Field

[0001] The present invention relates to the technical field of medical image transmission methods, and more specifically, to a medical image transmission method and system based on big data. Background Art

[0002] With advances in medical technology and improvements in data storage capabilities, the application of medical imaging data is becoming increasingly widespread, encompassing everything from routine health checks to complex medical diagnosis. In particular, the vast amounts of data generated in imaging fields like radiography, ultrasound, and MRI provide detailed information support for doctors' diagnoses. However, medical image data is characterized by its large volume, high density, and demanding transmission quality. Efficiently and reliably transmitting this large amount of imaging data is a major challenge facing existing technologies.

[0003] Most existing medical image transmission methods rely directly on data compression and sequential transmission, lacking a refined distinction between the information density of different regions within the image. The usual approach is to compress the image as a whole and then transmit it directly, or to divide the image data into equal segments and then transmit them sequentially. However, different regions of a medical image often have significant density differences. Regions in the image that contain rich tissue information are generally more valuable for diagnosis, while other background regions have lower information density and are relatively less important. Existing technologies lack effective means to distinguish and process these regions, resulting in the inability to guarantee the efficiency and order of critical data transmission, affecting overall transmission efficiency and the real-time nature of diagnosis.

[0004] Furthermore, existing methods generally rely on a static transmission order for image transmission, lacking dynamic priority management and scheduling mechanisms. When network bandwidth or storage space is limited, critical image data and background data are often not clearly prioritized, and all data is processed identically. The biggest drawback of this static transmission mechanism is that it fails to prioritize critical data, potentially preventing doctors from receiving important imaging information in a timely manner, impacting diagnostic effectiveness and efficiency.

[0005] Traditional methods also have shortcomings in controlling data transmission quality. Conventional image compression transmission technologies lack dynamic adaptability to metrics such as link stability and packet loss rates during data transmission. Traditional methods lack effective remediation and optimization mechanisms for packet loss or data delays during transmission. This leads to unstable medical image quality during transmission, potentially causing image quality loss during data reconstruction, and impacting subsequent clinical applications and analysis.

[0006] In summary, existing medical image transmission technologies still have significant room for improvement in areas such as prioritizing critical data transmission, dynamic data scheduling, and ensuring image reconstruction quality, making it difficult to meet the growing demand for medical data transmission. There is an urgent need for an image transmission method that can identify information density differences between different regions in an image, dynamically adjust transmission priorities, and improve image reconstruction quality. Summary of the Invention

[0007] In response to the shortcomings of existing medical image transmission methods, the present invention proposes a medical image transmission method and system based on big data, which mainly solves the following technical problems: how to identify high-density information areas during medical image transmission and give priority processing to improve data transmission efficiency and image reconstruction quality; how to adapt to different network transmission conditions through dynamic scheduling and optimization algorithms to make the data transmission process more stable and reliable; how to balance data compression and the integrity of key data during transmission to ensure the accuracy and real-time performance of image transmission.

[0008] The present invention provides a medical image transmission method based on big data, comprising the following steps:

[0009] Acquire a data information value sequence of a medical image from a medical device, divide the data information value sequence into a plurality of segments of equal length, and obtain the segment sequence;

[0010] Based on each segment in the segment sequence, determining a distribution density gradient of each segment, wherein the distribution density gradient is a rate of change of data information density within the segment, and obtaining an initial correlation matrix based on the distribution density gradients of every two adjacent segments;

[0011] Performing dynamic weight normalization and offset adjustment on the initial correlation matrix to obtain a normalized correlation matrix;

[0012] Based on the normalized correlation matrix, an increasing weight matrix is ​​generated through multi-level projection aggregation to obtain an increasing weight for each pair of segments;

[0013] Based on the incremental weight matrix, applying a difference adaptive filtering method to obtain a difference optimization matrix;

[0014] Based on the difference optimization matrix, a power law distribution model is applied to generate a final allocation matrix to obtain the transmission priority of each fragment;

[0015] The transmission order of the segments is determined based on the final allocation matrix to achieve orderly transmission of the medical image data.

[0016] Preferably, the distribution density gradient is used to characterize the density change of the information value in each segment, wherein the distribution density gradient G of each segment is i For fragment Si The rate of change of density is expressed as:

[0017]

[0018] Among them G i For fragment S i The distribution density gradient, s k represents the kth data information value, and L represents the length of the segment.

[0019] Preferably, the correlation in the initial correlation matrix is ​​obtained based on the distribution density gradient difference of adjacent segments, and the correlation is determined by the following formula:

[0020]

[0021] where r ij For fragment S i and S j The initial correlation degree, G i and G j Segment S i and S j The distribution density gradient of , ∈ is a small constant to prevent division by zero.

[0022] Preferably, the normalization algorithm comprises the following steps:

[0023] For the initial correlation r ij Perform maximum and minimum normalization and offset adjustment, and the normalized correlation matrix element N(r ij ) is obtained by the following formula:

[0024]

[0025] Where N(r ij ) is the normalized correlation degree, min(r ij ) and max(r ij ) are the minimum and maximum values ​​of the correlation degree, δ is the offset constant to prevent division by zero, β is the normalized amplitude adjustment weight, and γ is the offset adjustment ratio.

[0026] Preferably, the normalized correlation matrix is ​​aggregated by multi-level projection to generate an increasing weight matrix, and the increasing weight is obtained by the following formula:

[0027]

[0028] Where W ij Represents fragment S i and S j The increasing weight, r ij For fragment S i and Sj The correlation degree, P ik Represents fragment S i With fragment S k The geometric projection of ζ is the adjustment factor for adjusting the uniformity of the projection weight, η is the incremental weight adjustment parameter, and n is the number of projection iterations.

[0029] Preferably, each element of the difference optimization matrix is ​​obtained by the following formula:

[0030]

[0031] Among them F ij represents the difference optimization value after filtering, W ij For fragment S i and S j , λ is the adaptive adjustment coefficient, κ is the filter gradient adjustment coefficient, and θ is the difference threshold.

[0032] Preferably, the power law distribution model is obtained by applying the following formula:

[0033]

[0034] where q ij Represents fragment S i and fragment S j The transmission priority, F ij is the element of the difference optimization matrix, α is the scale factor of the power law distribution, and ω is the distribution weight exponent.

[0035] Preferably, the data processing flow of the distribution density gradient calculation step, the initial correlation matrix calculation step, the dynamic weight normalization and offset adjustment step, the multi-level projection aggregation step, the difference adaptive filtering step and the power-law distribution model generation final distribution matrix step forms a closed loop in sequence to ensure the validity and priority control of the medical image transmission data stream.

[0036] A medical image transmission system based on big data includes a processing module and a communication module. The processing module is used to implement the steps described, and the communication module is used to transmit the data information value sequence between the medical device and the receiving module.

[0037] Preferably, the communication module includes a wireless communication module for transmitting the data information value sequence in a wireless manner to achieve data security and privacy protection.

[0038] The invention has the following beneficial effects:

[0039] The big data-based medical image transmission method and system proposed in the present invention have achieved significant improvements in transmission efficiency and data integrity through a series of innovative algorithms and scheduling mechanisms. First, the present invention effectively identifies high-information-density areas in medical images through distribution density gradient calculations. The distribution density gradient algorithm is based on the changes in the density of data information within the fragment. It can quickly identify areas containing key diagnostic information and assign them higher priority, thereby giving priority to important data during data transmission. This algorithm effectively solves the problem of traditional methods where dense image information areas fail to be transmitted first, greatly improving the transmission speed of important data.

[0040] In terms of transmission order management, this invention uses multi-level projection aggregation to generate an ascending weight matrix, accumulating the transmission priority of each image segment layer by layer based on density differences. This multi-level aggregation of ascending weights optimizes the weight distribution of different segments, ensuring that high-density information segments consistently receive higher transmission weights, ensuring scientific and rational data transmission. This layer-by-layer cumulative weight aggregation method not only ensures the priority of important data segments in transmission, but also effectively avoids the waste of network bandwidth resources.

[0041] Furthermore, the present invention smoothes the incremental weight matrix at the MAC layer through differential adaptive filtering, forming a stable differential optimization matrix. The adaptive filtering algorithm dynamically adjusts weight allocation based on the real-time state of data transmission. Through adaptive adjustment factors and gradient control, it ensures stability in high-density information areas during transmission and reduces fluctuations in data transmission. The introduction of this filtering mechanism not only improves image transmission stability but also effectively resolves the conflict between transmission efficiency and image quality encountered in traditional algorithms.

[0042] In terms of transmission priority control, this invention utilizes a power-law distribution model to further enhance the transmission priority of high-density information fragments, forming the final transmission order. This model significantly differentiates the priority of critical data fragments from common data fragments, ensuring the prioritized transmission of critical image data even in complex network environments with limited bandwidth. The introduction of the power-law distribution model ensures that high-density data fragments maintain high transmission stability despite network load fluctuations, further improving transmission reliability.

[0043] The present invention realizes the intelligent and dynamic management of medical image data in a large data transmission environment through a series of algorithmic steps such as distribution density gradient identification, increasing weight matrix, multi-level aggregation, adaptive filtering and power-law distribution. A complete transmission management system is formed through close collaboration and complementarity between the various steps. The efficient identification of distribution density gradients and the multi-level accumulation of increasing weights form the basis for priority sorting, the adaptive filtering balances the transmission priority and data stability, and the power-law distribution ultimately widens the data priority to ensure the priority transmission of critical data. This set of collaboratively optimized transmission mechanisms not only solves the contradiction between traditional methods in transmission efficiency and image quality, but also realizes the efficient transmission of large-scale medical image data, provides technical support for clinical decision-making and remote diagnosis, and has significant application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 Flow chart of the method of the present invention.

[0045] Figure 2 It is the system logic block diagram of the present invention.

[0046] Figure 3 It is a logic block diagram of the processing module of the present invention.

[0047] Figure 4 It is a logic block diagram of the communication module of the present invention. DETAILED DESCRIPTION

[0048] To further illustrate the technical means and effects employed by the present invention to achieve its intended objectives, the following detailed description, along with the accompanying drawings and preferred embodiments, includes a detailed description of the specific implementations, structures, features, and effects thereof. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0049] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0050] Please refer to Figure 1-4 The present invention provides a medical image transmission method and system based on big data, aiming to improve the efficiency and priority management of medical images during data transmission, and realize the orderly and rapid transmission of medical image data in a big data environment.

[0051] The present invention discloses a medical image transmission method based on big data, which specifically includes the following steps:

[0052] First, a data information value sequence of the medical image is obtained from the medical device. The data information value sequence is image data presented in numerical form, usually consisting of the grayscale or color value of each pixel. To facilitate subsequent data processing and compression, the data information value sequence is divided into several equal-length segments to form a segment sequence. Preferably, the length of the segment can be determined according to the resolution and transmission requirements of the image. For example, for high-resolution images, the segment length can be 512 information values; for images with general resolution, the segment length can be set to 256 information values, so that the stability and consistency of data transmission can still be guaranteed under conditions of less bandwidth. This step ensures that the image data is structured and segmented, which facilitates further analysis of data density and transmission priority by subsequent processing modules.

[0053] After the fragment sequence is generated, the distribution density gradient of each fragment is further determined based on the data content of each fragment. The distribution density gradient is a measure of the rate of change of data within a fragment, which is used to reflect the density of data information within the fragment. This density gradient is estimated by the rate of change of information values ​​in each fragment. For example, in a fragment, if the difference between adjacent data information values ​​is large, indicating that the information within the fragment has changed dramatically, the distribution density gradient value of the fragment will be high, thus reflecting the high-information area in the image. In this way, the distribution density gradient obtained can not only provide a basis for subsequent priority sorting, but also identify areas with high information density in the image.

[0054] Next, an initial correlation matrix is ​​calculated based on the distribution density gradient of adjacent segments. This initial correlation matrix is ​​used to measure the correlation between adjacent segments and to determine whether to prioritize certain highly correlated segments during image transmission.

[0055] Preferably, in one embodiment of the present invention, a step of obtaining a sequence of data information values ​​and dividing the sequence into segments of equal length is described. The selection of the segment length directly affects the processing efficiency of the image data and the subsequent compression effect, and is usually adjusted according to the actual scenario. When transmitting large-size, high-resolution medical images, the segment length is selected to be longer, such as 512 or 1024 information values, to reduce the number of segmentations during transmission and reduce system overhead; for smaller images or when lower resolution is required, the segment length can be shortened to 128 or 256 information values. This flexible segment division method ensures that image data can be efficiently segmented and transmitted under different transmission bandwidth conditions.

[0056] Preferably, in one embodiment of the present invention, the calculation of the distribution density gradient is explained in detail. The distribution density gradient is used to quantify the data information density within each segment, and measures the density within the segment based on the rate of change of the information value, preferably calculated using the following formula:

[0057]

[0058] Among them, G i represents the distribution density gradient of the i-th segment, s k is the kth data value, and L is the length of the segment. In practical applications, distribution density gradient calculation can help identify information-dense areas in an image and assign different transmission priorities based on their density. For example, in medical imaging, areas with rich tissue detail often have higher distribution density gradients, while background areas have lower gradients. This gradient differentiation calculation can prioritize the transmission of critical information areas in medical images, ensuring that high-density information is processed first, significantly improving data transmission efficiency.

[0059] Preferably, in one embodiment of the present invention, the construction of the initial correlation matrix includes the following method: the matrix is ​​used to measure the correlation between adjacent segments so that the transmission order can be adjusted according to the correlation in subsequent steps. The initial correlation is calculated as follows:

[0060]

[0061] Among them, r ij Represents fragment S i and fragment S j The initial correlation between G i and G j Segment S i and fragment S j The distribution density gradient, ∈, is a small constant to avoid zero denominator, typically set to 0.0001. This formula reflects the correlation between segments by calculating the relative difference in density gradients between adjacent segments. This correlation calculation method has the beneficial effect of arranging the transmission sequence between information-dense and sparse areas, giving high-information-density segments priority in the transmission process. For lesion and background areas in medical images, lesion areas are transmitted first due to their higher density gradient, thereby improving the transmission efficiency of important data.

[0062] Preferably, in one embodiment of the present invention, the dynamic weight normalization and offset adjustment of the initial correlation matrix are performed to obtain a normalized correlation matrix. The purpose of normalization is to ensure a balanced distribution of correlations between segments and to avoid uneven weights due to large differences in density gradients. The normalization formula is as follows:

[0063]

[0064] Among them, N(r ij ) is the normalized correlation degree, min(r ij ) and max(r ij) represent the minimum and maximum values ​​of the initial correlation, δ is an offset constant to prevent the denominator from being zero, usually 0.01, β is the weight of the adjustment amplitude, preferably 0.5, and γ is the offset adjustment factor, usually between 0.1 and 1.0. Through normalization and offset adjustment, the values ​​in the correlation matrix are ensured to be distributed within a relatively balanced range, preventing extreme values ​​from affecting subsequent transmission sorting. This process not only ensures a reasonable distribution of correlation, but also effectively avoids instability caused by excessive deviation in weights between segments.

[0065] Through this dynamic normalization, the correlation degree is reasonably distributed among the fragments, forming a balanced correlation matrix, which provides an accurate weight basis for subsequent priority sorting, thereby ensuring that the fragments with high density gradients are processed first during transmission.

[0066] Preferably, in one embodiment of the present invention, an incremental weight matrix is ​​generated through multi-level projection aggregation based on the normalized correlation matrix to obtain incremental weights for each pair of segments. This step uses the multi-level projection aggregation method to accumulate the correlation weights of each segment layer by layer, ensuring the incrementality and reasonable distribution of weights between segments.

[0067] Specifically, multi-level projection aggregation is accomplished by geometric projection and iterative increment, where each element of the weight matrix W is incremented. ij Obtained by the following formula:

[0068]

[0069] Among them, W ij For fragment S i and fragment S j The increasing weight of r ij For fragment S i and S j Normalized correlation degree; P ik For fragment S i With fragment S k The geometric projection value is used to measure the spatial similarity between fragments; ζ is the projection balance factor, which is usually set to 0.05 to ensure the balance between different projection weights; η is the adjustment parameter for the incremental weight, preferably set to 0.9, so that the weight increases layer by layer; n is the number of projection iterations, which is usually set to 3 to ensure the incremental effect of multi-layer aggregation.

[0070] The beneficial effect of multi-level projection aggregation lies in its layer-by-layer optimization and aggregation of weights, ensuring that the weights of data-dense areas continuously increase, thereby giving high-density segments higher transmission priority in the transmission sorting process. This increasing weight matrix effectively identifies and sorts high-density areas, prioritizing the transmission of important data and improving transmission efficiency.

[0071] Preferably, in one embodiment of the present invention, a differentially optimized matrix is ​​obtained by applying a differentially adaptive filtering method based on the incremental weight matrix. The purpose of adaptive filtering is to eliminate extreme weight fluctuations in the incremental weight matrix, ensure a more balanced distribution of weights between different segments, and thereby enhance the smoothness and stability of the transmitted data.

[0072] Specifically, each element of the differential optimization matrix F ij It is obtained by the following adaptive filtering formula:

[0073]

[0074] Among them, F ij For fragment S i and S j The difference optimization value of W ij For fragment S i and S j , λ is the adaptive adjustment coefficient, preferably 1.0, to adapt to most density changes; κ is the filter gradient adjustment coefficient, usually adjusted between 0.5 and 2.0 to smooth the changes in weights; θ is the difference threshold, preferably 0.3, which is used to determine under what circumstances the weights are filtered.

[0075] Adaptive filtering creates a smoother and more continuous distribution of weights within the matrix, giving greater weight priority to high-density data areas while suppressing weight fluctuations in low-density areas. This method also eliminates extreme weight differences through filtering, ensuring a more stable image data transmission sequence and improving transmission consistency.

[0076] Preferably, in one embodiment of the present invention, based on the differential optimization matrix, a power-law distribution model is applied to generate a final allocation matrix to obtain the transmission priority of each fragment. The power-law distribution model is introduced to ensure that fragments in data-intensive areas receive higher priority, thereby giving priority to these fragments in data transmission.

[0077] Specifically, the transmission priority is determined by the following power-law distribution formula:

[0078]

[0079] Among them, q ij Represents fragment S i and fragment S j The final transmission priority of F ij is the element of the difference optimization matrix; α is the power law distribution scaling factor, preferably 1.5, which is used to increase the weight gap of high priority fragments; ω is the distribution weight exponent, usually set to 2.0 to ensure that high-density data fragments are transmitted first.

[0080] The advantage of this power-law distribution model is that it effectively amplifies the priority of dense information areas based on the weight differences of each fragment, ensuring that key data is given priority during data transmission and avoiding information loss due to improper fragment sorting.

[0081] In one embodiment of the present invention, a big data-based medical image transmission system is proposed, comprising a processing module 1 and a communication module 2, to systematically execute the aforementioned method steps. Processing module 1 is primarily used to analyze, partition, and sort image data, while communication module 2 is used to transmit the processed data information value sequence between the medical device and the receiving module.

[0082] In practical applications, processing module 1 includes a data processing unit (not shown), which acquires an image data sequence and performs segmentation, correlation matrix calculation, dynamic normalization, and multi-level projection aggregation, ultimately outputting a sequence of image segments with priority tags. Communication module 2, on the other hand, includes a wireless communication unit for transmitting the data sequence to a receiving end.

[0083] The beneficial effect of this system design is that the processing module 1 processes the data to ensure that the transmission order of the image data is optimized, while the communication module 2 realizes fast and seamless data transmission through wireless transmission, ensuring the stability and efficiency of the data transmission process.

[0084] In one embodiment of the present invention, the wireless communication module of the communication module 2 is used to transmit a sequence of data information values ​​wirelessly to ensure data security and privacy protection. In this module, an encryption protocol (such as AES-256) is preferably used to encrypt the sequence of data information values ​​to ensure the security of data transmission. In addition, the wireless communication unit of the communication module 2 supports multiple transmission methods, including Wi-Fi, 5G, and Bluetooth protocols, to adapt to transmission requirements in different environments.

[0085] In practical applications, the wireless communication module can maintain a stable connection with the receiving end of medical images, ensuring that image data is not interfered with during transmission, thereby achieving real-time and high efficiency. The beneficial effect of the present invention is that the use of wireless communication not only improves the flexibility of data transmission, but also ensures the security of the transmission process through encryption, providing reliable support for the remote processing and analysis of medical data.

[0086] The present invention's big data-based medical image transmission method and system achieves efficient data transmission and priority management through steps such as image data fragmentation, correlation optimization, multi-level aggregation, adaptive filtering, and power-law sorting. Ultimately, with the help of processing module 1 and communication module 2, this system can stably complete data transmission, providing an efficient and reliable solution for telemedicine and medical image processing.

[0087] To validate the superiority of the present invention's big data-based medical image transmission method, a specific experimental design was implemented, using a professional medical image dataset and conducting detailed testing and analysis through comparative experiments. The core test metrics focused on the invention's innovative features, including the accuracy of distribution density gradient calculation, transmission priority sorting, and ultimately transmission efficiency and image reconstruction quality.

[0088] The present invention uses the public medical imaging dataset MIMIC-CXR as experimental data. This dataset contains a large amount of chest X-ray image data with high image resolution and different information densities, which is suitable for testing the transmission effect of the present invention under different data densities.

[0089] In this embodiment of the present invention, the image is first divided into segments of equal length, each containing 512 information values. The distribution density gradient is calculated for each segment, an initial correlation matrix is ​​established, and dynamic normalization is performed. Next, an increasing weight matrix is ​​generated through multi-level projection aggregation. Finally, adaptive filtering and a power-law distribution model are used to generate a priority ranking, ensuring that high-density information areas are transmitted first. The final data is transmitted to the receiving end in an encrypted manner via a wireless communication module.

[0090] The comparative test used a traditional medical image transmission method, which directly divides image data into segments of equal length and transmits them sequentially without considering the density differences and priority of the segments. This method lacks gradient analysis of data segments, correlation optimization, and transmission sequence management, resulting in a failure to effectively highlight areas of high information density during transmission.

[0091] The experiment tested the following key indicators:

[0092] Transmission efficiency: This evaluates the amount of image data per unit time, measured in MB / s, and examines how the differences in data density of each segment during transmission affect transmission time.

[0093] Image reconstruction quality: The quality of image reconstruction at the receiving end after image transmission is completed is evaluated using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) indicators, expressed in dB and a value between 0 and 1, respectively.

[0094] Data packet loss rate: Statistics on the amount of image information lost due to packet loss during transmission, expressed as a percentage.

[0095] The detection method uses the standard image transmission analysis tool Wireshark to monitor data packets, and uses MATLAB to perform PSNR and SSIM evaluation on image reconstruction at the receiving end to ensure the scientificity and accuracy of the results.

[0096] The test results and analysis are as follows:

[0097] index Embodiments of the present invention Comparative Example Transmission efficiency (MB / s) 5.6 3.2 Image reconstruction quality (PSNR) 38.7dB 32.4dB Image reconstruction quality (SSIM) 0.92 0.81 Data packet loss rate (%) 1.5 4.8

[0098] In this embodiment, the transmission efficiency is significantly better than the control example, reaching 5.6MB / s, while the control example is only 3.2MB / s. This shows that the priority management based on distribution density gradient and ascending weight sorting in this embodiment significantly improves transmission efficiency during data transmission. By prioritizing the transmission of high-density information fragments, the timely delivery of critical data is ensured, shortening the overall transmission time.

[0099] In terms of image reconstruction quality, the present invention outperforms the control example in both PSNR and SSIM, achieving a PSNR of 38.7dB and an SSIM of 0.92, compared to 32.4dB and 0.81, respectively, for the control example. This demonstrates that the present image transmission method can more effectively preserve image details and structural information, improving the accuracy of image reconstruction.

[0100] Furthermore, the packet loss rate of the embodiment of the present invention is 1.5%, significantly lower than the 4.8% of the comparative example. This low packet loss rate reflects that priority management and adaptive filtering make the transmission process more stable, effectively control packet loss, and ensure data integrity.

[0101] Test results demonstrate that this method effectively optimizes the transmission of medical images through distribution density gradient analysis, multi-level projection aggregation, and power-law distribution model sorting. By prioritizing the transmission of important segments under varying data densities, transmission efficiency is significantly improved and information loss is reduced. Furthermore, low packet loss rates and high PSNR / SSIM values ​​demonstrate that image data maintains high reconstruction quality after transmission, ensuring the accuracy and integrity of medical images, which is crucial for clinical decision support and remote medical analysis.

[0102] In the best embodiment of the present invention, efficient and reliable medical image transmission is achieved through intelligent analysis and sorting of image data, providing an effective solution for data transmission in the medical field.

[0103] It should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A medical image transmission method based on big data, characterized by comprising the following steps: Acquire a data information value sequence of a medical image from a medical device, divide the data information value sequence into a plurality of segments of equal length, and obtain the segment sequence; Based on each segment in the segment sequence, determining a distribution density gradient of each segment, wherein the distribution density gradient is a rate of change of data information density within the segment, and obtaining an initial correlation matrix based on the distribution density gradients of every two adjacent segments; Performing dynamic weight normalization and offset adjustment on the initial correlation matrix to obtain a normalized correlation matrix; Based on the normalized correlation matrix, an increasing weight matrix is ​​generated through multi-level projection aggregation to obtain an increasing weight for each pair of segments; Based on the incremental weight matrix, applying a difference adaptive filtering method to obtain a difference optimization matrix; Based on the difference optimization matrix, a power law distribution model is applied to generate a final allocation matrix to obtain the transmission priority of each fragment; determining a transmission order of the segments based on the final allocation matrix to achieve orderly transmission of the medical image data; The normalized correlation matrix is ​​aggregated through multi-level projection to generate an increasing weight matrix, and the increasing weight is obtained by the following formula: ； in Representation fragment and The increasing weight of For fragments and The correlation degree, Representation fragment With fragment The geometric projection of is the adjustment factor for adjusting the uniformity of projection weights, To increase the weight adjustment parameter, is the number of projection iterations; Each element of the difference optimization matrix is ​​obtained by the following formula: ； in represents the difference optimization value after filtering, For fragments and The increasing weight of is the adaptive adjustment coefficient, is the filter gradient adjustment coefficient, is the difference threshold.

2. A medical image transmission method based on big data according to claim 1, characterized in that: The distribution density gradient is used to characterize the density change of the information value in each segment, wherein the distribution density gradient of each segment For fragments The rate of change of density is expressed as: ； in For fragments The distribution density gradient, Indicates the Data information value, Indicates the length of the segment.

3. The method for transmitting medical images based on big data according to claim 2, characterized in that: The correlation in the initial correlation matrix is ​​obtained based on the distribution density gradient difference of adjacent segments, and the correlation is determined by the following formula: ； in For fragments and The initial correlation degree, and Separate fragments and The distribution density gradient, A small constant to prevent division by zero.

4. The method for transmitting medical images based on big data according to claim 3, characterized in that: The normalization algorithm consists of the following steps: The initial correlation Perform maximum and minimum normalization and offset adjustment, the normalized correlation matrix elements Obtained by the following formula: ； in is the normalized correlation, and are the minimum and maximum values ​​of the correlation, To prevent division by zero, the offset constant is the normalized amplitude adjustment weight, Adjust the offset ratio.

5. The method for transmitting medical images based on big data according to claim 1, characterized in that: The power law distribution model is obtained using the following formula: ； in Representation fragment and snippets The transmission priority of The elements of the matrix are optimized for diversity, is the scale factor of the power law distribution, is the distribution weight index.

6. The method for transmitting medical images based on big data according to claim 5, characterized in that: The data processing flow sequence of the distribution density gradient calculation step, the initial correlation matrix calculation step, the dynamic weight normalization and offset adjustment step, the multi-level projection aggregation step, the difference adaptive filtering step and the power-law distribution model generation final distribution matrix step forms a closed loop to ensure the validity and priority control of the medical image transmission data stream.

7. A medical image transmission system based on big data, characterized in that: It comprises a processing module and a communication module, wherein the processing module is used to implement the steps described in any one of claims 1 to 6, and the communication module is used to transmit the data information value sequence between the medical device and the receiving module.

8. The medical image transmission system based on big data according to claim 7, characterized in that: The communication module includes a wireless communication module for transmitting the data information value sequence in a wireless manner to achieve data security and privacy protection.

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