A chest and lung medical image processing and analysis software system with high transmission efficiency

Abstract

The present application relates to the field of medical information technology, and particularly relates to a chest and lung medical image processing and analysis software system with high transmission efficiency, which comprises an image high-speed acquisition module, a data compression and encoding module, an efficient transmission module, an image processing and analysis module and a result display and report generation module; the image high-speed acquisition module is used to realize the acquisition of medical image data, and ensure the integrity and real-time performance of the data; the image high-speed acquisition module, the data compression and encoding module, the efficient transmission module, the image processing and analysis module and the result display and report generation module cooperate with each other to realize the efficient and real-time transmission of medical images, and the images are processed in real time, and the lesions are intelligently analyzed and identified, the lesion area is segmented, a diagnosis report is automatically generated, the demand for storage space and transmission bandwidth is effectively reduced, and it is of great significance for promoting the medical informatization construction and improving the medical service level.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of medical information technology, and particularly relates to a chest and lung medical image processing and analysis software system with high transmission efficiency. BACKGROUND

[0002] With the rapid development of medical technology, medical images play an increasingly important role in clinical diagnosis and treatment. In particular, chest and lung medical images are images of the chest and lungs obtained through medical imaging technology to assist doctors in diagnosing, treating and monitoring diseases. Common medical imaging equipment includes X-ray, CT and MRI. Through chest and lung medical images, doctors can observe the structural changes of the lungs and chest, combine with clinical symptoms and signs, and make preliminary diagnosis of diseases. Chest and lung medical images play a key role in early detection of lung diseases, evaluation of treatment effect and development of individualized treatment plans. Traditional systems usually rely on local servers for data processing, which is limited by server performance and storage capacity, and the processing speed is slow, which cannot meet the real-time processing needs of large-scale image data. Image data is usually transmitted through a local area network or a wired network, and the transmission rate is limited by network bandwidth and stability, which affects the timeliness and accuracy of data. Moreover, it relies on the subjective experience and manual annotation of doctors, and it is difficult to realize automatic and high-precision image diagnosis. In view of the above problems, we propose a chest and lung medical image processing and analysis software system with high transmission efficiency. SUMMARY

[0003] In view of the deficiencies of the prior art, the present application provides a chest and lung medical image processing and analysis software system with high transmission efficiency to solve the above technical problems.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a chest and lung medical image processing and analysis software system with high transmission efficiency, which comprises: an image high-speed acquisition module, a data compression and encoding module, an efficient transmission module, an image processing and analysis module, and a result display and report generation module.

[0005] The image high-speed acquisition module is used to realize the acquisition of medical image data, and ensure the integrity and real-time performance of the data.

[0006] The data compression and encoding module is used to efficiently compress and encode the acquired image data, so as to reduce the data storage space and transmission bandwidth requirement, while maintaining the image quality.

[0007] The efficient transmission module is used to realize the safe and rapid transmission of the compressed and encoded image data, supports multiple network environments and protocols, and ensures the stability and efficiency of data transmission.

[0008] The image processing and analysis module is used to process the received image data, and intelligently analyze and identify lesions and segment lesion areas through a region growing algorithm;

[0009] The result display and report generation module provides an intuitive and easy-to-use operation interface, supports multi-modal and multi-dimensional image display, allows users to dynamically adjust and interactively operate, and automatically generates detailed reports containing analysis results to facilitate medical personnel to make accurate diagnoses.

[0010] Preferably, the image high-speed acquisition module is provided with replaceable slots, which are replaced according to different medical image device interfaces to support rapid acquisition of multiple image formats, including X-ray, CT, and MRI.

[0011] Preferably, the data compression and encoding module works on the steps of efficiently compressing and encoding the image data as follows:

[0012] S1. Read the original chest and lung medical image data in the image high-speed acquisition module;

[0013] S2. Design a predictor according to the characteristics of chest and lung medical images, use the predictor to predict each pixel in the image, calculate the error between the actual pixel value and the predicted value, quantize the prediction error, convert the continuous error value into a discrete quantized value, encode the quantized prediction error, and generate a DPCM encoded data stream;

[0014] S3. Frequency statistics of each symbol in the DPCM encoded data stream are performed to obtain the occurrence probability of each symbol, a Huffman tree is constructed according to the frequency of the symbol, and each symbol is assigned a unique code, the DPCM encoded data stream is encoded using the Huffman encoding table, and a Huffman encoded data stream is generated;

[0015] S4. The Huffman encoded data stream is organized according to the requirements of the PNG format, including the PNG file header, image header, color palette, and image data block part, the data in the image data block part is compressed using the lossless compression algorithm of PNG, the compressed data is written into the PNG file, and necessary file tail information is added to complete the generation of the PNG file;

[0016] S5. The compressed chest and lung medical image is evaluated for image quality using the PSNR index.

[0017] Preferably, the high-speed transmission module adopts high-speed network communication technology and parallel transmission protocol, supports multi-channel concurrent transmission, dynamically adjusts transmission parameters according to network conditions during data transmission, optimizes transmission path, and ensures efficient transmission of image data within a limited time.

[0018] Preferably, the step S2 of designing the predictor according to the characteristics of the chest lung medical image is:

[0019] A1, consider linear prediction and nonlinear prediction when selecting a prediction model, linear prediction is based on linear combination of adjacent pixels, and nonlinear prediction uses polynomial regression to capture spatial relationship;

[0020] A2, according to the characteristics of the chest lung medical image, determine the weight parameters in the predictor through experiment, and select the prediction window size to balance the prediction accuracy and the calculation complexity;

[0021] A3, dynamically adjust the parameters of the predictor according to the local characteristics of the current pixel, divide the image into different regions and design different predictors for each region;

[0022] A4, use the global statistical information of the image to assist prediction;

[0023] A5, collect a large amount of chest lung medical image data, including normal and lesion images, evaluate the performance of different predictors through experiment, adjust the design of the predictor according to the experimental results, and iterate multiple times to optimize the performance.

[0024] Preferably, the step S5 of using PSNR index to evaluate the image quality is:

[0025] B1, take the original uncompressed chest lung medical image as the reference image, and take the same chest lung medical image after compression processing as the image to be evaluated;

[0026] B2, MSE is the average value of the square of the difference between the corresponding pixels of two images, and the calculation formula is:

[0027]

[0028] Where I(i,j) and K(i,j) are the pixel values of the original image and the compressed image at position (i,j), and m and n are the height and width of the image respectively;

[0029] B3, PSNR is calculated based on MSE, and the calculation formula is:

[0030]

[0031] Where MAX is the maximum value of the pixel value in the image;

[0032] B4, according to the calculated PSNR value, the quality of the compressed chest and lung medical image is evaluated, the PSNR value is between 30dB to 50dB, which represents that the image quality is good, when the PSNR value is greater than 50dB, the quality of the compressed image is almost consistent with the original image, and when the PSNR value is less than 30dB, the image quality is poor.

[0033] Preferably, the image processing and analysis module includes image denoising, image enhancement, image registration and image three-dimensional reconstruction in the processing method of the received image data.

[0034] Preferably, the specific steps of the region growing algorithm intelligent analysis and identification of the lesion and the segmentation of the lesion area are as follows:

[0035] C1, selecting one or more seed points in the medical image, which are located in the interior of the lesion or the lesion area, and the selection of the seed points is realized by the artificial designation method;

[0036] C2, determining the measurement criterion of pixel similarity, including the difference between the gray values of the pixels, the color difference and the texture feature;

[0037] C3, taking the initial seed point as a region, and adding it to the region set;

[0038] C4, in each iteration, a region is selected from the region set, and the neighborhood pixels of the region are traversed;

[0039] C5, repeating the above iteration and growth process until all pixels are accessed or no new pixel can be added to the region;

[0040] C6, the final region set is output as the segmentation result of the image, and in the medical image, these segmentation regions usually represent different tissues or lesion sites.

[0041] Preferably, the processed image and the analysis result are displayed through a graphical user interface, a diagnosis basis is provided, and a standardized diagnosis report containing patient information, image description and diagnosis conclusion content is automatically generated.

[0042] Preferably, the system adopts modular design, and each module is relatively independent, which is convenient for maintenance and upgrading.

[0043] Compared with the prior art, the beneficial effects of the present application are as follows:

[0044] The application achieves the purpose of efficient real-time transmission of medical images, real-time processing of images, intelligent analysis and identification of lesions, segmentation of lesion areas, and automatic generation of diagnosis reports through the cooperation of the image high-speed acquisition module, the data compression and encoding module, the efficient transmission module, the image processing and analysis module, and the result display and report generation module. Through the data compression and encoding module and the efficient transmission module, the redundant information of image data in the storage and transmission process is greatly reduced, effectively reducing the demand for storage space and transmission bandwidth, thereby realizing efficient and rapid transmission of image data. While efficiently compressing and encoding, the system uses advanced algorithms and technologies to ensure minimal loss of image data during compression, maintaining the high definition and detail information of the original image, providing a reliable data foundation for subsequent image processing and analysis. The image processing and analysis module reduces the dependence on manual intervention through the region growing algorithm, improving the accuracy and efficiency of image diagnosis. The result display and report generation module provides an intuitive and easy-to-use operation interface and a variety of display methods, allowing users to dynamically adjust and interact, greatly improving user experience. In terms of improving image transmission efficiency, ensuring image data quality, improving image processing and analysis accuracy, optimizing user experience, and promoting medical resource sharing and cooperation, the application shows significant beneficial effects, which is of great significance for promoting medical informatization construction and improving medical service level. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 The chest and lung medical image processing and analysis software system framework diagram. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the application will be described below in conjunction with the drawings in the embodiments of the application. Obviously, the described embodiments are only a part of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0047] The application provides a technical solution: a chest and lung medical image processing and analysis software system with high transmission efficiency, which includes an image high-speed acquisition module, a data compression and encoding module, an efficient transmission module, an image processing and analysis module, and a result display and report generation module.

[0048] The image high-speed acquisition module is used to realize the acquisition of medical image data, ensuring the integrity and real-time nature of the data.

[0049] The data compression and encoding module is used to efficiently compress and encode the acquired image data to reduce the data storage space and transmission bandwidth requirement while maintaining the image quality.

[0050] The high-efficiency transmission module is used to realize the safe and fast transmission of compressed and encoded image data, supports various network environments and protocols, and ensures the stability and efficiency of data transmission;

[0051] The image processing and analysis module is used to process the received image data, and intelligently analyze and identify lesions and segment lesion areas through a region growing algorithm;

[0052] The result display and report generation module provides an intuitive and easy-to-use operation interface, supports multi-modal and multi-dimensional image display, allows users to dynamically adjust and interactively operate, and automatically generates detailed reports containing analysis results, facilitating medical personnel to make accurate diagnoses.

[0053] Further, the image high-speed acquisition module is provided with replaceable slots, which are replaced according to different medical image device interfaces to ensure compatibility with medical image devices. This plug-and-play design simplifies the device connection process and avoids compatibility problems caused by mismatched interfaces, thereby improving the overall operation efficiency of the system, supporting the rapid acquisition of various image formats, and enabling the system to access different types of image data sources, providing materials for subsequent data processing and analysis, including X-ray, CT, and MRI.

[0054] Further, the data compression and encoding module works on the steps of efficiently compressing and encoding image data as follows:

[0055] S1, the original chest and lung medical image data is read in the image high-speed acquisition module;

[0056] S2, a predictor is designed according to the characteristics of chest and lung medical images. The predictor uses the spatial correlation between pixels in the image to predict each pixel in the image. The prediction process is based on the gray value, texture feature or structure information of the surrounding pixels, and calculates the error between the actual pixel value and the predicted value. The prediction error is quantized to convert the continuous error value into a discrete quantized value to reduce data redundancy. The quantized prediction error is encoded to generate a DPCM encoded data stream. This process effectively reduces the dynamic range of the data and provides a more easily compressed data format for subsequent encoding;

[0057] S3, frequency statistics are performed on each symbol in the DPCM encoded data stream to obtain the occurrence probability of each symbol, a Huffman tree is constructed according to the frequency of the symbol, and each symbol is assigned a unique code whose length is inversely proportional to its occurrence probability, and the DPCM encoded data stream is encoded using the Huffman encoding table, which can further reduce the length of the data, because the symbols with high frequency are assigned shorter codes, and the symbols with low frequency are assigned longer codes, and this process generates the Huffman encoded data stream to realize efficient compression of the data;

[0058] S4, the Huffman encoded data stream is organized according to the requirements of PNG format, PNG is a widely used lossless compression image format, which supports transparency, palette index and multiple compression algorithms, including PNG file header, image header, palette and image data block part, the module first adds PNG file header and image header data part, which describes the basic information of the image, if the image contains palette information, the palette part is added, the image data block part is compressed using the lossless compression algorithm of PNG, the compressed data is written into the PNG file, and the necessary file tail information is added, the generation of PNG file is completed;

[0059] S5, the PSNR index is used to evaluate the image quality of the compressed chest and lung medical image, PSNR is a common method for measuring the quality of compressed image, which calculates the difference between the original image and the compressed image at the corresponding pixel points, a higher PSNR value means that the quality of the compressed image is better, that is, the image distortion is smaller, by evaluating the PSNR value, medical personnel can understand the influence degree of compression processing on the diagnostic value of the image, so as to adjust the compression parameters to achieve the best effect.

[0060] Further, as an important part of the whole chest and lung medical image processing and analysis system, the performance and stability of the high-speed transmission module directly affect whether the image data can be timely and accurately delivered to the target location, and further affect the efficiency and accuracy of medical diagnosis. Therefore, the high-speed transmission module uses high-speed network communication technology and parallel transmission protocol to support multi-channel concurrent transmission. Compared with traditional serial transmission, parallel transmission can transmit multiple data bits at the same time, thereby transmitting more data in the same time. Through the designed parallel transmission protocol, the module can support multi-channel concurrent transmission, that is, simultaneously using multiple transmission channels for data transmission. In the data transmission process, the transmission parameters are dynamically adjusted according to the network status to optimize the transmission path. This mechanism can monitor the network status in real time, including bandwidth, delay, and packet loss rate, to ensure efficient transmission of image data within a limited time.

[0061] Further, the predictor designed according to the characteristics of chest and lung medical images in step S2 is:

[0062] A1, consider linear prediction and nonlinear prediction when selecting a prediction model, linear prediction predicts based on linear combination of adjacent pixels, which is simple and intuitive, and has high computational efficiency, and is suitable for smooth or gradual change areas in images, however, chest lung medical images often contain rich texture, edge and detail information, nonlinear prediction uses polynomial regression to capture spatial relationships, in the edge and texture-rich areas of the image, by combining the advantages of linear prediction and nonlinear prediction, a more flexible and efficient predictor is designed;

[0063] A2, according to the characteristics of chest lung medical images, the weight parameters in the predictor are determined through experiments, for the images of the lung region, due to the uniform texture, a smaller weight difference is preferred, while for the regions containing blood vessels, nodules and other lesions, the weight needs to be adjusted more finely to capture subtle changes, and the prediction window size is selected, which determines the range of pixels considered during prediction, a large window will introduce unnecessary noise and computational burden, while a small window cannot fully utilize the spatial correlation of the image, to balance the prediction accuracy and computational complexity;

[0064] A3, dynamically adjust the parameters of the predictor according to the local characteristics of the current pixel, divide the image into different regions and design different predictors for each region, for example, use a set of parameters in the lung region to capture its uniform texture characteristics, while in the blood vessel or nodule region, another set of parameters is needed to better capture its edge and detail information, this local characteristic driven parameter adjustment method makes the predictor more flexible to cope with complex changes in the image, thereby improving the overall compression efficiency;

[0065] A4, use global statistical information of the image to assist prediction, global statistical information includes mean, variance, histogram distribution of the image, which reflects the overall characteristics of the image, by using these information, the prediction result can be further corrected and optimized;

[0066] A5, collect a large amount of chest lung medical image data, including normal and lesion images, evaluate the performance of different predictors through experiments, adjust the design of the predictor according to the experimental results, and iterate multiple times to optimize the performance.

[0067] Further, the PSNR index is used to evaluate the image quality in step S5, and the detailed steps are as follows:

[0068] B1, take the original uncompressed chest lung medical image as the reference image, and take the same chest lung medical image after compression processing as the image to be evaluated;

[0069] B2, MSE is the average of the square of the difference between the corresponding pixels of two images, the calculation formula is:

[0070]

[0071] where I(i,j) and K(i,j) are the pixel values of the original image and the compressed image at position (i,j) respectively, and m and n are the height and width of the image respectively;

[0072] B3, PSNR is calculated based on MSE, and the calculation formula is:

[0073]

[0074] where MAX is the maximum value of the pixel value in the image;

[0075] B4, according to the calculated PSNR value, the quality of the compressed chest and lung medical image is evaluated, the image quality is good when the PSNR value is between 30dB and 50dB, the quality of the compressed image is almost consistent with the original image when the PSNR value is greater than 50dB, and the image quality is poor when the PSNR value is less than 30dB.

[0076] Further, the image processing and analysis module receives the image data processing method, which includes image denoising, image enhancement, image registration and image three-dimensional reconstruction;

[0077] Image denoising aims to eliminate or weaken the noise component in the image to improve the clarity and signal-to-noise ratio of the image, the noise sources existing in the chest and lung medical image include device noise, transmission noise and patient's own factors, there are many methods for image denoising, the common ones are median filter, Gaussian filter and wavelet transform denoising

[0078] Image enhancement aims to improve the visual effect of the image, highlight the useful information in the image, and suppress or remove the useless information, for chest and lung medical image, the image enhancement method includes contrast enhancement, sharpening, histogram equalization, contrast enhancement can improve the contrast between different tissues in the image, make the lesion area more obvious, sharpening can enhance the edge information of the image, make the details more clear, and histogram equalization can adjust the brightness distribution of the image, so that the image looks more uniform as a whole;

[0079] Image registration aligns the images obtained at different times, different angles or different imaging devices in space, so as to carry out subsequent comparison and analysis, and facilitates doctors to compare the images of patients at different time points and under different conditions to evaluate the changes of the disease;

[0080] Image three-dimensional reconstruction is the process of converting two-dimensional image data into a three-dimensional model, which can provide doctors with more intuitive and comprehensive lesion information. Three-dimensional reconstruction can help doctors better understand the morphology, location and relationship with the surrounding tissue of the lesion. In order to realize three-dimensional reconstruction, first, useful information needs to be extracted from two-dimensional images, then three-dimensional modeling and rendering are performed using this information, and finally the desired three-dimensional image is obtained.

[0081] Further, the region growing algorithm intelligently analyzes and identifies the lesion and segments the lesion area, and the specific steps are as follows:

[0082] C1. Select one or more seed points in the medical image, located inside the lesion or lesion area. The selection of seed points is realized by manual designation method, which is manually selected inside the lesion or lesion area through visual inspection. The seed point will be the starting point of the algorithm growth, and will affect the quality and accuracy of the final segmentation result.

[0083] C2. Determine the similarity measure of the pixel, including the difference between the gray value of the pixel, the color difference, and the texture feature. One of the most commonly used similarity measures is the difference between the gray values of the pixels. By setting a threshold, the algorithm can determine whether the adjacent pixels are similar enough to be added to the current region. For color images, the distance in the color space is considered as the similarity measure. For areas with rich texture, texture features are introduced as a supplement to the similarity measure.

[0084] C3. Take the initial seed point as a region and add it to the region set.

[0085] C4. In each iteration, select a region from the region set as the current processing object, and traverse the neighborhood pixels of the region. According to the similarity measure defined before, judge whether these neighborhood pixels meet the condition of joining the current region.

[0086] C5. Repeat the above iteration and growth process until all pixels are visited or no new pixel can be added to the region.

[0087] C6. Take the final region set as the segmentation result of the image and output it. In medical images, these segmented regions usually represent different tissues or lesion sites, providing doctors with intuitive lesion identification information.

[0088] Further, the processed image and analysis result are displayed through a graphical user interface, providing a basis for diagnosis. The graphical user interface provides dynamic interaction functions, including zooming, panning, and rotating, so that the doctor can freely adjust the display mode and angle of the image to obtain the best observation effect. This multi-view display mode helps the doctor to observe the shape, position, and relationship with the surrounding tissue of the lesion comprehensively and carefully. After the doctor completes image analysis and diagnosis, the system should be able to automatically extract relevant data from the patient information database, image processing and analysis results, and fill them into the diagnosis report template, and automatically generate a standardized diagnosis report containing patient information, image description, and diagnosis conclusion content.

[0089] Further, the system adopts modular design, which reduces the direct dependence between modules and the coupling degree between parts of the system, and the modules are relatively independent. When the system fails or needs to be maintained, the specific module is checked and repaired without the need for comprehensive inspection of the entire system, facilitating maintenance and upgrading.

[0090] It should be noted that, in this document, the terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed or inherent to such process, method, article or device.

[0091] Although embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made thereto without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A high-efficiency software system for processing and analyzing chest and lung medical images, characterized in that, The image processing and analysis software system includes: a high-speed image acquisition module, a data compression and encoding module, a high-efficiency transmission module, an image processing and analysis module, and a results display and report generation module; The high-speed image acquisition module is used to acquire medical image data, ensuring data integrity and real-time performance. The data compression and encoding module is used to efficiently compress and encode the acquired image data to reduce data storage space and transmission bandwidth requirements while maintaining image quality. Within this module, the predictor design steps, based on the characteristics of chest and lung medical images, are as follows: A1. When selecting a prediction model, consider linear prediction and nonlinear prediction. Linear prediction is based on the linear combination of adjacent pixels, while nonlinear prediction uses multinomial regression to capture spatial relationships. A2. Based on the characteristics of chest and lung medical images, the weight parameters in the predictor are determined through experiments, and the prediction window size is selected to balance prediction accuracy and computational complexity. A3. Dynamically adjust the parameters of the predictor based on the local characteristics of the current pixel, segment the image into different regions, and design a different predictor for each region; A4. Utilize global statistical information of the image to assist in prediction; A5. Collect a large amount of chest and lung medical imaging data, including normal and diseased images, evaluate the performance of different predictors through experiments, adjust the predictor design based on the experimental results, and perform multiple iterations to optimize performance. The high-efficiency transmission module is used to achieve secure and fast transmission of compressed and encoded image data, supports multiple network environments and protocols, and ensures the stability and efficiency of data transmission. The image processing and analysis module is used to process the received image data and perform intelligent analysis to identify lesions and segment disease areas through region growing algorithms; The results display and report generation module provides an intuitive and easy-to-use interface, supports multimodal and multi-dimensional image display, allows users to make dynamic adjustments and interactive operations, and automatically generates detailed reports containing analysis results to facilitate accurate diagnosis by medical personnel.

2. The high-efficiency chest and lung medical image processing and analysis software system according to claim 1, characterized in that: The high-speed image acquisition module has replaceable slots, allowing for the replacement of compatible slots according to different medical imaging equipment interfaces. It supports rapid acquisition of various image formats, including X-ray, CT, and MRI.

3. The high-efficiency chest and lung medical image processing and analysis software system according to claim 1, characterized in that, The data compression and encoding module performs efficient compression and encoding of image data through the following steps: S1. Read the raw chest and lung medical image data in the high-speed image acquisition module; S2. Based on the characteristics of chest and lung medical images, a predictor is designed. The predictor is used to predict each pixel in the image and calculate the error between the actual pixel value and the predicted value. The prediction error is quantized to convert continuous error values ​​into discrete quantized values. The quantized prediction error is encoded to generate a DPCM encoded data stream. S3. Perform frequency statistics on each symbol in the DPCM-encoded data stream to obtain the probability of occurrence of each symbol. Construct a Huffman tree based on the frequency of the symbols and assign a unique code to each symbol. Use the Huffman coding table to encode the DPCM-encoded data stream to generate a Huffman-encoded data stream. S4. Organize the Huffman-encoded data stream according to the requirements of PNG format, including PNG file header, image header, color palette and image data block part. Compress the data of the image data block part using PNG lossless compression algorithm, write the compressed data into PNG file, and add file end information to complete the generation of PNG file; S5. Use the PSNR index to assess the image quality of compressed chest and lung medical images.

4. The high-efficiency chest and lung medical image processing and analysis software system according to claim 1, characterized in that: The high-speed transmission module adopts high-speed network communication technology and parallel transmission protocol, supports multi-channel concurrent transmission, and dynamically adjusts transmission parameters and optimizes transmission path according to network conditions during data transmission to ensure efficient transmission of image data within a limited time.

5. The high-efficiency chest and lung medical image processing and analysis software system according to claim 3, characterized in that, The detailed steps for evaluating image quality using the PSNR index in Step S5 are as follows: B1. Use the original, uncompressed chest and lung medical images as reference images, and use the same compressed chest and lung medical images as images to be evaluated. B2 and MSE are the average of the squared differences between corresponding pixels in two images, calculated using the following formula: Where I(i,j) and K(i,j) are the pixel values ​​of the original image and the compressed image at position (i,j), respectively, and m and n are the height and width of the image, respectively; B3. PSNR is calculated based on MSE, and its calculation formula is as follows: Where MAX is the maximum value of a pixel in the image; B4. Based on the calculated PSNR value, evaluate the quality of the compressed chest and lung medical images. A PSNR value between 30dB and 50dB indicates good image quality. When the PSNR value is greater than 50dB, the quality of the compressed image is almost identical to the original image. When the PSNR value is less than 30dB, the image quality is poor.

6. The high-efficiency chest and lung medical image processing and analysis software system according to claim 1, characterized in that: The image processing and analysis module processes the received image data using methods including image denoising, image enhancement, image registration, and 3D image reconstruction.

7. The high-efficiency chest and lung medical image processing and analysis software system according to claim 1, characterized in that, The specific steps and methods for intelligent analysis and identification of lesions and segmentation of lesion regions using the region growing algorithm are as follows: C1. Select one or more seed points in the medical image, located inside the lesion or disease area. The selection of seed points is achieved by manual specification. C2. Determine the criteria for measuring pixel similarity, including the difference in grayscale values, color differences, and texture features of pixels; C3. Treat the initial seed point as a region and add it to the region set; C4. In each iteration, select a region from the region set and iterate through the neighboring pixels of that region. C5. Repeat the above iterative growth process until all pixels have been visited or no new pixels can be added to the region; C6. Output the final set of regions as the segmentation result of the image. In medical imaging, these segmented regions represent different tissues or lesion sites.

8. The high-efficiency chest and lung medical image processing and analysis software system according to claim 1, characterized in that: The system displays processed images and analysis results through a graphical user interface, provides diagnostic evidence, and automatically generates standardized diagnostic reports containing patient information, image descriptions, and diagnostic conclusions.

9. The high-efficiency chest and lung medical image processing and analysis software system according to claim 1, characterized in that: The system adopts a modular design, with each module being relatively independent, which facilitates maintenance and upgrades.

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