Massive medical image cold data joint compression method and system

Abstract

The application provides a mass medical image cold data joint compression method, a feature extraction network is used for fine-grained feature extraction on large-scale medical images, a feature database of medical images is established, and the extracted image features are stored according to classification; the feature extraction network is used for the medical image to be compressed, features are extracted and similarity matching is performed; then, based on image brightness, slices of the reference image and the image to be compressed are sorted to optimize the compression order; finally, according to the reference image, lossy coding of the current image is assisted, and according to the original image, lossless coding of the current image is assisted.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical image compression, and particularly relates to a method for jointly compressing massive medical image cold data. Background Art

[0002] Cold data refers to data that is accessed less frequently and is less critical to business and applications. This data typically needs to be stored long-term but requires less frequent access and processing. In the field of medical imaging, cold data typically includes historical patient imaging records, long-term follow-up data, and research data. Although this data is accessed less frequently, it holds significant value in medical research, disease tracking, and querying patient historical information, thus requiring long-term, reliable storage. Existing compression methods mostly process single images or sets of slices, failing to fully exploit the similarities and correlations between massive amounts of data, resulting in poor compression results. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a joint compression method for massive medical image cold data, which can effectively utilize the similarities and correlations between massive data, reduce the transmission code rate, and improve the compression ratio. The scheme of the present invention first designs a feature extraction network to extract fine-grained features from large-scale medical images and construct an efficient and retrievable feature library of medical images. Subsequently, the medical images are predicted and sorted based on features such as image brightness, and the compression order of the images is optimized. As a supporting design for the compression scheme, finally, a low-code-rate lossless encoding and decoding scheme for medical images is designed, which uses a reference image to perform lossy encoding and decoding on the compressed image in sequence, and then performs lossless encoding and decoding on the residual, achieving a lossless encoding and decoding effect with fewer bits. The present invention effectively solves the problem of efficient compression in the storage and transmission of massive medical image cold data.

[0004] The technical solution specifically adopted by the present invention to solve the technical problem is:

[0005] A massive cold data compression method for medical images uses a feature extraction network to extract fine-grained features from large-scale medical images, thereby establishing a feature database for medical images and storing the extracted image features by category.

[0006] Then, the feature extraction network is used on the medical image to be compressed to extract features and perform similarity matching;

[0007] Then, based on image brightness, the slices of the reference image and the image to be compressed are sorted to optimize the compression order;

[0008] Finally, the reference image is used to assist in the lossy encoding of the current image, and then the original image is used to assist in the lossless encoding of the current image.

[0009] Further, the feature extraction network is used to extract features, defined as:

[0010]

[0011] wherein, represents a feature extractor, used to convert images into feature representations, preferably using I-frame encoding techniques of video encoders, represents the input image, represents the parameter set of the feature extractor, represents the extracted feature vector or feature map.

[0012] Further, a feature database of medical images is established, and the extracted image features are stored according to classification, represented as:

[0013]

[0014] wherein, N represents the total amount of medical image data, represents the feature vector or feature map of the ith image, represents the corresponding classification label; the data in the feature library is stored according to classification .

[0015] Further, the feature extraction network is used to extract features and perform similarity matching on the medical image to be compressed, represented as:

[0016]

[0017]

[0018] wherein, represents the image to be compressed, represents the feature vector or feature map extracted from the image to be compressed, is the feature vector with the smallest distance to the current image feature , is the corresponding image data, is the corresponding image identifier, is a function of calculating the distance between the query feature and the feature in the database.

[0019] Further, the slices of the reference image and the image to be compressed are sorted based on a certain index, represented as:

[0020]

[0021]

[0022] in, is a sorting algorithm, It is a ranking indicator, such as image brightness, organ area, and image similarity.

[0023] Furthermore, the auxiliary lossy encoding of the current image according to the reference image is specifically as follows:

[0024]

[0025]

[0026] in, Represents a lossy encoding network, which converts the current image and reference images As input, output lossy coded information , Represents a lossy decoding network that converts the encoded information and the reference image As input, obtain a lossy reconstruction of the current image ;

[0027] The auxiliary lossless coding of the current image according to the original image is specifically as follows:

[0028]

[0029] in, Represents a lossless coding network, which converts the current image and lossy reconstruction The error between the two images, and the current image As input, output lossless encoding information .

[0030] Furthermore, during decompression, the image identifier Obtain reference images from a database and sort the reference images using a compressed sorting method;

[0031] Then, based on the reference image, the lossy coded information is losslessly decoded, and the formula is expressed as follows:

[0032]

[0033] The lossless coding information is then losslessly decoded, and the formula is expressed as follows:

[0034]

[0035] in, Represents a lossless decoding network that converts lossless encoding information As input, obtain a lossless reconstruction of the current image .

[0036] A massive medical image cold data joint compression system, comprising:

[0037] Feature extraction module: used to perform fine-grained feature extraction on large-scale medical images through a feature extraction network, thereby establishing a feature database of medical images and storing the extracted image features by category;

[0038] A similarity matching module, configured to use the feature extraction network to extract features and perform similarity matching on the medical image to be compressed;

[0039] A sorting module, for sorting slices of a reference image and an image to be compressed based on image brightness to optimize a compression order;

[0040] The compression module is used to assist in lossy encoding of the current image based on the reference image, and then assist in lossless encoding of the current image based on the original image.

[0041] Furthermore, it also includes a decompression module: used to obtain a reference image from a database according to an image identifier, and sort the reference image using a compressed sorting method; then, based on the reference image, lossy decoding is performed on the lossy coded information, and then lossless decoding is performed on the lossless coded information.

[0042] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the above-mentioned method for joint compression of massive medical image cold data are implemented.

[0043] A non-transitory computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for joint compression of massive medical image cold data.

[0044] Compared with the existing technology, the present invention and its preferred solution can effectively utilize the similarity and correlation between massive data, reduce the transmission code stream, and improve the compression ratio. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0046] Figure 1 is an algorithm flow chart of an embodiment of the present invention;

[0047] Figure 2 This is a bpp comparison diagram of an embodiment of the present invention;

[0048] Figure 3 A compression ratio comparison diagram of an embodiment of the present invention;

[0049] Figure 4This is a comparison diagram of bpp savings according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] To make the features and advantages of this patent more clearly understood, the following embodiments are specifically described in detail as follows:

[0051] It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meaning as commonly understood by those skilled in the art to which this application belongs.

[0052] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0053] like Figure 1 As shown, an embodiment of the present invention provides a method for joint compression of massive medical image cold data, and its implementation process includes: first, fine-grained feature extraction is performed on large-scale medical images through a feature extraction network, thereby establishing a feature database of medical images, and the extracted image features are stored by category; then, the feature extraction network is used on the medical images to be compressed to extract features and perform similarity matching; then, based on the image brightness, the slices of the reference image and the image to be compressed are sorted to optimize the compression order; finally, based on the reference image, the lossy encoding of the current image is assisted, and then based on the original image, the lossless encoding of the current image is assisted.

[0054] The design of the algorithm includes the following steps:

[0055] Step S1: Prediction structure design for massive medical images;

[0056] Step S2: Design of a lossless medical image encoder based on prediction;

[0057] Step S3: Design of corresponding medical image decoder.

[0058] Wherein, step S1 is specifically implemented as follows:

[0059] Step S11: Design a feature extraction network to perform fine-grained feature extraction on large-scale medical images. The formula is as follows:

[0060]

[0061] in, Represents a feature extractor (Encoder), which is a function or model responsible for converting an image into a feature representation. represents the input image, represents the parameter set of the feature extractor, Represents the extracted feature vector or feature map. In particular, the present invention adopts the I-frame encoding technology of the video encoder to extract features. By using the features extracted by I-frame encoding, a reference image similar to the image to be compressed can be retrieved more accurately, which helps to improve compression efficiency and image quality.

[0062] Step S12: Establish a feature database of medical images, store the extracted image features by category, and form an efficient and searchable feature library. The formula is as follows:

[0063]

[0064] Where N represents the total amount of medical imaging data, Represents the feature vector or feature map of the i-th image, Indicates the corresponding classification label. The data in the feature library is classified Store for later retrieval.

[0065] Step S13: Use the same feature extraction network to extract features from the compressed medical image and perform similarity matching. The formula is as follows:

[0066]

[0067]

[0068] in, represents the feature extractor (Encoder), represents the image to be compressed, represents the parameter set of the feature extractor, Represents the feature vector or feature map extracted from the image to be compressed, is related to the current image features The eigenvector with the smallest distance, is the corresponding image data, is the corresponding image identifier, Is to calculate the query features Features in the database A function of the distance between them (such as Euclidean distance, cosine similarity).

[0069] Step S2 is specifically implemented as follows:

[0070] Step S21: Based on a certain index, the slices of the reference image and the image to be compressed are sorted to optimize the compression order. The formula is as follows:

[0071]

[0072]

[0073] in, is a sorting algorithm, It is an index for sorting, and in particular, the average brightness of the image is used in the present invention.

[0074] Step S22: assisting lossy coding of the current image based on the reference image, which is expressed as follows:

[0075]

[0076]

[0077] in, Represents a lossy encoding network, which converts the current image and reference images As input, output lossy coded information , Represents a lossy decoding network that converts the encoded information and the reference image As input, obtain a lossy reconstruction of the current image .

[0078] Step S23: assisting lossless coding of the current image based on the original image, the formula is as follows:

[0079]

[0080] in, Represents a lossless coding network, which converts the current image and lossy reconstruction The error between the two images, and the current image As input, output lossless encoding information .

[0081] Step S3 is specifically implemented as follows:

[0082] Step S31: According to step S13 Reference images are obtained from the database, and the reference images are sorted using the method of step S21.

[0083] Step S32: Lossy decoding is performed on the lossy coded information according to the reference image. The formula is as follows:

[0084]

[0085] Step S33: Lossless decoding is performed on the lossless coding information. The formula is as follows:

[0086]

[0087] in, represents a lossless decoding network that will losslessly encode information As input, obtain a lossless reconstruction of the current image .

[0088] The overall scheme of the joint compression method of the present invention is obtained through the above design. The trained model is further used to test on three data sets together with various commonly used traditional and deep neural network-based methods. The bpp (bits per pixel) required for data compression by different models is as follows: Figure 2 As shown in the figure, bpp refers to the number of effective bits per pixel. The smaller the value, the higher the image compression efficiency, that is, the fewer bits required for storage or transmission. The bpp required for the proposed method to compress the Axial, Coronal, and Sagittal datasets are 4.47, 3.59, and 4.81, respectively. Figure 3 The compression ratio is the bpp before compression divided by the bpp after compression. The larger the value, the higher the image compression efficiency. The original data is represented by 8 bits per pixel, that is, bpp=8. The compression ratios of the proposed method on the Axial, Coronal, and Sagittal datasets are 1.76, 4.66, and 2.23, respectively. The percentage of bit rate saved by various methods compared with the FLIF (Free Lossless Image Format) method is shown in the figure below. Figure 4 As shown in the figure, a positive value indicates that the bpp after compression is less than FLIF, and a negative value indicates that the bpp after compression is more than FLIF. The larger the value, the higher the image compression efficiency. The bpp savings of the proposed method on the Axial, Coronal, and Sagittal datasets are 8.0%, 10.1%, and 6.9%, respectively.

[0089] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0090] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0091] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0092] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0093] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0094] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other manner. Any person skilled in the art may utilize the above-disclosed technical content to modify or modify the present invention into equivalent embodiments. However, any simple modifications, equivalent variations, and modifications to the above embodiments that do not depart from the technical content of the present invention and are based on the technical essence of the present invention remain within the scope of protection of the present invention.

[0095] This patent is not limited to the above-mentioned best implementation method. Anyone can derive various other forms of a joint compression method for massive medical image cold data under the inspiration of this patent. All equal changes and modifications made within the scope of the patent application of this invention should fall within the scope of this patent.

Claims

1. A joint compression method for massive medical image cold data, characterized by: Perform fine-grained feature extraction on large-scale medical images through feature extraction networks, thereby establishing a feature database of medical images and storing the extracted image features by category; Then, the feature extraction network is used on the medical image to be compressed to extract features and perform similarity matching; Then, based on image brightness, the slices of the reference image and the image to be compressed are sorted to optimize the compression order; Finally, based on the reference image, assist the lossy encoding of the current image, and then based on the original image, assist the lossless encoding of the current image; The feature extraction network is used for the medical image to be compressed to extract features and perform similarity matching, which is expressed as: in, represents the image to be compressed, Represents the feature vector or feature map extracted from the image to be compressed, is related to the current image features The eigenvector with the smallest distance, is the corresponding image data, is the corresponding image identifier, Is to calculate the query features Features in the database A function of the distance between The ordering of the slices of the reference image and the image to be compressed based on the image brightness is expressed as: in, is a sorting algorithm, It is a ranking index, which is one of image brightness, organ area, and image similarity; The auxiliary lossy coding of the current image according to the reference image is specifically as follows: in, Represents a lossy encoding network, which converts the current image and reference images As input, output lossy coded information , Represents a lossy decoding network that converts the encoded information and the reference image As input, obtain a lossy reconstruction of the current image ; The auxiliary lossless coding of the current image according to the original image is specifically as follows: in, Represents a lossless coding network, which converts the current image and lossy reconstruction The error between the two images, and the current image As input, output lossless encoding information .

2. The method for joint compression of massive medical image cold data according to claim 1, characterized in that: Features are extracted using a feature extraction network, defined as: in, Representation feature extractor, used to convert images into feature representations, represents the input image, represents the parameter set of the feature extractor, Represents the extracted feature vector or feature map.

3. The method for joint compression of massive medical image cold data according to claim 1, characterized in that: Establish a feature database of medical images and store the extracted image features by classification as follows: Where N represents the total amount of medical imaging data, Represents the feature vector or feature map of the i-th image, Indicates the corresponding classification label; the data in the feature library is classified storage.

4. The method for joint compression of massive medical image cold data according to claim 1, characterized in that: During decompression, the image identifier Obtain reference images from a database and sort the reference images using a compressed sorting method; Then, based on the reference image, the lossy coded information is losslessly decoded, and the formula is expressed as follows: The lossless coding information is then losslessly decoded, and the formula is expressed as follows: in, Represents a lossless decoding network that converts lossless encoding information As input, obtain a lossless reconstruction of the current image .

5. A massive medical image cold data joint compression system, based on the method according to any one of claims 1 to 4, characterized in that: include: Feature extraction module: used to perform fine-grained feature extraction on large-scale medical images through a feature extraction network, thereby establishing a feature database of medical images and storing the extracted image features by category; A similarity matching module, configured to use the feature extraction network to extract features and perform similarity matching on the medical image to be compressed; A sorting module, for sorting slices of a reference image and an image to be compressed based on image brightness to optimize a compression order; The compression module is used to assist in lossy encoding of the current image based on the reference image, and then assist in lossless encoding of the current image based on the original image.

6. The massive medical image cold data joint compression system according to claim 5, characterized in that: It also includes a decompression module: used to obtain a reference image from a database according to an image identifier, and sort the reference image using a compressed sorting method; then, based on the reference image, lossy decoding is performed on the lossy coded information, and then lossless decoding is performed on the lossless coded information.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method for joint compression of massive medical image cold data as described in any one of claims 1 to 4 are implemented.

Images