Method, device, equipment, medium and computer program product for obtaining orthopedic model data

Abstract

The present application relates to the field of image processing technology, and specifically to a method, apparatus, device, medium and computer program product for acquiring orthopedic model data. The method filters the initial bone image based on a preset filter model combination, analyzes the filtering effect in combination with the corresponding denoised bone image, and dynamically adjusts the preset filter parameters according to the filtering effect to ensure that the filter parameters accurately match the actual needs of the image. The reference filter parameters are statistically analyzed, the parameter sub-ranges are divided and the number is counted, providing a clear data distribution basis for screening the target filter model combination, helping to explore the potential laws of the data, optimize parameter selection, and determine the target filter model combination with the optimal parameters based on the distribution of the filter parameters, ensuring that the bone image of each part can obtain an adapted filtering scheme, significantly improving the accuracy and reliability of the filtering effect, effectively improving the quality of the bone image, and providing a guarantee for obtaining high-quality orthopedic model data.

Description

Technical Field

[0001] The present disclosure generally relates to the field of image processing technology, and more particularly to a method, apparatus, device, medium, and computer program product for acquiring orthopedic model data. Background Art

[0002] In orthopedic medical image processing, it is crucial to accurately obtain high-quality orthopedic model data. Therefore, filtering and denoising the collected orthopedic images plays an important role. Traditional image filtering methods have many shortcomings when processing bone images.

[0003] In the field of image filtering, combined filtering methods are often used to achieve better image effects. This involves simply stacking multiple filtering algorithms or combining them in a fixed order. For example, a mean filter can be used for initial noise reduction, followed by a sharpening filter to enhance image edges. Alternatively, a Gaussian filter can be used to preserve image edges, followed by a median filter to remove salt and pepper noise. By flexibly adjusting the weights and order of the various filtering methods, optimal filtering effects can be achieved.

[0004] However, in bone image processing, bone images of different parts have unique structures and noise characteristics. The existing image combination filtering method does not take into account the noise differences in different parts. It usually adopts a general combination method and fixed filtering parameters, and does not perform personalized optimization processing for bone images of different parts. As a result, the quality of the obtained orthopedic model data is difficult to guarantee, and it is impossible to provide accurate and reliable basic data for the subsequent construction of orthopedic models.

[0005] Therefore, in the task of acquiring orthopedic model data, how to improve the quality of orthopedic model data acquisition has become an urgent problem to be solved. Summary of the Invention

[0006] In view of the above-mentioned defects or deficiencies in the prior art, it is desirable to provide a method, apparatus, device, medium and computer program product for acquiring orthopedic model data.

[0007] In a first aspect, an embodiment of the present application provides a method for obtaining orthopedic model data, the method for obtaining orthopedic model data comprising the following steps:

[0008] S1, obtain N part labels, a preset filter model combination corresponding to each part label, several initial bone images corresponding to each part label, and a denoised bone image corresponding to each initial bone image, wherein each preset filter model combination corresponds to a preset filter parameter, N>1.

[0009] S2, for any initial bone image corresponding to any part label, update the preset filtering parameters corresponding to the current part label according to the preset filtering model combination corresponding to the current part label, the current initial bone image and the denoised bone image corresponding to the current initial bone image, and obtain the reference filtering parameters of the current part label relative to the current initial bone image.

[0010] S3 , according to the preset step size and all reference filtering parameters corresponding to the current part label, obtain several filtering parameter sub-ranges corresponding to the current part label and the number of reference filtering parameters included in each filtering parameter sub-range.

[0011] S4. Obtain a target filter model combination corresponding to the current part label according to the number of reference filter parameters included in each filter parameter sub-range.

[0012] S5, when the bone image to be processed is obtained, a target filter model combination corresponding to the bone image to be processed is obtained according to the part label corresponding to the bone image to be processed.

[0013] S6, filtering the bone image to be processed according to the target filtering model combination corresponding to the bone image to be processed to obtain a target bone image, wherein the target bone image belongs to orthopedic model data.

[0014] In a second aspect, an embodiment of the present application provides a device for acquiring orthopedic model data, the device for acquiring orthopedic model data comprising:

[0015] The data acquisition module is used to obtain N part labels, a preset filtering model combination corresponding to each part label, several initial bone images corresponding to each part label, and a denoised bone image corresponding to each initial bone image, wherein each preset filtering model combination corresponds to a preset filtering parameter, N>1.

[0016] The parameter updating module is used to update the preset filtering parameters corresponding to any initial bone image corresponding to any part label according to the preset filtering model combination corresponding to the current part label, the current initial bone image and the denoised bone image corresponding to the current initial bone image, and obtain the reference filtering parameters of the current part label relative to the current initial bone image.

[0017] The parameter analysis module is used to obtain several filter parameter sub-ranges corresponding to the current part label and the number of reference filter parameters contained in each filter parameter sub-range based on the preset step size and all reference filter parameters corresponding to the current part label.

[0018] The parameter determination module is used to obtain the target filtering model combination corresponding to the current part label according to the number of reference filtering parameters contained in each filtering parameter sub-range.

[0019] The model acquisition module is used to obtain the target filtering model combination corresponding to the bone image to be processed according to the part label corresponding to the bone image to be processed when the bone image to be processed is obtained.

[0020] The image filtering module is used to filter the bone image to be processed according to the target filtering model combination corresponding to the bone image to be processed, and obtain the target bone image, wherein the target bone image belongs to orthopedic model data.

[0021] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the embodiment of the present application when executing the program.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored, which, when executed by a processor, implements the method described in the embodiment of the present application.

[0023] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which implements the method described in the embodiment of the present application when executed by a processor.

[0024] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Other features, objects and advantages of the present application will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0026] Figure 1 A diagram illustrating an implementation environment architecture of a method for acquiring orthopedic model data provided in an embodiment of the present application is shown;

[0027] Figure 2 A schematic diagram showing a flow chart of a method for acquiring orthopedic model data provided in one embodiment of the present application is shown;

[0028] Figure 3 Another flowchart of the method for obtaining orthopedic model data provided by one embodiment of the present application is shown;

[0029] Figure 4 Another flowchart of the method for obtaining orthopedic model data provided by one embodiment of the present application is shown;

[0030] Figure 5 Another flowchart of the method for obtaining orthopedic model data provided by one embodiment of the present application is shown;

[0031] Figure 6 Another flowchart of the method for obtaining orthopedic model data provided by one embodiment of the present application is shown;

[0032] Figure 7 Another flowchart of the method for obtaining orthopedic model data provided by one embodiment of the present application is shown;

[0033] Figure 8 An exemplary structural block diagram of an apparatus for acquiring orthopedic model data provided in one embodiment of the present application is shown;

[0034] Figure 9 A schematic diagram of the structure of a computer system of an electronic device or server suitable for implementing an embodiment of the present application is shown.

[0035] Figure 1: 101-terminal device, 102-server; 800-a device for acquiring orthopedic model data, 81-data acquisition module, 82-parameter updating module, 83-parameter analysis module, 84-parameter determination module, 85-model acquisition module, 86-image filtering module; 901-CPU, 902-ROM, 903-RAM, 904-bus, 905-I / O interface, 906-input part, 907-output part, 908-storage part, 909-communication part, 910-drive, 911-removable medium. DETAILED DESCRIPTION

[0036] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the invention are shown in the accompanying drawings.

[0037] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0038] The specific implementation environment of the method for obtaining orthopedic model data proposed in this application can be found in Figure 1 . Figure 1 The following diagram shows the implementation environment architecture of the method for obtaining orthopedic model data provided in an embodiment of the present application.

[0039] like Figure 1 As shown, the implementation environment architecture includes: a terminal device 101 and a server 102.

[0040] The terminal device 101 can be a desktop computer, a laptop computer, a smart phone, a tablet computer, an e-book reader, smart glasses, a smart watch, etc., but is not limited thereto.

[0041] Server 102 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. Server 102 is used to provide replacement terms to terminal device 101 and execute strategies for identifying abnormalities in target terms.

[0042] The terminal device 101 and the server 102 are connected directly or indirectly via wired or wireless communication. Optionally, the wireless or wired network uses standard communication technologies and / or protocols. The network is typically the Internet, but can also be any network, including but not limited to a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a private network, or any combination of a virtual private network.

[0043] The method for acquiring orthopedic model data proposed in the present application can be implemented by an orthopedic model data acquisition device, and the orthopedic model data acquisition device can be installed on a terminal device or a server.

[0044] In order to further illustrate the technical solutions provided by the embodiments of the present application, this is described in detail below with reference to the accompanying drawings and specific implementation methods. Although the embodiments of the present application provide the method operation instruction steps shown in the following embodiments or drawings, more or fewer operation instruction steps may be included in the method based on conventional or no creative labor. In the steps where there is no necessary causal relationship logically, the execution order of these steps is not limited to the execution order provided by the embodiments of the present application. During the actual processing process or when the device is executed, the method may be executed in the order of the methods shown in the embodiments or drawings or in parallel.

[0045] It should be noted that the acquisition or use of data in the embodiments of this application requires the user's consent. The relevant data can only be obtained after the user's authorization and permission, and the acquisition or use of the data complies with relevant legal provisions.

[0046] Please refer to Figure 2 , Figure 2 FIG. 1 is a flow chart showing a method for obtaining orthopedic model data provided by an embodiment of the present application. Figure 2As shown, the method for obtaining the orthopedic model data includes:

[0047] S1, obtain N part labels, a preset filter model combination corresponding to each part label, several initial bone images corresponding to each part label, and a denoised bone image corresponding to each initial bone image, wherein each preset filter model combination corresponds to a preset filter parameter, N>1.

[0048] S2, for any initial bone image corresponding to any part label, update the preset filtering parameters corresponding to the current part label according to the preset filtering model combination corresponding to the current part label, the current initial bone image and the denoised bone image corresponding to the current initial bone image, and obtain the reference filtering parameters of the current part label relative to the current initial bone image.

[0049] S3 , according to the preset step size and all reference filtering parameters corresponding to the current part label, obtain several filtering parameter sub-ranges corresponding to the current part label and the number of reference filtering parameters included in each filtering parameter sub-range.

[0050] S4. Obtain a target filter model combination corresponding to the current part label according to the number of reference filter parameters included in each filter parameter sub-range.

[0051] S5, when the bone image to be processed is obtained, a target filter model combination corresponding to the bone image to be processed is obtained according to the part label corresponding to the bone image to be processed.

[0052] S6, filtering the bone image to be processed according to the target filtering model combination corresponding to the bone image to be processed to obtain a target bone image, wherein the target bone image belongs to orthopedic model data.

[0053] Among them, the part label can be the skull, humerus, femur, tibia, wrist joint, ankle joint, etc., which are used to identify different bone parts.

[0054] Each part label corresponds to a set of preset filter model combinations. For example, the skull is a relatively complex structure, with numerous sutures, holes, and relatively flat areas. Its morphology, structural details, and relationship with surrounding tissues must be clearly displayed in the image, while noise interference must be suppressed to prevent the observation of fine structures. Therefore, Gaussian filtering combined with wavelet filtering can be used. Gaussian filtering can effectively remove Gaussian noise from the image, smoothing the image while preserving the general outline of the skull. Wavelet filtering can perform multi-scale image analysis, highlighting the edges and details of the skull, facilitating the observation of fine structures such as sutures. The femur is the longest tubular bone in the human body. In the image, the integrity of the diaphysis, the thickness of the cortex, and the morphology of the joints at both ends require high image contrast and edge clarity. Therefore, bilateral filtering combined with Laplacian sharpening can be used. Bilateral filtering can effectively preserve the edge information of the femur while removing noise, ensuring clear boundaries between the diaphysis and joints. Laplacian sharpening can enhance image contrast, highlighting edge features such as the femoral cortex, and facilitating the observation of bone quality.

[0055] Each preset filter model combination has preset filter parameters, such as the standard deviation of Gaussian filtering, the window size of median filtering, etc.

[0056] Initial bone images, acquired through X-rays, CT scans, and other equipment, contain noise and other interfering information. By performing computer-generated reconstruction on these initial bone images, the raw data acquired from different angles or layers is reconstructed into intuitive two-dimensional or three-dimensional images, removing noise interference, enhancing image resolution and clarity, and highlighting detailed information. This results in a near-ideal denoised bone image, which serves as the basis for optimizing filtering parameters.

[0057] The current initial bone image can be filtered according to the preset filtering model combination corresponding to the current part label. The filtering effect can be analyzed in combination with the corresponding denoised bone image, and the preset filtering parameters can be updated according to the filtering effect to improve the filtering effect of the initial bone image of the corresponding part.

[0058] All reference filter parameters under the current part label are analyzed according to the preset step size, the reference filter parameters are divided into several filter parameter sub-ranges, and the number of reference filter parameters contained in each sub-range is counted. This will help to find a parameter range that is more representative of the filtering effect through statistical distribution, thereby determining the target filter parameters that are more suitable for the current body part and obtaining a target filter model combination, which is used to filter the bone image to be processed, and obtain a higher quality target bone image as orthopedic model data to be transmitted to the orthopedic model database for subsequent medical research and orthopedic model construction.

[0059] In a specific embodiment, the target filtering model combination includes a first filtering model and a second filtering model, wherein the target filtering parameters include a first filtering parameter and a first weight corresponding to the first filtering model, and a second filtering parameter and a second weight corresponding to the second filtering model.

[0060] The value of each parameter in the target filtering parameters can be obtained by dividing the parameter sub-ranges in step S3, counting the number of parameters and determining the target value in step S4, and further combined to form the target filtering parameters.

[0061] The first filter parameters obtained according to the above steps are (a0, a1, a2, a3, a4, a5), the first weight is q1, and the corresponding first filter model is f(x, y) = a0 + a1×x + a2×y + a3×x 2 + a4×x×y+ a5×y 2 The second filter parameter is (Z, Q), and the convolution kernel of size (L, L) is obtained according to Q, where L=(Q-1) / 2. Correspondingly, the second filter model is g(x, y)=(1 / (2×π×Z 2 ))×e^(-((x 2 +y 2 ) / (2×Z 2 ))) represents the coefficient of the convolution kernel at position (x, y), and the second weight is q2.

[0062] The pixel value at the position (x, y) in the bone image to be processed is h(x, y), and the bone image to be processed is filtered according to the target filter model combination corresponding to the bone image to be processed, and the pixel value at the position (x, y) in the target bone image is obtained as c(x, y)=q1×(h(x, y)×f(x, y))+q2×(Σ u=-L L (Σ v=-L L (h(x+u, y+v)×g(u, v)))). Where h(x+u, y+v) is the value of the pixel at position (x+u, y+v) in the bone image to be processed.

[0063] As described above, the initial bone image is filtered based on the preset filter model combination, and the filtering effect is analyzed in combination with the corresponding denoised bone image, so that the preset filter parameters are dynamically adjusted according to the filtering effect to ensure that the filter parameters are accurately matched with the actual needs of each image, and the reference filter parameters are statistically analyzed, the parameter sub-ranges are divided and the number is counted, which provides a clear data distribution basis for screening the target filter parameters, helps to explore the potential laws of the data, optimize parameter selection, and determine the optimal target filter parameters and target filter model combination based on the distribution of filter parameters, ensuring that the bone images of each part can obtain an adapted filtering scheme, significantly improving the accuracy and reliability of the filtering effect, effectively improving the quality of bone images, and providing a guarantee for the acquisition of high-quality orthopedic model data.

[0064] Please refer to Figure 3 , Figure 3 The flowchart of step S2 provided in one embodiment of the present application is shown. S2 includes the following steps:

[0065] S21 , filtering the current initial bone image according to a preset filter model combination corresponding to the current part label, and obtaining an intermediate bone image corresponding to the current initial bone image.

[0066] S22, obtaining the degree of image difference according to the intermediate bone image and the denoised bone image corresponding to the current initial bone image.

[0067] S23, for all the initial bone images corresponding to the current part label and the denoised bone images corresponding to each initial bone image, construct an objective function according to the degree of image difference, and update the preset filtering parameters corresponding to the preset filtering model combination corresponding to the current part label by the gradient descent method until the objective function converges, and determine the updated preset filtering parameters corresponding to the convergence of the objective function as the reference filtering parameters of the current part label relative to the current initial bone image.

[0068] Among them, the intermediate bone image is compared with the denoised bone image to quantify the degree of difference, which reflects the gap between the processing effect of the preset filtering model combination on the initial bone image under the current preset filtering parameters and the ideal denoising effect. By clarifying the effect gap, it provides direction and basis for the subsequent adjustment of the filtering parameters.

[0069] In a specific embodiment, the grayscale value difference between each pixel point in the intermediate bone image and the pixel point at the same position in the denoised bone image is obtained respectively, and the average value of the grayscale value difference between all the pixels is determined as the image difference degree between the intermediate bone image and the denoised bone image corresponding to the current initial bone image.

[0070] The gradient descent method quickly and efficiently finds the parameter values that minimize the objective function—that is, the filter parameters that minimize the difference between the intermediate bone image and the denoised bone image. Therefore, the updated preset filter parameters corresponding to convergence are used as the reference filter parameters. This optimizes the preset filter parameters, making them more closely aligned with the actual characteristics of the bone image at the current location, thereby improving the filtering effect.

[0071] The above process, from initial filtering to generate intermediate bone images, to quantifying the degree of image differences, and then using the optimization algorithm to update the preset filtering parameters, finally obtains the reference filtering parameters for the initial bone image of the current part. This helps to better adapt to the characteristics of bone images in different parts, improves the accuracy of filtering processing, and enables the subsequently processed images to more clearly present the structure and characteristics of the bones, thereby improving the quality of orthopedic model data.

[0072] Please refer to Figure 4 , Figure 4 The flowchart of step S3 provided in one embodiment of the present application is shown. S3 includes the following steps:

[0073] S31 , obtaining a total range of filtering parameters corresponding to the current part label according to all reference filtering parameters corresponding to the current part label.

[0074] S32 , obtaining a plurality of sub-ranges of filtering parameters corresponding to the current part label according to the preset step size and the total range of filtering parameters corresponding to the current part label.

[0075] S33 , according to the inclusion relationship between each reference filtering parameter and each filtering parameter sub-range, obtaining the number of reference filtering parameters included in each filtering parameter sub-range.

[0076] The preset step size determines the degree of refinement of the sub-range. The specific value of the preset step size can be set by the implementer based on actual conditions or based on the total range of the filter parameters. For example, the preset step size d = 0.1 × (b a ), where b refers to the upper limit of the total range of the filter parameters and a refers to the lower limit of the total range of the filter parameters.

[0077] By determining the filter parameter sub-range to which each reference filter parameter belongs, the number of reference filter parameters contained in each filter parameter sub-range is counted, and the filter parameter values corresponding to the filter parameter sub-range that appears frequently are more suitable for filtering the part with better effect.

[0078] As mentioned above, from determining the total range of filtering parameters to dividing the filtering parameters into sub-ranges, and then to counting the number of parameters contained in the filtering parameter sub-ranges, the distribution characteristics of the filtering parameters have been gradually and deeply explored, which is helpful to discover the concentration trend and discrete degree of parameters when the filtering processing effect of bone images in different parts is better, thereby improving the reliability of obtaining subsequent target filtering parameters.

[0079] Please refer to Figure 5 , Figure 5 The flowchart of step S4 provided in one embodiment of the present application is shown. S4 includes the following steps:

[0080] S41 , determining a filter parameter sub-range corresponding to the maximum value of the number of reference filter parameters as an intermediate range corresponding to the current part label.

[0081] S42, determining the filter parameter corresponding to the center of the middle range as the target filter parameter corresponding to the current part label;

[0082] S43, obtaining a target filtering model combination according to the target filtering parameters corresponding to the current part label.

[0083] The center value of the middle range is representative and stable, integrating the characteristics of many reference filter parameters within the range. Therefore, using the center value as the target filter parameter not only avoids the blindness of randomly selecting among many parameters, but also balances the differences in parameters within the range to a certain extent, making the target filter parameter more suitable for the filtering requirements of the bone image at that location, thereby improving the filtering effect.

[0084] Please refer to Figure 6 , Figure 6 Another flow chart of a method for acquiring orthopedic model data provided by an embodiment of the present application is shown. Each target filter model combination includes M different types of preset filter models, M>1, and the method for acquiring orthopedic model data further includes the following steps:

[0085] S7, grouping the N part labels according to the target filtering model combination corresponding to each part label, and obtaining several part label combinations, wherein all part labels in the same part label combination correspond to the same M preset filtering models.

[0086] S8, for any part label combination, cluster all target filter parameters corresponding to the current part label combination to obtain several isolated target filter parameters, several filter parameter sets and the filter parameter center corresponding to each filter parameter set.

[0087] S9, correcting the target filtering parameter corresponding to each part label in the current part label combination according to several isolated target filtering parameters corresponding to the current part label combination, several filtering parameter sets, and the filtering parameter center corresponding to each filtering parameter set.

[0088] Among them, N part labels are grouped according to the target filtering model combination, and the part labels using the same M preset filtering models are grouped together, which helps to uniformly manage and process parts with similar filtering requirements and reduce repeated operations and calculations.

[0089] By grouping, the filtering strategy can be optimized for each group of part labels, and a common filtering strategy can be formulated for some parts, thereby improving the efficiency of acquiring orthopedic model data.

[0090] For each part label combination, all corresponding target filter parameters are clustered, similar target filter parameters are grouped together to form a filter parameter set, and the center of each set is found. At the same time, isolated target filter parameters are identified, which helps to discover the inherent laws and distribution patterns between the target filter parameters and provide a basis for subsequent correction of the filter parameters.

[0091] As mentioned above, the part labels with the same filtering model are integrated, which reduces repeated processing steps, improves the efficiency of the entire orthopedic model data acquisition process, and deeply explores the relationship between the target filtering parameters. By correcting the target filtering parameters, the filtering parameters of each part are made more reasonable and accurate, which helps to improve the filtering effect and reduce the filtering error caused by unreasonable parameters, thereby improving the quality of orthopedic model data.

[0092] Please refer to Figure 7 , Figure 7 The flowchart of step S9 provided in one embodiment of the present application is shown. S9 includes the following steps:

[0093] S91 , for any part label in the current part label combination, if the target filtering parameter corresponding to the current part label is an isolated target filtering parameter, then the target filtering parameter corresponding to the current part label is kept unchanged.

[0094] S92: If the target filtering parameter corresponding to the current part label is in the filtering parameter set, the target filtering parameter corresponding to the current part label is corrected to the filtering parameter center corresponding to the corresponding filtering parameter set.

[0095] Among them, the isolated target filtering parameters represent that the part has unique imaging characteristics or filtering requirements, which are quite different from other parts. Therefore, the unique filtering settings of the part are retained to ensure that the filtering processing for these special parts can accurately reflect their actual conditions, avoid poor filtering effects due to forced unification of parameters, and ensure the accuracy of image processing of special parts.

[0096] The parameters in the filter parameter set have certain similarities and represent a group of parts with similar filtering requirements. Therefore, using the center of the set as the corrected parameter can comprehensively consider the characteristics of all parameters in the set, making the filtering parameters of this part more reasonable and representative, thereby balancing the filtering effects of each part in the set and improving the overall filtering quality and consistency.

[0097] The above mentioned methods not only take into account the uniqueness of the filtering parameters of isolated targets, but also pay attention to the commonalities of a set of parts with similar filtering requirements. By adopting different processing methods, we can not only retain the personalized filtering settings of special parts, but also uniformly optimize parts with similar characteristics, making the filtering processing in the entire orthopedic model data acquisition process more comprehensive and reasonable, thereby improving the accuracy and effectiveness of the filtering processing and providing a guarantee for obtaining high-quality orthopedic model data.

[0098] It should be noted that although the operations of the method of the present application are described in a particular order in the drawings, this does not require or imply that these operations must be performed in this particular order, or that all illustrated operations must be performed to achieve the desired results.

[0099] Please refer to Figure 8 , Figure 8 FIG. 8 shows an exemplary structural block diagram of an orthopedic model data acquisition device 800 provided in one embodiment of the present application. Figure 8 As shown, the orthopedic model data acquisition device 800 includes:

[0100] The data acquisition module 81 is used to obtain N part labels, a preset filtering model combination corresponding to each part label, several initial bone images corresponding to each part label, and a denoised bone image corresponding to each initial bone image, wherein each preset filtering model combination corresponds to a preset filtering parameter, N>1.

[0101] The parameter updating module 82 is used to update the preset filtering parameters corresponding to any initial bone image corresponding to any part label according to the preset filtering model combination corresponding to the current part label, the current initial bone image and the denoised bone image corresponding to the current initial bone image, and obtain the reference filtering parameters of the current part label relative to the current initial bone image.

[0102] The parameter analysis module 83 is used to obtain several filtering parameter sub-ranges corresponding to the current part label and the number of reference filtering parameters contained in each filtering parameter sub-range according to the preset step size and all reference filtering parameters corresponding to the current part label.

[0103] The parameter determination module 84 is configured to obtain a target filter model combination corresponding to the current part label according to the number of reference filter parameters contained in each filter parameter sub-range.

[0104] The model acquisition module 85 is used to acquire the target filtering model combination corresponding to the bone image to be processed according to the part label corresponding to the bone image to be processed when the bone image to be processed is acquired.

[0105] The image filtering module 86 is used to filter the bone image to be processed according to the target filtering model combination corresponding to the bone image to be processed to obtain the target bone image, wherein the target bone image belongs to orthopedic model data.

[0106] In one embodiment, the parameter updating module 82 includes:

[0107] The image filtering submodule is used to filter the current initial bone image according to the preset filtering model combination corresponding to the current part label to obtain the intermediate bone image corresponding to the current initial bone image.

[0108] The difference degree acquisition submodule is used to obtain the image difference degree according to the intermediate bone image and the denoised bone image corresponding to the current initial bone image.

[0109] The parameter updating submodule is used to construct an objective function according to the degree of image difference for all initial bone images corresponding to the current part label and the denoised bone image corresponding to each initial bone image, and to update the preset filtering parameters corresponding to the preset filtering model combination corresponding to the current part label by the gradient descent method until the objective function converges. The updated preset filtering parameters corresponding to the convergence of the objective function are determined as the reference filtering parameters of the current part label relative to the current initial bone image.

[0110] In one embodiment, the parameter analysis module 83 includes:

[0111] The parameter total range acquisition submodule is used to obtain the total range of filtering parameters corresponding to the current part label based on all reference filtering parameters corresponding to the current part label.

[0112] The parameter sub-range acquisition submodule is used to acquire several filter parameter sub-ranges corresponding to the current part label according to the preset step size and the total range of filter parameters corresponding to the current part label.

[0113] The parameter quantity acquisition submodule is used to acquire the quantity of reference filtering parameters included in each filtering parameter subrange according to the inclusion relationship between each reference filtering parameter and each filtering parameter subrange.

[0114] In one embodiment, the parameter determination module 84 includes:

[0115] The middle range acquisition submodule is used to determine the filtering parameter subrange corresponding to the maximum value of the number of reference filtering parameters as the middle range corresponding to the current part label.

[0116] The target filter parameter acquisition submodule is used to determine the filter parameter corresponding to the center of the middle range as the target filter parameter corresponding to the current part label;

[0117] The target filter model combination acquisition submodule is used to obtain the target filter model combination according to the target filter parameters corresponding to the current part label.

[0118] In a specific embodiment, each target filter model combination includes M different types of preset filter models, M>1, and the orthopedic model data acquisition device further includes:

[0119] The part label combination acquisition module is used to group N part labels according to the target filtering model combination corresponding to each part label, and obtain several part label combinations, where all part labels in the same part label combination correspond to the same M preset filtering models.

[0120] The parameter clustering module is used to cluster all target filter parameters corresponding to any part label combination, and obtain several isolated target filter parameters, several filter parameter sets and the filter parameter center corresponding to each filter parameter set.

[0121] The parameter correction module is used to correct the target filtering parameters corresponding to each part label in the current part label combination according to several isolated target filtering parameters corresponding to the current part label combination, several filtering parameter sets and the filtering parameter center corresponding to each filtering parameter set.

[0122] In a specific embodiment, the parameter correction module includes:

[0123] The first parameter correction submodule is used to keep the target filtering parameter corresponding to the current part label unchanged for any part label in the current part label combination if the target filtering parameter corresponding to the current part label is an isolated target filtering parameter.

[0124] The second parameter correction submodule is used to correct the target filtering parameter corresponding to the current part label to the filtering parameter center corresponding to the corresponding filtering parameter set if the target filtering parameter corresponding to the current part label is in the filtering parameter set.

[0125] It should be understood that the modules or modules recorded in the orthopedic model data acquisition device 800 are similar to those in the reference Figure 2 The steps in the method described above correspond to each other. Thus, the operations and features described above for the method are also applicable to the acquisition device 800 of orthopedic model data and the modules contained therein, and will not be repeated here. The acquisition device 800 of orthopedic model data can be pre-implemented in a browser or other security application of an electronic device, or can be loaded into the browser or its security application of an electronic device by downloading or other means. The corresponding modules in the acquisition device 800 of orthopedic model data can cooperate with the modules in the electronic device to implement the solution of the embodiment of the present application.

[0126] The several modules or units mentioned in the detailed description above are not necessarily divided into one module or unit. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0127] Reference below Figure 9 , Figure 9 A schematic diagram of the structure of a computer system of an electronic device or server suitable for implementing an embodiment of the present application is shown.

[0128] like Figure 9 As shown, the computer system includes a CPU 901 (central processing unit), which can perform various appropriate actions and processes according to programs stored in a ROM 902 (read-only memory) or programs loaded from a storage unit 908 into a RAM 903 (random access memory). RAM 903 also stores various programs and data necessary for the system's operating instructions. CPU 901, ROM 902, and RAM 903 are interconnected via a bus 904. An input / output (I / O) interface 905 is also connected to bus 904.

[0129] The following components are connected to the I / O interface 905: an input section 906 including a keyboard, a mouse, and the like; an output section 907 including devices such as a CRT (cathode ray tube), an LCD (liquid crystal display), and speakers; a storage section 908 including devices such as a hard disk; and a communication section 909 including a network interface card such as a LAN card or a modem. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the I / O interface 905 as needed. Removable media 911, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 910 as needed, so that computer programs read therefrom can be installed in the storage section 908 as needed.

[0130] In particular, according to the embodiment of the present application, the above reference flow chart Figure 2 The described process can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 909, and / or installed from a removable medium 911. When the computer program is executed by the CPU 901, the above-mentioned functions defined in the system of the present application are executed.

[0131] It should be noted that the computer-readable medium described herein may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable storage media may include, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, RAM, ROM, erasable programmable read-only memory (EPROM or flash memory), optical fiber, CD-ROM (portable compact disk read-only memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. This propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical cable, RF, or any suitable combination thereof.

[0132] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operating instructions of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the aforementioned module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than the order marked in the accompanying drawings. For example, the boxes represented by two connections can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operating instruction, or can be implemented using a combination of dedicated hardware and computer instructions.

[0133] The units or modules involved in the embodiments described in this application may be implemented in software or hardware. The units or modules described may also be provided in a processor. The names of these units or modules do not, in certain circumstances, constitute limitations on the units or modules themselves.

[0134] As another aspect, the present application further provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments, or may exist independently and not be incorporated into the electronic device. The computer-readable storage medium stores one or more programs, which, when used by one or more processors, execute the method for acquiring orthopedic model data described in the present application.

[0135] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by a specific combination of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned disclosed concepts. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for acquiring orthopedic model data, characterized in that: The acquisition method includes: S1, obtaining N part labels, a preset filter model combination corresponding to each part label, a number of initial bone images corresponding to each part label, and a denoised bone image corresponding to each initial bone image, wherein each preset filter model combination corresponds to a preset filter parameter, and N>1; S2, for any initial bone image corresponding to any part label, based on the preset filtering model combination corresponding to the current part label, the current initial bone image, and the denoised bone image corresponding to the current initial bone image, updating the preset filtering parameters corresponding to the current part label, and obtaining the reference filtering parameters of the current part label relative to the current initial bone image; S3, obtaining, based on a preset step size and all reference filtering parameters corresponding to the current part label, several filtering parameter subranges corresponding to the current part label and the number of reference filtering parameters contained in each filtering parameter subrange, wherein the preset step size d = 0.1 × (b a ), b is the upper limit of the total filtering parameter range, and a is the lower limit of the total filtering parameter range; S4, obtaining a target filter model combination corresponding to the current part label according to the number of reference filter parameters contained in each filter parameter sub-range; S5, when the bone image to be processed is obtained, obtaining a target filtering model combination corresponding to the bone image to be processed according to the part label corresponding to the bone image to be processed; S6, filtering the bone image to be processed according to the target filtering model combination corresponding to the bone image to be processed to obtain a target bone image, wherein the target bone image belongs to orthopedic model data.

2. The method for acquiring orthopedic model data according to claim 1, characterized in that: S2 includes the following steps: S21, filtering the current initial bone image according to the preset filter model combination corresponding to the current part label to obtain the intermediate bone image corresponding to the current initial bone image; S22, obtaining the degree of image difference based on the intermediate bone image and the denoised bone image corresponding to the current initial bone image; S23, for all the initial bone images corresponding to the current part label and the denoised bone images corresponding to each initial bone image, construct an objective function according to the degree of image difference, and update the preset filtering parameters corresponding to the preset filtering model combination corresponding to the current part label by the gradient descent method until the objective function converges, and determine the updated preset filtering parameters corresponding to the convergence of the objective function as the reference filtering parameters of the current part label relative to the current initial bone image.

3. The method for acquiring orthopedic model data according to claim 1, characterized in that: S3 includes the following steps: S31, obtaining a total range of filtering parameters corresponding to the current part label based on all reference filtering parameters corresponding to the current part label; S32, acquiring a plurality of sub-ranges of filtering parameters corresponding to the current part label according to the preset step size and the total range of filtering parameters corresponding to the current part label; S33 , according to the inclusion relationship between each reference filtering parameter and each filtering parameter sub-range, obtaining the number of reference filtering parameters included in each filtering parameter sub-range.

4. The method for acquiring orthopedic model data according to claim 3, characterized in that: S4 includes the following steps: S41, determining the filter parameter sub-range corresponding to the maximum number of reference filter parameters as the middle range corresponding to the current part label; S42, determining the filtering parameter corresponding to the center of the middle range as the target filtering parameter corresponding to the current part label; S43, obtaining a target filtering model combination according to the target filtering parameters corresponding to the current part label.

5. The method for acquiring orthopedic model data according to claim 4, characterized in that: Each target filter model combination includes M preset filter models of different types, where M>1. The method for acquiring orthopedic model data further includes the following steps: S7, grouping the N part labels according to the target filter model combination corresponding to each part label to obtain several part label combinations, wherein all part labels in the same part label combination correspond to the same M preset filter models; S8, for any part label combination, clustering all target filter parameters corresponding to the current part label combination, obtaining several isolated target filter parameters corresponding to the current part label combination, several filter parameter sets, and the filter parameter center corresponding to each filter parameter set; S9, correcting the target filtering parameter corresponding to each part label in the current part label combination according to several isolated target filtering parameters corresponding to the current part label combination, several filtering parameter sets, and the filtering parameter center corresponding to each filtering parameter set.

6. The method for acquiring orthopedic model data according to claim 5, characterized in that: S9 includes the following steps: S91, for any part label in the current part label combination, if the target filtering parameter corresponding to the current part label is an isolated target filtering parameter, keep the target filtering parameter corresponding to the current part label unchanged; S92: If the target filtering parameter corresponding to the current part label is in the filtering parameter set, the target filtering parameter corresponding to the current part label is corrected to the filtering parameter center corresponding to the corresponding filtering parameter set.

7. A device for acquiring orthopedic model data, characterized in that: The device for acquiring orthopedic model data includes: A data acquisition module is used to obtain N part labels, a preset filter model combination corresponding to each part label, a number of initial bone images corresponding to each part label, and a denoised bone image corresponding to each initial bone image, wherein each preset filter model combination corresponds to a preset filter parameter, and N>1; A parameter updating module is used to update the preset filtering parameters corresponding to any initial bone image corresponding to any part label based on the preset filtering model combination corresponding to the current part label, the current initial bone image, and the denoised bone image corresponding to the current initial bone image, so as to obtain the reference filtering parameters of the current part label relative to the current initial bone image; A parameter analysis module is configured to obtain, based on a preset step size and all reference filtering parameters corresponding to the current part label, a plurality of filtering parameter subranges corresponding to the current part label and the number of reference filtering parameters contained in each filtering parameter subrange, wherein the preset step size d = 0.1 × (b a ), b is the upper limit of the total filtering parameter range, and a is the lower limit of the total filtering parameter range; A parameter determination module is used to obtain a target filter model combination corresponding to the current part label according to the number of reference filter parameters contained in each filter parameter sub-range; A model acquisition module is used to acquire a target filtering model combination corresponding to the bone image to be processed according to the part label corresponding to the bone image to be processed when the bone image to be processed is acquired; The image filtering module is used to filter the bone image to be processed according to the target filtering model combination corresponding to the bone image to be processed to obtain a target bone image, wherein the target bone image belongs to orthopedic model data.

8. 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 method for acquiring orthopedic model data as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for acquiring orthopedic model data as described in any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for acquiring orthopedic model data according to any one of claims 1 to 6 are implemented.

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