An image data processing method, device and computer equipment

Abstract

The application relates to an image data processing method, device and computer equipment, wherein the method comprises the following steps: when the maximum value of slice data collection times of volume data and the number of volume data meet a preset condition, determining a target value of slice data collection times in current volume data according to the maximum value of slice data collection times of volume data, the number of volume data and the total number of slice data under a current first-level volume data label; the preset condition is set according to the total number of slice data under the current first-level volume data label; the current first-level volume data label is a first-level volume data label to which slice data to be classified belongs; the current volume data is volume data to which slice data to be classified belongs; and whether the current volume data is collected completely is determined according to the target value of slice data collection times in the current volume data and the current collection times of slice data in the current volume data. Through the application, data processing is performed on the collected volume data, so that the data processing efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of image data processing, and in particular to an image data processing method and device and a computer device. BACKGROUND

[0002] Slice data generated in a medical image scanning process such as a magnetic resonance scan or a computed tomography scan needs to be post-processed to obtain a more ideal result. The post-processing operation is generally based on a set of slice objects, that is, volume data. Therefore, a set of slice data needs to be collected before the post-processing operation to obtain a complete volume data. Among them, the volume data collection work is the basis of the entire post-processing process, and the accuracy of the collection method and the rationality of the collection process will have an important impact on the entire post-processing process.

[0003] Since the order in which the images generated by the magnetic resonance system or the computed tomography system reach a certain operation node is random, and the image data of multiple volume data is crossed together, the existing image data processing collection method cannot confirm whether a certain volume data is collected completely before all volume data collection is completed. It can be determined that there are several volume data only after all image data is collected. This will cause a volume data to be collected to be not processed in time after being collected, prolong the processing time, and result in low data processing efficiency. SUMMARY

[0004] In the present application, an image data processing method, device and computer device are provided to solve the problem of low data processing efficiency in the prior art.

[0005] In a first aspect, an image data processing method is provided in the present application, the method comprising:

[0006] When the maximum value of the slice data collection times of the volume data and the number of volume data satisfy a preset condition, a target value of the slice data collection times in the current volume data is determined according to the maximum value of the slice data collection times of the volume data, the number of volume data and the total number of slice data under the current first-level volume data label;

[0007] According to the target value of the slice data collection times in the current volume data and the current collection times of the slice data in the current volume data, it is determined whether the current volume data is collected.

[0008] In some embodiments, the method further comprises:

[0009] When the maximum value of the slice data collection times of the volume data and the number of volume data do not satisfy the preset condition, the slice data to be classified is classified, and the number of volume data and the slice data collection times under the volume data are updated.

[0010] In some embodiments, the classification operation of the fragmented data to be classified includes:

[0011] Based on the primary volume data label and the secondary volume data label of the slice data to be classified, the slice data to be classified is classified into volume data.

[0012] Update the number of volume data and the number of times the current volume data's slice data is collected.

[0013] In some embodiments, when the maximum number of times the volume data fragments are collected and the number of volume data satisfy preset conditions, a target value for the number of times the volume data fragments are collected is determined based on the maximum number of times the volume data fragments are collected, the number of volume data, and the total number of fragments under the current first-level volume data tag, including:

[0014] Based on the total number of fragment data under the current first-level volume data label, a quantitative relationship correspondence table is determined between the volume data quantity of the volume data and the corresponding fragment data collection times; the quantitative relationship correspondence table includes the preset volume data quantity of the volume data and the preset number of fragment data collection times corresponding to the preset volume data quantity.

[0015] When the maximum number of times the slice data of the volume data is collected and the number of volume data meet the preset conditions in the corresponding table of quantity relationships, the target value of the number of times the slice data is collected in the current volume data is determined according to the data in the corresponding table of quantity relationships.

[0016] In some embodiments, when the maximum value of the number of times the volume data is collected in slices and the number of volume data meet preset conditions with the data in the quantity relationship correspondence table, a target value for the number of times the slice data is collected in the current volume data is determined based on the data in the quantity relationship correspondence table, including:

[0017] When the quantity of volume data equals the current preset quantity of volume data in the quantity relationship correspondence table, and the maximum value of the number of times the volume data fragments are collected is greater than the preset number of times the fragments are collected corresponding to the second preset quantity of volume data, the preset number of times the fragments are collected corresponding to the current preset quantity of volume data is determined as the target value of the number of times the fragments are collected in the current volume data; wherein, the second preset quantity of volume data is the preset quantity of volume data in the quantity relationship correspondence table that is greater than the current preset quantity of volume data and has the smallest difference from the current preset quantity of volume data.

[0018] In some embodiments, when the maximum value of the number of times the volume data is collected in slices and the number of volume data meet preset conditions with the data in the quantity relationship correspondence table, a target value for the number of times the slice data is collected in the current volume data is determined based on the data in the quantity relationship correspondence table, including:

[0019] Obtain a preset quantity of third-body data from the quantity relationship correspondence table; wherein, the preset quantity of third-body data is the preset quantity of body data in the quantity relationship correspondence table that is greater than the quantity of body data and has the smallest difference between the quantity of body data and the quantity of body data;

[0020] When the maximum value of the number of times the slice data of the volume data is collected is greater than the preset number of times the slice data is collected corresponding to the preset number of the fourth volume data, the preset number of times the slice data is collected corresponding to the preset number of the third volume data is determined as the target value of the number of times the slice data is collected in the current volume data; wherein, the preset number of the fourth volume data is the preset number of volume data in the quantity relationship correspondence table that is greater than the preset number of the third volume data and has the smallest difference with the preset number of the third volume data.

[0021] In some of these embodiments, the target number of times to collect the slice data of each volume data under the current first-level volume data label is the same.

[0022] In some embodiments, the method further includes:

[0023] Based on the primary volume data label of the slice data to be classified, determine the total number of slice data under the current primary volume data label.

[0024] Secondly, this application provides an image data processing apparatus, the apparatus comprising:

[0025] The first determining module is used to determine the target value of the number of times the slice data is collected in the current volume data when the maximum value of the number of times the slice data is collected and the number of volume data meet preset conditions, based on the maximum value of the number of times the slice data is collected in the current volume data, the number of volume data, and the total number of slice data under the current first-level volume data tag.

[0026] The second determining module is used to determine whether the current volume data has been collected completely based on the target value of the number of times the slice data in the current volume data is collected and the current number of times the slice data in the current volume data is collected.

[0027] Thirdly, this application provides a computer device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the image data processing method described in the first aspect.

[0028] Compared with the prior art, the image data processing method, apparatus, and computer equipment provided in this application improve data processing efficiency by determining a target value for the number of times the slice data is collected in the current volume data when the maximum value of the number of times slice data is collected and the number of volume data meet preset conditions, based on the maximum value of the number of times slice data is collected in the current volume data, the number of volume data, and the total number of slice data under the current first-level volume data label. Based on the target value of the number of times slice data is collected in the current volume data and the current number of times slice data is collected in the current volume data, it is determined whether the current volume data has been collected. Thus, it is possible to determine whether the corresponding volume data has been collected without waiting for all slice data under the first-level volume data label to be collected, and to process the collected volume data.

[0029] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0030] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0031] Figure 1 This is a hardware structure block diagram of a terminal that executes an image data processing method according to an embodiment of this application;

[0032] Figure 2 This is a flowchart of an image data processing method according to an embodiment of this application;

[0033] Figure 3 This is a flowchart of another image data processing method according to an embodiment of this application;

[0034] Figure 4 This is a flowchart of another image data processing method according to an embodiment of this application;

[0035] Figure 5 This is a structural block diagram of an image data processing apparatus according to an embodiment of this application. Detailed Implementation

[0036] To better understand the purpose, technical solution, and advantages of this application, the application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0037] Unless otherwise defined, the technical or scientific terms used in this application shall have the general meaning as understood by one of ordinary skill in the art to which this application pertains. Words such as “a,” “an,” “an,” “the,” “the,” and “these,” used in this application, do not indicate quantitative limitation and may be singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or modules (units) is not limited to the listed steps or modules (units) but may include steps or modules (units) not listed, or may include other steps or modules (units) inherent to such processes, methods, products, or devices. The terms “connected,” “linked,” and “coupled,” used in this application, are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. The term “multiple” used in this application refers to two or more. The "and / or" operator describes the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: A alone, A and B simultaneously, and B alone. Typically, the character " / " indicates that the objects before and after it are in an "or" relationship. The terms "first," "second," and "third," etc., used in this application are merely for distinguishing similar objects and do not represent a specific ordering of the objects.

[0038] The method embodiments provided in this application can be executed on a terminal, computer, or similar computing device. For example, they can be run on a terminal. Figure 1 This is a hardware structure block diagram of a terminal executing an image data processing method according to an embodiment of this application. For example... Figure 1 As shown, a terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 and a memory 104 for storing data are also included. The processor 102 may be, but is not limited to, a microprocessor (MCU) or a programmable logic device (FPGA). The terminal may also include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that… Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the terminal described above. For example, the terminal may also include components that are larger than... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown are illustrated.

[0039] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to an image data processing method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0040] The transmission device 106 is used to receive or send data via a network. This network includes a wireless network provided by the terminal's communication provider. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 can be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0041] This application provides a method for processing image data. Figure 2 This is a flowchart of an image data processing method according to an embodiment of this application, such as... Figure 2 As shown, the process includes the following steps:

[0042] Step S210: When the maximum number of times the volume data is collected and the number of volume data meet the preset conditions, determine the target value of the number of times the volume data is collected in the current volume data based on the maximum number of times the volume data is collected, the number of volume data, and the total number of volume data under the current first-level volume data label.

[0043] Specifically, the preset conditions are set based on the total number of fragment data under the current first-level volume data label. The current first-level volume data label is the first-level volume data label to which the fragment data to be classified belongs, and the current volume data is the volume data to which the fragment data to be classified belongs. During the fragment data collection process, the fragment data is classified according to the first-level and second-level volume data labels to which the fragment data belongs, and is assigned to the corresponding volume data and matched accordingly. The fragment data to be classified carries the total number of fragment data under the current first-level volume data label. Each first-level volume data label corresponds to one or more volume data, identified by second-level volume data labels. The number of fragments in each volume data is the same, and the preset conditions are determined based on the total number of fragment data under the current first-level volume data label. It is determined whether the maximum number of fragment data collection times and the number of volume data meet the preset conditions. When the maximum number of fragment data collection times and the number of volume data meet the preset conditions, the target value of the number of fragment data collection times in the current volume data is determined based on the maximum number of fragment data collection times, the number of volume data, and the total number of fragment data under the current first-level volume data label. The target number of times the slice data was collected can be understood as the total number of slice data in the current volume data when the current volume data collection is completed. The primary volume data label is the identifier of a group of images to which the slice data belongs, that is, it is used to identify slice data belonging to the same group of images, which includes one or more volume data. The secondary volume data label is the label under the primary volume data label, that is, one primary volume data label can include one or more secondary volume data labels, and the secondary volume data label is used to identify which volume data under the primary volume data label the slice data belongs to. In this embodiment, the volume data can be understood as three-dimensional medical image data such as CT images and MRI images (Magnetic Resonance Imaging), and the volume data includes a group of slice objects, a group of slice images, or a group of slice data.

[0044] Step S220: Determine whether the current volume data collection is complete based on the target value of the number of times the slice data in the current volume data is collected and the current number of times the slice data in the current volume data is collected.

[0045] Specifically, after determining the target value for the number of times the fragment data in the current volume data is collected according to step S210, the total number of fragment data in the current volume data is determined when the current volume data collection is completed. Based on the target value for the number of times the fragment data in the current volume data is collected and the current number of times the fragment data in the current volume data is collected, it is determined whether the current volume data collection is complete. Here, the current number of times the fragment data in the current volume data is collected can be understood as follows: after classifying the fragment data to be classified, the fragment data to be classified is assigned to the corresponding current volume data, and the number of fragment data in the current volume data is updated. The updated number of fragment data in the current volume data is the current number of times the fragment data in the current volume data is collected. In other words, the completion of the current volume data collection is determined based on the total number of fragment data in the current volume data and the updated number of fragment data in the current volume data. For example, if the number of fragment data in the updated current volume data is less than the total number of fragment data in the current volume data, it is determined that the current volume data has not been collected. If the number of fragment data in the updated current volume data is equal to the total number of fragment data in the current volume data, it is determined that the current volume data has been collected.

[0046] In this embodiment, when the maximum number of times the fragment data of the volume data is collected and the number of volume data meet preset conditions, the target value of the number of times the fragment data of the current volume data is determined based on the maximum number of times the fragment data of the volume data is collected, the number of volume data, and the total number of fragment data under the current first-level volume data label. Based on the target value of the number of times the fragment data of the current volume data is collected and the current number of times the fragment data of the current volume data is collected, it is determined whether the current volume data has been collected. Thus, it is not necessary to wait for all fragment data under the first-level volume data label to be collected before it can be determined whether the corresponding volume data has been collected. Data processing is then performed on the collected volume data, thereby improving data processing efficiency.

[0047] In some embodiments, the method further includes: when the maximum value of the number of times the volume data is collected and the number of volume data do not meet preset conditions, performing a classification operation on the volume data to be classified, and updating the number of volume data and the number of times the volume data is collected.

[0048] In some embodiments, the classification operation of the fragment data to be classified includes: classifying the fragment data to be classified into volume data based on the first-level volume data label and the second-level volume data label of the fragment data to be classified; and updating the number of volume data and the number of times the current volume data fragment data has been collected.

[0049] In some embodiments, when the maximum value of the number of times the fragment data is collected and the number of volume data meet preset conditions, a target value for the number of times the fragment data is collected in the current volume data is determined based on the maximum value of the number of times the fragment data is collected, the number of volume data, and the total number of fragment data under the current first-level volume data label. This includes: determining a correspondence table between the number of volume data and the corresponding number of times the fragment data is collected based on the total number of fragment data under the current first-level volume data label; the correspondence table includes a preset number of volume data and a preset number of times the fragment data is collected; when the maximum value of the number of times the fragment data is collected and the number of volume data meet the preset conditions in the correspondence table, the target value for the number of times the fragment data is collected in the current volume data is determined based on the data in the correspondence table.

[0050] In some embodiments, when the maximum value of the number of times the volume data is collected and the data in the volume data quantity and quantity relationship correspondence table meet preset conditions, a target value for the number of times the volume data is collected in the current volume data is determined based on the data in the quantity relationship correspondence table. This includes: when the volume data quantity is equal to the current volume data preset quantity in the quantity relationship correspondence table, and the maximum value of the number of times the volume data is collected is greater than the preset number of times the volume data is collected corresponding to the second preset quantity of volume data, the preset number of times the volume data is collected corresponding to the current preset quantity of volume data is determined as the target value for the number of times the volume data is collected in the current volume data; wherein, the second preset quantity of volume data is the preset quantity of volume data in the quantity relationship correspondence table that is greater than the current preset quantity of volume data and has the smallest difference from the current preset quantity of volume data.

[0051] In some embodiments, when the maximum value of the number of times the volume data is collected in slices and the data in the volume data correspondence table meet preset conditions, a target value for the number of times the volume data is collected in the current volume data is determined based on the data in the correspondence table. This includes: obtaining a third preset quantity of volume data in the correspondence table; wherein the third preset quantity of volume data is the volume data preset quantity in the correspondence table that is greater than the quantity of volume data and has the smallest difference from the quantity of volume data; when the maximum value of the number of times the volume data is collected in slices is greater than the preset number of times the fourth preset quantity of volume data is collected in slices, the preset number of times the third preset quantity of volume data is determined as the target value for the number of times the volume data is collected in the current volume data; wherein the fourth preset quantity of volume data is the volume data preset quantity in the correspondence table that is greater than the third preset quantity of volume data and has the smallest difference from the third preset quantity of volume data.

[0052] In some of these embodiments, the target number of times to collect the slice data of each volume data under the current first-level volume data label is the same.

[0053] In some embodiments, the method further includes: determining the total number of slice data under the current first-level volume data label based on the first-level volume data label of the slice data to be classified.

[0054] In some embodiments, the image data processing method includes an image data collection method, and particularly relates to a magnetic resonance imaging data collection method.

[0055] This application also provides a method for processing image data. Figure 3 This is a flowchart of another image data processing method according to an embodiment of this application, such as... Figure 3 As shown, the process includes the following steps:

[0056] Step S310: Read the slice data.

[0057] Specifically, the layered data here refers to two-dimensional layered data or layered images.

[0058] Step S320: Classify the slice data according to the volume data labels and match the corresponding volume data.

[0059] Specifically, volumetric data is a three-dimensional data type that contains the following information: Primary identification information: study UID, series UID, and specified frame of reference UID, etc. Pixel value information: Pixel values ​​of all slices belonging to the same volumetric data are arranged in a specific order. Spatial orientation information: Composed of slice normal vectors and principal orientations (transverse, coronal, sagittal), etc. Spatial location information: Composed of the slice center coordinates and starting point (top left corner of the image) coordinates for each slice. Slice attribute information: Composed of slice size, slice thickness, interslice spacing, and actual pixel physical spacing, etc. Dimensional index information: Composed of repetition index, echo index, phase index, etc. Other information: Such as bed information of the scan sequence to which the image belongs, acquisition type, series description information, etc. Volumetric data labels include primary volumetric data labels and secondary volumetric data labels. The volume data label includes primary volume data labels and secondary volume data labels. For example, a primary volume data label may include a sequence ID (seriesUID), representing the identifier of a slice data or a group of images to which a slice data belongs. The slice data also carries the total number of slice data included under that primary volume data label. A secondary volume data label may include the slice group ID to which the slice data belongs, the image orientation of the slice in space, the repetition index of the slice data during the scanning process, the echo index of the slice data during the scanning process, and the phase index of the slice data during the scanning process. The primary volume data label includes a sequence ID (series UID) for initial image differentiation, while the secondary volume data label includes other information for further differentiation of images under the primary volume data label.

[0060] More specifically, by judging through volume data tags, if the corresponding volume data already exists, the relevant information of this fragment data is put into the volume data; if the corresponding volume data does not exist, a new volume data is created, and then the relevant information of the fragment data is put into the volume data.

[0061] Step S330: After collecting data for each slice, determine whether the target number of times the current volume data can be collected can be determined.

[0062] Specifically, if the target number of times the current volume data can be collected can be determined, then it is determined whether the current collection count has reached the target collection count; if the target collection count cannot be determined, then the slice data is read and steps S310 to S330 are repeated.

[0063] Step S340: If the current collection count reaches the target collection count, the current volume data has been completely collected. The volume data pixel values ​​are arranged in a certain order. If the current collection count does not reach the target collection count, the slice data is read again.

[0064] Specifically, if the current collection count does not reach the target collection count, the slice data is read again, and steps S310 to S340 are repeated.

[0065] Step S350: Save and export the collected volume data for post-processing operations.

[0066] In this example, after collecting each slice of data, it is determined whether the target number of times the current volume data can be collected can be determined. If the target number of times the current volume data can be determined, it is determined whether the current collection count has reached the target number of collection count. If the current collection count has reached the target number of collection count, the current volume data has been collected completely. The collected volume data is saved and exported for use in post-processing operations. Thus, it is not necessary to wait for all slice data under the first-level volume data label to be collected before it can be determined whether the corresponding volume data has been collected. Data processing is then performed on the collected volume data, thereby improving data processing efficiency.

[0067] This application also provides a method for processing image data. Figure 4 This is a flowchart of another image data processing method according to an embodiment of this application, such as... Figure 4 As shown, the process includes the following steps:

[0068] Step S501: Read the slice data.

[0069] Step S502: Determine whether the volume data label can match the corresponding volume data; if yes, proceed to step S504; if no, proceed to step S503.

[0070] Specifically, the fragmented data is categorized using volume data tags, and corresponding volume data is matched. If the corresponding volume data already exists, the fragmented data information is added to the volume data; otherwise, a new volume data is created, and then the fragmented data information is added to it.

[0071] Step S503: Create volume data.

[0072] Step S504: Set body data information.

[0073] Specifically, setting volume data information includes putting relevant information about the current layer data into the volume data.

[0074] Step S505: Determine whether the number of times the target body data can be collected can be determined; if yes, proceed to step S506; if no, proceed to step S501.

[0075] Step S506: Set the target number of volume data collections.

[0076] Step S507: Determine whether the data has been collected completely; if yes, proceed to step S508; if no, proceed to step S501.

[0077] Specifically, if the current collection count reaches the target collection count, then the current body data has been completely collected.

[0078] Step S508: Set the volume data pixel values.

[0079] Step S509: Save the complete volume data.

[0080] Specifically, after saving the complete volume data, the volume data is exported and the slice data is read.

[0081] Step S510: Export volume data.

[0082] Step S511, post-processing operation.

[0083] In this example, after collecting each slice of data, it is determined whether the target number of times the current volume data can be collected can be determined. If the target number of times the current volume data can be determined, it is determined whether the current collection count has reached the target number of collection count. If the current collection count has reached the target number of collection count, the current volume data has been collected completely. The collected volume data is saved and exported for use in post-processing operations. Thus, it is not necessary to wait for all slice data under the first-level volume data label to be collected before it can be determined whether the corresponding volume data has been collected. Data processing is then performed on the collected volume data, thereby improving data processing efficiency.

[0084] The following section provides a detailed explanation of the method for determining the number of volume data points and the target number of collections in real time during the collection process.

[0085] Using a set of slice images or slice data corresponding to a primary volume data label as the unit of judgment, the total number of slice data contained within it is obtained. The total number of slice data is analyzed, and the number of collected volume data and the maximum number of collections of slice data within the collected volume data are updated in real time during the collection process. The relationship between these two and the total number of slice data is determined. If certain conditions are met, the target number of volume data collections can be determined during the collection process, without waiting for all slice data to be collected. The collected volume data can be processed first. The specific method is as follows:

[0086] Step S410: Obtain the total number of slice data corresponding to the first-level volume data label, denoted as Sum. The number of slices under each second-level volume data label is the same.

[0087] Step S420: Parse Sum and list the numbers divisible by it. In this way, the number of volume data points corresponding to Sum can be listed. Taking Sum = 48 as an example, the number of volume data points Gro... i and the corresponding number of collections n i The quantitative relationship correspondence table is shown in Table 1. Additionally, taking Sum = 60 as an example, the number of data points Gro... i and the corresponding number of collections n i The corresponding quantitative relationships are shown in Table 2.

[0088] Table 1 Correspondence Table of Quantitative Relationships

[0089]

[0090]

[0091] Table 2 Correspondence Table of Quantitative Relationships

[0092]

[0093] Step S430: The images under the primary volume data labels can be further classified using the secondary volume data labels to obtain the number of volume data points for the current collection count, denoted as Gro.

[0094] Step S440: After each collection, update the size of Gro in real time, and the maximum number of collections n among all volume data under the current first-level volume data label. max n max For the number of collections n i The maximum value in.

[0095] Step S450: Based on the information provided by the Sum parsing table, check Gro and n in real time. max :

[0096] When Gro=Gro i And n max >n i+1 At that time, it can be determined that the number of volume data in this set of first-level volume data labels is Gro. i .

[0097] When Gro ≠ Gro i Find the first Gro that is larger than Gro. i And n max >n i+i At that time, it can be determined that the number of volume data in this set of first-level volume data labels is Gro.i .

[0098] Step S460: Special handling is performed for boundary cases.

[0099] Sum = 1, Gro = 1.

[0100] Gro last-1 <<Gro<<Gro last At that time, Gro = Gro last Gro last This represents the number of volume data points corresponding to the last case in the Sum parsing table.

[0101] It should be noted that the steps shown in the above process or in the flowchart of the accompanying figures can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0102] This application also provides an image data processing apparatus for implementing the above embodiments and preferred embodiments, which will not be repeated hereafter. The terms "module," "unit," "subunit," etc., used below refer to combinations of software and / or hardware that perform a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0103] Figure 5 This is a structural block diagram of an image data processing apparatus according to an embodiment of this application, such as... Figure 5 As shown, the device includes:

[0104] The first determining module 510 is used to determine the target value of the number of times the slice data is collected in the current volume data when the maximum value of the number of times the slice data is collected and the number of volume data meet the preset conditions, based on the maximum value of the number of times the slice data is collected, the number of volume data, and the total number of slice data under the current first-level volume data label.

[0105] The second determining module 520 is used to determine whether the current volume data has been collected completely based on the target value of the number of times the slice data in the current volume data is collected and the current number of times the slice data in the current volume data is collected.

[0106] In some embodiments, the apparatus is further configured to perform a classification operation on the slice data to be classified and update the quantity of volume data and the number of slice data collections under the volume data when the maximum value of the number of slice data collections and the quantity of volume data do not meet preset conditions.

[0107] In some embodiments, the apparatus is further configured to classify the slicing data to be classified into volume data based on the primary volume data label and the secondary volume data label of the slicing data to be classified; and update the number of volume data and the number of times the current volume data slicing data has been collected.

[0108] In some embodiments, the device is further configured to determine a quantitative relationship correspondence table between the number of volume data and the corresponding number of times the slicing data is collected, based on the total number of slicing data under the current first-level volume data label; the quantitative relationship correspondence table includes a preset number of volume data and a preset number of times the slicing data is collected corresponding to the preset number of volume data; when the maximum value of the number of times the slicing data is collected and the number of volume data meet preset conditions with the data in the quantitative relationship correspondence table, a target value for the number of times the slicing data is collected in the current volume data is determined based on the data in the quantitative relationship correspondence table.

[0109] In some embodiments, the device is further configured to, when the quantity of volume data is equal to the current preset quantity of volume data in the quantity relationship correspondence table, and the maximum value of the number of times the volume data is collected in slice data is greater than the preset number of times the second preset quantity of volume data is collected in slice data, determine the preset number of times the current preset quantity of volume data is collected in slice data as the target value of the number of times slice data is collected in the current volume data; wherein, the second preset quantity of volume data is the preset quantity of volume data in the quantity relationship correspondence table that is greater than the current preset quantity of volume data and has the smallest difference from the current preset quantity of volume data.

[0110] In some embodiments, the apparatus is further configured to: obtain a preset quantity of third-body data in a quantity relationship correspondence table; wherein the preset quantity of third-body data is a preset quantity of body data in the quantity relationship correspondence table that is greater than the quantity of body data and has the smallest difference from the quantity of body data; when the maximum value of the number of times the body data is collected is greater than the preset number of times the fourth-body data is collected, the preset number of times the third-body data is collected is determined to be the target value of the number of times the body data is collected in the current body data; wherein the preset quantity of fourth-body data is a preset quantity of body data in the quantity relationship correspondence table that is greater than the preset quantity of third-body data and has the smallest difference from the preset quantity of third-body data.

[0111] In some embodiments, the apparatus is further configured to determine the total number of slice data under the current first-level volume data label based on the first-level volume data label of the slice data to be classified.

[0112] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0113] This application also provides an electronic device including a memory and a processor, the memory storing a computer program and the processor being configured to run the computer program to perform the steps in any of the above method embodiments.

[0114] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0115] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0116] S1, when the maximum number of times the slice data is collected and the number of volume data meet the preset conditions, determine the target value of the number of times the slice data is collected in the current volume data based on the maximum number of times the slice data is collected, the number of volume data, and the total number of slice data under the current first-level volume data label;

[0117] S2, based on the target value of the number of times the slice data in the current volume data is collected and the current number of times the slice data in the current volume data is collected, determine whether the current volume data collection is complete.

[0118] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated in this embodiment.

[0119] Furthermore, in conjunction with the image data processing method provided in the above embodiments, this embodiment can also provide a storage medium for implementation. The storage medium stores a computer program; when executed by a processor, the computer program implements the steps of any of the image data processing methods in the above embodiments.

[0120] It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. All other embodiments derived by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0121] Obviously, the accompanying drawings are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar situations based on these drawings without any creative effort. Furthermore, it is understood that although the work done in this development process may be complex and lengthy, for those skilled in the art, certain design, manufacturing, or production modifications made based on the technical content disclosed in this application are merely conventional technical means and should not be considered as insufficient disclosure of this application.

[0122] The term "embodiment" in this application refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily imply the same embodiment, nor does it imply that it is mutually exclusive with or independent of other embodiments. It will be clearly or implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0123] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of patent protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims

1. A method for processing image data, characterized in that, The method includes: Based on the total number of fragmented data under the current first-level volume data label, a quantitative relationship correspondence table is determined between the volume data quantity and the corresponding fragmented data collection times. When the maximum value of the fragmented data collection times of the volume data and the volume data quantity meet the preset conditions in the quantitative relationship correspondence table, a target value for the fragmented data collection times in the current volume data is determined based on the data in the quantitative relationship correspondence table. When the maximum value of the fragmented data collection times of the volume data and the volume data quantity meet the preset conditions in the quantitative relationship correspondence table, a target value for the fragmented data collection times in the current volume data is determined based on the data in the quantitative relationship correspondence table, including: when the volume data quantity is equal to the current volume data preset quantity in the quantitative relationship correspondence table, and the maximum value of the fragmented data collection times of the volume data is greater than the preset fragmented data collection times corresponding to the second volume data preset quantity, the preset fragmented data collection times corresponding to the current volume data preset quantity is determined as the target value for the fragmented data collection times in the current volume data. Based on the target value of the number of times the slice data in the current volume data is collected and the current number of times the slice data in the current volume data is collected, it is determined whether the current volume data collection is complete.

2. The image data processing method according to claim 1, characterized in that, The method further includes: When the maximum number of times the volume data is collected and the number of volume data do not meet the preset conditions, the volume data to be classified is classified, and the number of volume data and the number of times the volume data is collected are updated.

3. The image data processing method according to claim 2, characterized in that, The classification operation for the fragmented data to be classified includes: Based on the primary volume data label and the secondary volume data label of the slice data to be classified, the slice data to be classified is classified into volume data. Update the number of volume data and the number of times the current volume data's slice data is collected.

4. The image data processing method according to claim 1, characterized in that, When the maximum number of times the volume data is collected and the number of volume data meet the preset conditions in the quantity relationship correspondence table, the target value of the number of times the volume data is collected in the current volume data is determined according to the data in the quantity relationship correspondence table, and the method further includes: Obtain the preset quantity of third-party data from the table corresponding to the quantity relationship; When the maximum value of the number of times the slice data of the volume data is collected is greater than the preset number of times the slice data is collected corresponding to the preset number of the fourth volume data, the preset number of times the slice data is collected corresponding to the preset number of the third volume data is determined as the target value of the number of times the slice data is collected in the current volume data.

5. The image data processing method according to claim 1, characterized in that, The target collection number of the slice data of each volume data under the current first-level volume data label is the same.

6. The image data processing method according to claim 1, characterized in that, The method further includes: Based on the primary volume data label of the slice data to be classified, determine the total number of slice data under the current primary volume data label.

7. An image data processing apparatus, characterized in that, The device includes: The first determining module is used to determine a quantitative relationship correspondence table between the number of volume data and the corresponding number of fragment data collections of the current volume data based on the total number of fragment data under the current first-level volume data label; when the maximum value of the number of fragment data collections of the volume data and the number of volume data meet the preset conditions with the data in the quantitative relationship correspondence table, the target value of the number of fragment data collections in the current volume data is determined according to the data in the quantitative relationship correspondence table; when the maximum value of the number of fragment data collections of the volume data and the number of volume data meet the preset conditions with the data in the quantitative relationship correspondence table, the target value of the number of fragment data collections in the current volume data is determined according to the data in the quantitative relationship correspondence table, including: when the number of volume data is equal to the preset number of current volume data in the quantitative relationship correspondence table, and the maximum value of the number of fragment data collections of the volume data is greater than the preset number of fragment data collections corresponding to the second preset number of volume data, the preset number of fragment data collections corresponding to the preset number of current volume data is determined as the target value of the number of fragment data collections in the current volume data; The second determining module is used to determine whether the current volume data has been collected completely based on the target value of the number of times the slice data in the current volume data is collected and the current number of times the slice data in the current volume data is collected.

8. A computer device, comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the image data processing method according to any one of claims 1 to 6.

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