CN116523938A - Method, device, equipment and readable storage medium for processing data after bone segmentation - Google Patents
Method, device, equipment and readable storage medium for processing data after bone segmentation Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 54
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- G06T7/11—Region-based segmentation
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- G06T7/136—Segmentation; Edge detection involving thresholding
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Abstract
The application provides a method, a device, equipment and a computer readable storage medium for processing data after bone segmentation. The data processing method after bone segmentation comprises the following steps: dividing an input skeleton CT image to obtain a division result image; traversing the segmentation result image, and marking each connected domain in the segmentation result image; calculating the area of each marked connected domain; and comparing the area of each connected domain with a preset threshold value, and removing the connected domains with areas smaller than the threshold value to obtain a segmentation result image after impurity filtration. According to the embodiment of the application, impurity filtering treatment can be carried out on the data after skeleton segmentation, so that the accuracy of the data is improved.
Description
Technical Field
The application belongs to the technical field of deep learning intelligent recognition, and particularly relates to a data processing method, device and equipment after skeleton segmentation and a computer readable storage medium.
Background
At present, after bones are segmented, more impurities in the misrecognized areas exist in the data, so that the data is inaccurate.
Therefore, how to perform impurity filtering treatment on the data after bone segmentation, so as to improve the accuracy of the data is a technical problem that needs to be solved by those skilled in the art.
Disclosure of Invention
The embodiment of the application provides a method, a device, equipment and a computer readable storage medium for processing data after skeleton segmentation, which can carry out impurity filtering processing on the data after skeleton segmentation, thereby improving the accuracy of the data.
In a first aspect, an embodiment of the present application provides a method for processing data after bone segmentation, including:
dividing an input skeleton CT image to obtain a division result image;
traversing the segmentation result image, and marking each connected domain in the segmentation result image;
calculating the area of each marked connected domain;
and comparing the area of each connected domain with a preset threshold value, and removing the connected domains with areas smaller than the threshold value to obtain a segmentation result image after impurity filtration.
Optionally, after obtaining the segmentation result image after impurity filtering, the method further includes:
calculating the mass center of each marked connected domain;
clustering the centroid points to determine clustering center points;
calculating the average value of the coordinates of the clustering center points;
each of the connected domains after marking is a left side region if smaller than the average value, and a right side region if larger than the average value.
Optionally, clustering the centroid points includes:
calculating Euclidean distance between data points and clustering center points in the space;
based on Euclidean distance, calculating the error square sum to represent the degree of the clustering result.
Optionally, determining the cluster center includes:
randomly selecting k samples as mean vectors of initial clustering centers;
dividing each sample data set into cluster centers closest to the sample data set;
updating the mean vector of the clustering center according to the clustering center to which each sample belongs;
and repeatedly executing the two steps, and when the set iteration times are reached or the mean value vector of the clustering center is not changed, completing the clustering and outputting the result of the clustering center point.
Optionally, traversing the segmentation result image, marking each connected domain in the segmentation result image, including:
the segmentation result image is traversed from top to bottom and left to right, assigning a digital label to each non-zero pixel.
Optionally, traversing the segmentation result image from top to bottom and from left to right, assigning a digital label to each non-zero pixel, including:
if the pixel points of the upper neighborhood and the left neighborhood of the current pixel point are zero, a new digital label is given to the current pixel point, and meanwhile, the label value is recorded;
the upper neighborhood pixel point of the current pixel point is zero, the left neighborhood pixel point is not zero, and the digital label of the current pixel point is consistent with the digital label of the left pixel point;
the upper neighborhood pixel point of the current pixel point is not zero, and the left neighborhood pixel point is zero, so that the digital label of the current pixel point is consistent with the digital label of the upper neighborhood pixel point;
and if the pixel points of the upper neighborhood and the left neighborhood of the current pixel point are not zero, the digital label of the current pixel point is the minimum value of the digital labels of the pixels of the left neighborhood and the upper neighborhood.
Optionally, after assigning a digital label to each non-zero pixel, the method further comprises:
when the digital labels of all the pixel points in the same connected domain are inconsistent, carrying out a merging and gathering process on the array storing the digital labels so as to enable the digital labels of all the pixel points in the same connected domain to be consistent.
In a second aspect, embodiments of the present application provide a bone segmented data processing apparatus, including:
the segmentation module is used for segmenting the input skeleton CT image to obtain a segmentation result image;
the marking module is used for traversing the segmentation result image and marking each connected domain in the segmentation result image;
the calculating module is used for calculating the area of each marked connected domain;
and the filtering module is used for comparing the area of each connected domain with a preset threshold value, and removing the connected domains with areas smaller than the threshold value to obtain a segmentation result image after impurity filtering.
In a third aspect, an embodiment of the present application provides an electronic device, including: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements the method for processing data after bone segmentation according to the first aspect.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement a method for processing bone segmented data according to the first aspect.
According to the method, the device, the equipment and the computer readable storage medium for processing the data after bone segmentation, impurity filtering processing can be carried out on the data after bone segmentation, and therefore accuracy of the data is improved.
The data processing method after bone segmentation comprises the following steps: dividing an input skeleton CT image to obtain a division result image; traversing the segmentation result image, and marking each connected domain in the segmentation result image; calculating the area of each marked connected domain; and comparing the area of each connected domain with a preset threshold value, and removing the connected domains with areas smaller than the threshold value to obtain a segmentation result image after impurity filtration.
Therefore, the method compares the area of each connected domain with the preset threshold value, removes the connected domains with the areas smaller than the threshold value, obtains the segmentation result image after impurity filtering, and can carry out impurity filtering treatment on the data after skeleton segmentation, thereby improving the accuracy of the data.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described below, it will be obvious that the drawings in the description below are some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for processing data after bone segmentation according to one embodiment of the present application;
FIG. 2 is a schematic diagram of image impurity filtering provided in one embodiment of the present application;
FIG. 3 is a schematic representation of cluster analysis provided in one embodiment of the present application;
FIG. 4 is a flow chart of a method for processing data after bone segmentation according to one embodiment of the present application;
FIG. 5 is a schematic diagram of a bone segmented data processing apparatus according to one embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Features and exemplary embodiments of various aspects of the present application are described in detail below to make the objects, technical solutions and advantages of the present application more apparent, and to further describe the present application in conjunction with the accompanying drawings and the detailed embodiments. It should be understood that the specific embodiments described herein are intended to be illustrative of the application and are not intended to be limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by showing examples of the present application.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
At present, after bones are segmented, more impurities in the misrecognized areas exist in the data, so that the data is inaccurate.
To solve the problems in the prior art, embodiments of the present application provide a method, an apparatus, a device, and a computer readable storage medium for processing data after bone segmentation. The following first describes a method for processing data after bone segmentation according to an embodiment of the present application.
Fig. 1 is a flow chart of a method for processing data after bone segmentation according to an embodiment of the present application. As shown in fig. 1, the method for processing data after bone segmentation includes:
s101, dividing an input skeleton CT image to obtain a division result image;
s102, traversing the segmentation result image, and marking each connected domain in the segmentation result image;
s103, calculating the area of each marked connected domain;
s104, comparing the area of each connected domain with a preset threshold value, and removing the connected domains with areas smaller than the threshold value to obtain a segmentation result image after impurity filtration.
In one embodiment, traversing the segmented result image, marking each connected domain in the segmented result image, comprises:
the segmentation result image is traversed from top to bottom and left to right, assigning a digital label to each non-zero pixel.
In one embodiment, traversing the segmentation result image from top to bottom and left to right, assigning a digital label to each non-zero pixel comprises:
if the pixel points of the upper neighborhood and the left neighborhood of the current pixel point are zero, a new digital label is given to the current pixel point, and meanwhile, the label value is recorded;
the upper neighborhood pixel point of the current pixel point is zero, the left neighborhood pixel point is not zero, and the digital label of the current pixel point is consistent with the digital label of the left pixel point;
the upper neighborhood pixel point of the current pixel point is not zero, and the left neighborhood pixel point is zero, so that the digital label of the current pixel point is consistent with the digital label of the upper neighborhood pixel point;
and if the pixel points of the upper neighborhood and the left neighborhood of the current pixel point are not zero, the digital label of the current pixel point is the minimum value of the digital labels of the pixels of the left neighborhood and the upper neighborhood.
In one embodiment, after assigning a digital label to each non-zero pixel, the method further comprises:
when the digital labels of all the pixel points in the same connected domain are inconsistent, carrying out a merging and gathering process on the array storing the digital labels so as to enable the digital labels of all the pixel points in the same connected domain to be consistent.
Specifically, the connected regions in the image refer to regions composed of pixels having the same pixel values and adjacent positions, and the connected region marking algorithm is a process of marking all the connected regions in the image with different symbols. The method comprises the following steps:
1. first traversing image
From top to bottom, the image is traversed from left to right, assigning a digital label to each non-zero pixel (the digital label for a 0 pixel defaults to a digital 0). The pixel points right above and the pixel points right to the left of the accessed current pixel point from the traversing sequence are given digital labels; when the current pixel point is a non-zero pixel, there are four cases as follows.
1) If the pixel points of the upper neighborhood and the left neighborhood of the current pixel point are zero, a new digital label is given to the current pixel point, and meanwhile, the label value is recorded;
2) The upper neighborhood pixel point of the current pixel point is zero, the left neighborhood pixel point is not zero, and the digital label of the current pixel point is consistent with the digital label of the left pixel point;
3) The upper neighborhood pixel point of the current pixel point is not zero, and the left neighborhood pixel point is zero, so that the digital label of the current pixel point is consistent with the digital label of the upper neighborhood pixel point;
4) If the pixel points of the upper neighborhood and the left neighborhood of the current pixel point are not zero, the digital label of the current pixel point is the minimum value of the digital labels of the pixels of the left neighborhood and the upper neighborhood
2. Traversing the image a second time
After the first traversal is completed, one or more labels may be given to the same connected domain, and the purpose of the second traversal is to combine different digital labels belonging to the same connected domain, and finally, to achieve label consistency of all pixels in the same connected domain. Therefore, before the second traversal starts, a union process needs to be performed on the array of stored digital tags, so that different tags in the same connected domain all point to the same tag.
3. After the image is marked, the area of each region is calculated, so that a plurality of connected regions can be obtained. Setting a threshold value:
4. the area of the mark removed is smaller than the threshold value Thr, and the rest is the impurity removed area.
Fig. 2 is a schematic diagram of filtering image impurities according to an embodiment of the present application, as shown in fig. 2, in the image, there are four partial areas, where two larger areas are femur areas, and the other two smaller areas are areas that are misrecognized as femur, and by removing the smaller area areas in the above manner, the larger area remains.
In one embodiment, after obtaining the segmented result image after impurity filtering, the method further comprises:
calculating the mass center of each marked connected domain;
clustering the centroid points to determine clustering center points;
calculating the average value of the coordinates of the clustering center points;
each of the connected domains after marking is a left side region if smaller than the average value, and a right side region if larger than the average value.
Specifically, the left and right joint positions are distinguished based on a clustering method, and the method is as follows:
(1) Marking a communication region in the image by a communication region mode;
(2) Calculating the mass center of each connected region;
(3) Clustering centroid points by a clustering method;
(4) Calculating an average avg of the coordinates of the central points after clustering;
(5) The communication area of the mark is smaller than avg, namely the left area, and the communication area is larger than avg, namely the right area.
FIG. 3 is a schematic view of cluster analysis provided in an embodiment of the present application, where the patella region is divided into two regions on the right side, the connected regions are marked by the connected regions, and the centroid of each connected region is calculated, so as to obtain three centroids, such as gray points in FIG. 3; through cluster analysis, two points on the right side are clustered into one type because of the close distance, the cluster center is "+" in the graph, the average value avg of coordinates of the two cluster center points is calculated, the marked connected area is smaller than avg and is a left area, and the connected area is larger than avg and is a right area.
In one embodiment, clustering centroid points includes:
calculating Euclidean distance between data points and clustering center points in the space;
based on Euclidean distance, calculating the error square sum to represent the degree of the clustering result.
Specifically, the clustering method is as follows:
the Euclidean distance calculation formula between the data point position and the clustering center position in the space is as follows:
wherein X is the data point location; ci is the ith cluster center; m is the dimension of the data object; xj, cij is the j-th attribute value of X and Ci.
The square error sum SSE calculation formula for the whole dataset is:
wherein, the size of SSE represents the quality of the clustering result; k is the number of clusters.
In one embodiment, determining a cluster center point includes:
randomly selecting k samples as mean vectors of initial clustering centers;
dividing each sample data set into cluster centers closest to the sample data set;
updating the mean vector of the clustering center according to the clustering center to which each sample belongs;
and repeatedly executing the two steps, and when the set iteration times are reached or the mean value vector of the clustering center is not changed, completing the clustering and outputting the result of the clustering center point.
Fig. 4 is a flowchart of a method for processing data after bone segmentation according to an embodiment of the present application, as shown in fig. 4, after CT data is input, bone segmentation is performed by using an Nnunet network, and bone segmentation results include segmentation results of four parts of femur, tibia, patella and fibula. And after impurity filtering is carried out on the segmentation result, the positions of the left joint and the right joint are distinguished through a clustering algorithm.
Fig. 5 is a schematic structural diagram of a bone-segmented data processing apparatus according to an embodiment of the present application, where the bone-segmented data processing apparatus includes:
the segmentation module 501 is used for segmenting the input bone CT image to obtain a segmentation result image;
the marking module 502 is configured to traverse the segmentation result image and mark each connected domain in the segmentation result image;
a calculating module 503, configured to calculate an area of each of the labeled connected domains;
and the filtering module 504 is configured to compare the area of each connected domain with a preset threshold value, and remove connected domains with areas smaller than the threshold value, so as to obtain a segmentation result image after impurity filtering.
Fig. 6 shows a schematic structural diagram of an electronic device according to an embodiment of the present application.
The electronic device may include a processor 601 and a memory 602 storing computer program instructions.
In particular, the processor 601 may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured to implement one or more integrated circuits of embodiments of the present application.
Memory 602 may include mass storage for data or instructions. By way of example, and not limitation, memory 602 may include a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, magnetic tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of the above. The memory 602 may include removable or non-removable (or fixed) media, where appropriate. The memory 602 may be internal or external to the electronic device, where appropriate. In particular embodiments, memory 602 may be a non-volatile solid state memory.
In one embodiment, memory 602 may be Read Only Memory (ROM). In one embodiment, the ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically Erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.
The processor 601 reads and executes the computer program instructions stored in the memory 602 to implement any of the bone segmented data processing methods of the above embodiments.
In one example, the electronic device may also include a communication interface 603 and a bus 610. As shown in fig. 6, the processor 601, the memory 602, and the communication interface 603 are connected to each other through a bus 610 and perform communication with each other.
The communication interface 603 is mainly configured to implement communication between each module, apparatus, unit and/or device in the embodiments of the present application.
Bus 610 includes hardware, software, or both, that couple components of the electronic device to one another. By way of example, and not limitation, the buses may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a micro channel architecture (MCa) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus, or a combination of two or more of the above. Bus 610 may include one or more buses, where appropriate. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.
In addition, in combination with the method for processing data after bone segmentation in the above embodiment, the embodiment of the application may be implemented by providing a computer readable storage medium. The computer readable storage medium has stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement a bone segmented data processing method of any of the above embodiments.
It should be clear that the present application is not limited to the particular arrangements and processes described above and illustrated in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions, or change the order between steps, after appreciating the spirit of the present application.
The functional blocks shown in the above-described structural block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be different from the order in the embodiments, or several steps may be performed simultaneously.
Aspects of the present application are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In the foregoing, only the specific embodiments of the present application are described, and it will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein. It should be understood that the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, which are intended to be included in the scope of the present application.
Claims (10)
1. A method of processing bone segmented data, comprising:
dividing an input skeleton CT image to obtain a division result image;
traversing the segmentation result image, and marking each connected domain in the segmentation result image;
calculating the area of each marked connected domain;
and comparing the area of each connected domain with a preset threshold value, and removing the connected domains with areas smaller than the threshold value to obtain a segmentation result image after impurity filtration.
2. The method of claim 1, wherein after obtaining the segmented result image after filtering the impurities, the method further comprises:
calculating the mass center of each marked connected domain;
clustering the centroid points to determine clustering center points;
calculating the average value of the coordinates of the clustering center points;
each of the connected domains after marking is a left side region if smaller than the average value, and a right side region if larger than the average value.
3. The method of bone segmented data processing according to claim 2, wherein clustering the centroid points comprises:
calculating Euclidean distance between data points and clustering center points in the space;
based on Euclidean distance, calculating the error square sum to represent the degree of the clustering result.
4. A method of bone segmented data processing according to claim 3, wherein determining cluster center points comprises:
randomly selecting k samples as mean vectors of initial clustering centers;
dividing each sample data set into cluster centers closest to the sample data set;
updating the mean vector of the clustering center according to the clustering center to which each sample belongs;
and repeatedly executing the two steps, and when the set iteration times are reached or the mean value vector of the clustering center is not changed, completing the clustering and outputting the result of the clustering center point.
5. The method of claim 1, wherein traversing the segmented result image marks each connected domain in the segmented result image, comprising:
the segmentation result image is traversed from top to bottom and left to right, assigning a digital label to each non-zero pixel.
6. The method of claim 5, wherein traversing the segmented result image from top to bottom and left to right assigns a digital label to each non-zero pixel, comprising:
if the pixel points of the upper neighborhood and the left neighborhood of the current pixel point are zero, a new digital label is given to the current pixel point, and meanwhile, the label value is recorded;
the upper neighborhood pixel point of the current pixel point is zero, the left neighborhood pixel point is not zero, and the digital label of the current pixel point is consistent with the digital label of the left pixel point;
the upper neighborhood pixel point of the current pixel point is not zero, and the left neighborhood pixel point is zero, so that the digital label of the current pixel point is consistent with the digital label of the upper neighborhood pixel point;
and if the pixel points of the upper neighborhood and the left neighborhood of the current pixel point are not zero, the digital label of the current pixel point is the minimum value of the digital labels of the pixels of the left neighborhood and the upper neighborhood.
7. The method of claim 5, further comprising, after assigning a digital label to each non-zero pixel:
when the digital labels of all the pixel points in the same connected domain are inconsistent, carrying out a merging and gathering process on the array storing the digital labels so as to enable the digital labels of all the pixel points in the same connected domain to be consistent.
8. A bone segmented data processing apparatus comprising:
the segmentation module is used for segmenting the input skeleton CT image to obtain a segmentation result image;
the marking module is used for traversing the segmentation result image and marking each connected domain in the segmentation result image;
the calculating module is used for calculating the area of each marked connected domain;
and the filtering module is used for comparing the area of each connected domain with a preset threshold value, and removing the connected domains with areas smaller than the threshold value to obtain a segmentation result image after impurity filtering.
9. An electronic device, the electronic device comprising: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements a bone segmented data processing method as claimed in any one of claims 1-7.
10. A computer-readable storage medium, wherein computer program instructions are stored on the computer-readable storage medium, which when executed by a processor, implement the bone-segmented data processing method of any one of claims 1-7.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202310551704.7A CN116523938A (en) | 2023-05-16 | 2023-05-16 | Method, device, equipment and readable storage medium for processing data after bone segmentation |
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