CN114782414A - Artificial bone data analysis method based on image data processing - Google Patents

Artificial bone data analysis method based on image data processing Download PDF

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CN114782414A
CN114782414A CN202210675640.7A CN202210675640A CN114782414A CN 114782414 A CN114782414 A CN 114782414A CN 202210675640 A CN202210675640 A CN 202210675640A CN 114782414 A CN114782414 A CN 114782414A
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韩苗苗
邓荣霞
张桂东
张建光
罗鹏
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Shenzhen Meijie Life Science Co ltd
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Abstract

The invention discloses an artificial bone data analysis method based on image data processing, which relates to the technical field of image processing and solves the technical problem of artificial bone data analysis, and the method comprises the following steps: step 1, acquiring data information of an artificial bone CT image; obtaining the image data information of the cross section of the artificial bone by a CT image scanning method; outputting the section image information of the artificial bone sagittal position or coronal position; step 2, processing the data information of the artificial bone CT image; carrying out noise reduction and low-frequency component conversion on the acquired data information of the artificial bone CT image through a layered image fusion algorithm so as to realize fusion of data information of different positions; step 3, analyzing the data information of the artificial bone CT image; realizing artificial data information analysis in an image recombination mode; and 4, calculating and outputting data information of the artificial bone CT image. The image processing capability is improved through the layered image fusion algorithm, and the artificial bone data analysis capability is improved.

Description

Artificial bone data analysis method based on image data processing
Technical Field
The invention relates to the technical field of image data processing, in particular to an artificial bone data analysis method based on image data processing.
Background
In China, more than 600 million patients with bone defects or dysfunction caused by wound fracture, spinal degenerative diseases, bone tumors, bone tuberculosis and other orthopedic diseases caused by traffic accidents and production safety accidents are treated every year. Bone grafting is the primary method of treatment for bone defects, which means the structural integrity of the bone is compromised, and is now the most widely used tissue graft in clinical settings, in addition to blood transfusion. A series of etiologies such as tumor, trauma, necrosis and congenital deformity often cause massive bone defects.
The manufacturing and processing of human body defected bones are a hot problem in medical rehabilitation engineering, and the quality of the prepared artificial bones directly influences the quality of medical treatment. With the increase of bone defect cases, higher requirements are put forward on bone repair materials, such as good biocompatibility, no immunogenicity and no toxic or side effect; the biodegradable plastic has good biodegradability; has good bone conduction capability; has good bone induction capability; has certain mechanical strength; has good plasticity. In order to meet individual requirements of different patients, it is necessary to analyze data information of the artificial bone, for example, using adult femur as a research object, on the basis of a helical CT tomographic image of an individual patient, applying a theory of computer aided geometric design, combining computer graphics and computer image processing technology, and applying computer aided software such as medical image software, reverse engineering software and solid modeling to realize shape modeling and diagnosis of the defective artificial bone. The technical problem to be solved urgently is how to realize the analysis of the artificial bone data to judge the quality of the artificial bone in the process of diagnosing the artificial bone in the prior art.
Disclosure of Invention
Aiming at the technical defects, the invention discloses an artificial bone data analysis method based on image data processing, which can realize artificial bone data analysis by an image analysis method.
In order to realize the technical effects, the invention adopts the following technical scheme:
an artificial bone data analysis method based on image data processing comprises the following steps:
step 1, acquiring data information of an artificial bone CT image;
obtaining the image data information of the cross section of the artificial bone by a CT image scanning method; outputting the section image information of the artificial bone sagittal position or coronal position;
step 2, processing the data information of the artificial bone CT image;
denoising and converting low-frequency components of the acquired data information of the artificial bone CT image through a layered image fusion algorithm so as to realize the fusion of data information of different parts;
when the fusion of data information of different parts is realized, firstly, the artificial bone CT image is split into a plurality of small image segments, which are recorded as:
Figure 353863DEST_PATH_IMAGE001
（1）
in the formula (1), the first and second groups,P I a source image collection representing CT image information,prepresenting the split-up image segments after the split-up,nrepresenting the number of segments;
classifying smooth and non-smooth segments based on a smooth threshold, wherein random segments and main direction segments both belong to non-smooth segments, and calculating the gradient of each artificial bone CT image pixel during classificationk ij （j=1，2，…，w），iA source image number is represented,i=（1，2，…，n），wis a gradientk ij The weight of (a) is calculated,k ij byxAndygradient of coordinatesg ij （x) Andg ij （y) Composition, image vectorv i Each pixel ink ij The gradient values of (A) are:
Figure 802162DEST_PATH_IMAGE002
（2）
then, decomposing the gradient value of each image segment, wherein the gradient value has the formula:
Figure 988424DEST_PATH_IMAGE003
（3）
in the formula (3), the first and second groups of the compound,G i the gradient of the image segment is represented by,
Figure 586895DEST_PATH_IMAGE004
the abscissa and ordinate of the gradient of the image segment are indicated,U i S i V i to representG i The resolution of the gradient values of (a) is performed,U i represents the transverse coordinate data information of the main direction of the artificial bone CT image,V i representing the longitudinal coordinate data information of the main direction vector of the artificial bone CT image;S i diagonal 2 x 2 matrix representing principal direction vectors；When obtainingS i Then, a dominant direction measure may be calculatedR，RThe calculation method of (2) is shown in formula (4):
Figure 666847DEST_PATH_IMAGE005
（4）
in the formula (4), the first and second groups of the chemical reaction are shown in the specification,Rthe smaller the artificial bone CT image vector, the more random it is, in this case, the threshold is calculatedRThe method comprises the steps of distinguishing random and main direction segments of an artificial bone CT image, using the segments as a low-pass filter, and filtering the information of the artificial bone CT image so as to improve the information quality of the artificial bone CT image;
and then, realizing smooth classification of data information by adopting a two-dimensional Gaussian fuzzy function, wherein the classification formula is as follows:
Figure 895834DEST_PATH_IMAGE006
（5）
in the formula (5), the first and second groups,xis the distance on the horizontal axis from the origin,yis the distance on the vertical axis from the origin, σ is the standard deviation of the Gaussian distribution, σ is a variable number between 0-3;
step 3, analyzing the data information of the artificial bone CT image; realizing manual data information analysis in an image recombination mode;
and 4, calculating and outputting data information of the artificial bone CT image.
As a further technical scheme of the invention, the layered image fusion algorithm is also provided with image preprocessing, and the image preprocessing method comprises the following steps:
step (21), CT image information format conversion is carried out, and the DICOM image is converted into a BMP bitmap file or digital information;
step (22), CT image information preprocessing, wherein different noises are introduced in the formation of a CT image, the image is subjected to smoothing and noise removal preprocessing, and the image is subjected to smoothing processing by adopting median filtering, least mean square filtering or smoothing filtering;
step (23), CT image information segmentation is carried out, bone tissue regions are separated, a threshold value is automatically selected through image binarization, the image binarization is carried out by an Otsu method, and a gray value with the minimum variance and the maximum inter-class variance in the CT image information is used as an optimal threshold value;
step (24), contour extraction; the CT image information contour data is obtained through edge detection, the edge detection is to process the edge of the image to obtain the edge information of closed smoothness, the edge detection is carried out by utilizing an edge detection operator, the image vector contour data is the vectorization of a dot matrix pattern, the search is carried out along the boundary of the image, and the point coordinates on the searched contour line are recorded in a point column for storage.
As a further technical scheme of the invention, the image data information of the artificial bone CT image data is adjusted according to the size, and the size is 1.0mm multiplied by 1.0mm, 0.5mm multiplied by 0.5mm or 0.25mm multiplied by 0.25 mm; the image data information of the artificial bone CT image data is adjusted according to the resolution, and the size is 1.0mm multiplied by 1.0mm, 0.5mm multiplied by 0.5mm or 0.25mm multiplied by 0.25 mm.
As a further technical scheme of the invention, the artificial bone CT image data information processing also comprises a three-dimensional image recombination step, which comprises the following steps:
1) curve reconstruction, namely performing smoothing on a contour curve obtained by contour extraction, and performing curve fitting by using an interpolation method or an approximation method to finish curve smoothing;
(2) generating a curved surface, and realizing the reconstruction of the artificial bone CT image data information curved surface by using a Prim algorithm model, wherein the code is realized as follows:
Figure 366129DEST_PATH_IMAGE007
the construction of weighted undirected graph by the vertex and adjacency relation of the data information of the artificial bone CT image is completed according to the model modeling
Figure 96188DEST_PATH_IMAGE008
Wherein the artificial bone CT image data information topology structure vertex
Figure 171591DEST_PATH_IMAGE009
，
Figure 430534DEST_PATH_IMAGE010
Representing the set of all vertexes of the curved surface model of the artificial bone CT image;
Figure 591389DEST_PATH_IMAGE011
representing the data values after being continuously updated,
Figure 859559DEST_PATH_IMAGE012
the connection edge value after the data is continuously updated,
Figure 586206DEST_PATH_IMAGE013
representation model
Figure 156996DEST_PATH_IMAGE014
And model
Figure 929780DEST_PATH_IMAGE015
A connecting edge therebetween;
Figure 611428DEST_PATH_IMAGE016
representation model
Figure 458162DEST_PATH_IMAGE017
And model
Figure 324486DEST_PATH_IMAGE018
By the distance between the geodesic lines
Figure 459933DEST_PATH_IMAGE019
Calculating the minimum path of the model vertex to realize the curved surface reconstruction of the data information of the artificial bone CT image;
(3) the method comprises the following steps of surface materialization, wherein before materialization, the closure, continuity and geometric errors of the surface are detected, and after the surface is detected and modified, the materialization construction is carried out through a 3Dmine module, a three-dimensional GIS module and a BIMPro/E module.
As a further technical scheme of the invention, the artificial bone CT image data information analysis method is an EMD algorithm model.
As a further technical scheme of the invention, the working method of the EMD algorithm model comprises the following steps:
the method comprises the following steps of converting an information equation quantity of the CT image data of the artificial bone into a signal formula, wherein the signal formula is as follows:
Figure 69906DEST_PATH_IMAGE020
(6)
in formula (6)
Figure 708828DEST_PATH_IMAGE021
Representing the artificial bone CT image data information fault signal function,
Figure 683738DEST_PATH_IMAGE022
representing a summary of simulated failure artificial bone input data,
Figure 165535DEST_PATH_IMAGE023
representing normal artificial bone data;
EMD solution is carried out on the signal function of the formula (6), the maximum fault bearing fixed material and the minimum bearing material load of the artificial bone are calculated by fitting the state data information of each simulated artificial bone, the maximum allowable data error of the artificial bone is reflected according to the average value of the maximum fault bearing fixed material and the minimum bearing material load, and the error function is recorded as:
Figure 454565DEST_PATH_IMAGE024
(7)
in the formula (7), the first and second groups,
Figure 338207DEST_PATH_IMAGE025
represents the maximum fault allowance of the polymethyl methacrylate or high-density polyethylene of the test artificial bone,
Figure 421701DEST_PATH_IMAGE026
representing the data of the rated bearing material failure of the tested artificial bone,
Figure 390794DEST_PATH_IMAGE027
the minimum material load borne by the tested artificial bone is shown;
the formula (1) is combined with the formula (2) to convert the maximum fault information quantity of the tested artificial bone into a signal function, namely:
Figure 217935DEST_PATH_IMAGE028
(8)
in the formula (8), the first and second groups,
Figure 221663DEST_PATH_IMAGE029
representing a function of the maximum fault information quantity of the tested artificial bone;
the first order input signal converted into recognizable by algorithmic programming is represented as:
Figure 538375DEST_PATH_IMAGE030
(9)
in the formula (9), the reaction mixture is,
Figure 604551DEST_PATH_IMAGE031
a first order signal recognizable to the programming of the algorithm,
Figure 32122DEST_PATH_IMAGE032
representing analog input data that satisfies the EMD algorithm conditions,
Figure 624777DEST_PATH_IMAGE033
representing the simulated artificial bone fault data component.
The invention has the beneficial and positive effects that:
different from the conventional technology, the invention discloses an artificial bone data analysis method based on image data processing, which comprises the following steps: step 1, acquiring data information of an artificial bone CT image; obtaining image data information of the cross section of the artificial bone by a CT image scanning method; outputting the section image information of the artificial bone sagittal position or coronal position; step 2, processing the data information of the artificial bone CT image; denoising and converting low-frequency components of the acquired data information of the artificial bone CT image through a layered image fusion algorithm so as to realize the fusion of data information of different parts; step 3, analyzing the data information of the artificial bone CT image; realizing manual data information analysis in an image recombination mode; and 4, calculating and outputting the data information of the artificial bone CT image. The image processing capability is improved through the layered image fusion algorithm, and the artificial bone data analysis capability is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without inventive labor, wherein:
FIG. 1 is a schematic flow diagram of the overall process of the present invention;
FIG. 2 is a schematic diagram of a hierarchical image fusion algorithm architecture according to the present invention;
FIG. 3 is a schematic flow chart of a layered image fusion algorithm according to the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, and it should be understood that the embodiments described herein are merely for the purpose of illustrating and explaining the present invention and are not intended to limit the present invention.
As shown in fig. 1, an artificial bone data analysis method based on image data processing includes:
step 1, acquiring data information of an artificial bone CT image;
obtaining image data information of the cross section of the artificial bone by a CT image scanning method; outputting the section image information of the artificial bone sagittal position or coronal position;
step 2, processing the data information of the CT image of the artificial bone;
denoising and converting low-frequency components of the acquired data information of the artificial bone CT image through a layered image fusion algorithm so as to realize the fusion of data information of different parts;
in a specific embodiment, the layered image fusion algorithm divides the artificial bone image into three gaussian image segment types including a smooth segment, a random segment and a principal direction segment by a gaussian distribution feature extraction technology, and in order to reduce the influence of image noise, the segments are divided into high-frequency noise component types and low-frequency component types according to different bottom layer rules:
when fusion of data information of different parts is realized, firstly, an artificial bone CT image is split into a plurality of small image segments, and the small image segments are recorded as follows:
Figure 315652DEST_PATH_IMAGE034
（1）
in the formula (1), the first and second groups of the compound,P I a source image collection representing CT image information,pshows the split diagramLike the segments of the image, the image is,nrepresents the number of segments;
classifying smooth and non-smooth segments based on a smooth threshold, wherein random segments and main direction segments both belong to non-smooth segments, and calculating the gradient of each artificial bone CT image pixel during classificationk ij （j=1，2，…，w），iA source image number is represented,i=（1，2，…，n），wis a gradientk ij The weight of (a) is calculated,k ij byxAndygradient of coordinatesg ij （x) Andg ij （y) Composition, image vectorv i Each pixel ink ij The gradient values of (A) are:
Figure 259338DEST_PATH_IMAGE035
（2）
then, decomposing the gradient value of each image segment, wherein the gradient value has the formula:
Figure 428282DEST_PATH_IMAGE036
（3）
in the formula (3), the first and second groups,G i the gradient of the image segment is represented by,
Figure 406602DEST_PATH_IMAGE037
the abscissa and ordinate of the gradient of the image segment are indicated,U i S i V i to representG i The resolution of the gradient values of (a) is performed,U i represents the transverse coordinate data information of the main direction of the CT image of the artificial bone,V i representing the longitudinal coordinate data information of the main direction vector of the artificial bone CT image;S i diagonal 2 x 2 matrix representing principal direction vectors；When obtainingS i Then, a dominant direction measure may be calculatedR，RThe calculation method of (2) is shown in formula (4):
Figure 737221DEST_PATH_IMAGE038
（4）
in the formula (4), the first and second groups of the chemical reaction are shown in the specification,Rthe smaller the artificial bone CT image vector, the more random it is, in this case, the threshold is calculatedRThe method comprises the steps of distinguishing random and main direction segments of an artificial bone CT image, using the segments as a low-pass filter, and filtering the information of the artificial bone CT image so as to improve the information quality of the artificial bone CT image;
and then, implementing smooth classification of data information by adopting a two-dimensional Gaussian fuzzy function, wherein the classification formula is as follows:
Figure 433781DEST_PATH_IMAGE039
（5）
in the formula (5), the first and second groups,xis the distance on the horizontal axis from the origin,yis the distance from the origin on the vertical axis, σ is the standard deviation of the Gaussian distribution, σ is a variable number between 0 and 3;
step 3, analyzing the data information of the artificial bone CT image; realizing manual data information analysis in an image recombination mode;
and 4, calculating and outputting the data information of the artificial bone CT image.
In the above embodiment, the layered image fusion algorithm further includes image preprocessing, and the image preprocessing method includes:
step (21), converting the format of CT image information, and converting the DICOM image into a BMP bitmap file or digital information;
step (22), CT image information preprocessing, wherein different noises are introduced in the formation of a CT image, the image is subjected to smoothing and noise removal preprocessing, and the image is subjected to smoothing processing by adopting median filtering, least mean square filtering or smoothing filtering;
step (23), segmenting CT image information, separating a bone tissue region, automatically selecting a threshold value through image binarization, carrying out image binarization to an Otsu method, and taking a gray value with the minimum variance and the maximum inter-class variance in the CT image information as an optimal threshold value;
step (24), contour extraction; the CT image information contour data is obtained through edge detection, the edge detection is to process the edge of the image to obtain the edge information of closed smoothness, the edge detection is carried out by utilizing an edge detection operator, the image vector contour data is the vectorization of a dot matrix pattern, the search is carried out along the boundary of the image, and the point coordinates on the searched contour line are recorded in a point column for storage.
In a particular embodiment, the reconstruction is performed by means of a helical scan. The transverse cross-sectional plane image can be subjected to multi-dimensional and multi-plane recombination (reconstruction), the image can be observed from any angle in all directions, and the positioning, quantification and qualitative determination of lesions are more accurate. In the recombined image, tissues with different densities can be displayed in different pseudo colors, and the image is more vivid. The CT image is obtained by converting the CT value of each pixel in the reconstructed matrix into corresponding signals with different brightness levels through digital-to-analog (D/a) conversion, and displaying the signals on a monitor. The level of the luminance signal represented by the monitor is called gray scale (gray scale). The gray levels are 16, but each gray level has 4 continuous gray levels, so that 64 continuous transition levels with different gray levels are provided. The structure of the 2000Hu range is shown in 64 gray levels, each level representing 31 consecutive CT values: and each of the 16 gray levels shown contains 125 Hu. That is, the density difference of the object will be represented as the same gray level within 125 Hu. Since the density difference between many tissues and organs of the human body and their lesions (in particular, soft tissues and parenchymal organs) is largely in the range of 1O00Hu, they cannot be distinguished from each other. When the gray scale range of image display is enlarged to 2000Hu, the above soft tissues are all expressed as consistent gray scale, and the due density contrast is lost, which is not favorable for diagnosis and differential diagnosis.
In the above embodiment, the artificial bone CT image data is resized to have a size of 1.0mm × 1.0mm, 0.5mm × 0.5mm, or 0.25mm × 0.25mm by adjusting image data information; the image data information of the artificial bone CT image data is adjusted according to the resolution, and the size is 1.0mm multiplied by 1.0mm, 0.5mm multiplied by 0.5mm or 0.25mm multiplied by 0.25 mm.
The CT image has higher density resolution, the measurement accuracy of the X-ray absorption coefficient can reach 0.5 percent, and tissues with smaller density difference can be distinguished. The cross-sectional image obtained by CT has accurate layer thickness, clear image, high density resolution and no structural interference outside the layer. In addition, CT can be processed, recombined and reconstructed by computer software to obtain cross-sectional images of multiple planes such as sagittal and coronal planes required by diagnosis. CT has a higher density resolution, and CT examination has a higher density resolution than conventional imaging except for nuclear magnetic resonance. Quantitative analysis can be carried out according to the X-ray attenuation coefficient of the tissue, and specific CT values can be measured for quantitative analysis. And obtaining a rapid prototyping data interface (CLI) file by using image processing methods such as CT image filtering, segmentation, opening operation and the like and modeling methods such as contour extraction and sampling. In the processing process, the smoothing processing can be firstly carried out on the CT slice image to eliminate noise, and then the binarization processing is carried out; and then carrying out edge detection, and carrying out contour tracing processing to obtain a closed contour curve represented by a single pixel chain.
The artificial bone CT image data information processing also comprises a three-dimensional image recombination step, which comprises the following steps:
the three-dimensional reconstruction of the tomographic image is to restore the original three-dimensional appearance of a reconstructed object from a series of parallel sectional images, and the method mainly comprises the following steps: firstly, the outline curves of the interested region are segmented from each sectional image, and then the original three-dimensional appearance is constructed by the outline curves through an algorithm.
(1) Curve reconstruction, namely performing smoothing on a contour curve obtained by contour extraction, and performing curve fitting by using an interpolation method or an approximation method to finish curve smoothing; the control point and the starting point of each contour curve must be the same, so that the quality of the curved surface can be ensured.
(2) Generating a curved surface, and realizing the reconstruction of the artificial bone CT image data information curved surface by using a Prim algorithm model, wherein the code is realized as follows:
Figure 129118DEST_PATH_IMAGE040
the construction of weighted undirected graph by the vertex and adjacency relation of the data information of the artificial bone CT image is completed according to the model modeling
Figure 634049DEST_PATH_IMAGE041
Wherein the artificial bone CT image data information topology framework vertex
Figure 525782DEST_PATH_IMAGE042
，
Figure 319425DEST_PATH_IMAGE043
Representing the set of all vertexes of the curved surface model of the artificial bone CT image;
Figure 954806DEST_PATH_IMAGE044
representing the data values after being continuously updated,
Figure 251926DEST_PATH_IMAGE045
the connection edge value after the data is continuously updated,
Figure 580140DEST_PATH_IMAGE046
representation model
Figure 595500DEST_PATH_IMAGE047
And a model
Figure 300151DEST_PATH_IMAGE048
A connecting edge therebetween;
Figure 514095DEST_PATH_IMAGE049
representation model
Figure 622996DEST_PATH_IMAGE050
And a model
Figure 250286DEST_PATH_IMAGE051
By the distance between the geodetic lines
Figure 368415DEST_PATH_IMAGE053
Calculating the minimum path of the model vertex to realize the curved surface reconstruction of the data information of the artificial bone CT image;
(3) and (3) materializing the curved surface, wherein before materializing, the sealing property, continuity and geometric errors of the curved surface are detected, and after the curved surface is detected and modified, the materializing construction is carried out through a 3Dmine module, a three-dimensional GIS module and a BIMPro/E module.
The artificial bone substitute is made of artificial material or is a fixing material for fracture. The technical problem to be solved by the present application is how to analyze CT image data information of an artificial bone, wherein the material of the artificial bone is mainly composed of a polymer synthetic material, such as polymethyl methacrylate, or high-density polyethylene, and some inorganic materials, such as calcium phosphate, alumina bioceramic, etc.
The method for analyzing the data information of the artificial bone CT image is an EMD algorithm model.
The working method of the EMD algorithm model comprises the following steps:
the method comprises the following steps of converting an information equation quantity of the CT image data of the artificial bone into a signal formula, wherein the signal formula is as follows:
Figure 764761DEST_PATH_IMAGE055
(6)
in the formula (6)
Figure 106881DEST_PATH_IMAGE056
Representing the artificial bone CT image data information fault signal function,
Figure 96834DEST_PATH_IMAGE057
a summary of artificial bone input data representing simulated faults,
Figure 877708DEST_PATH_IMAGE058
representing normal artificial bone data;
EMD solution is carried out on a signal function of a formula (6), the maximum fault bearing fixed material and the minimum bearing material load of the artificial bone are calculated by fitting the state data information of each simulated artificial bone, the maximum allowable data error of the artificial bone is reflected according to the average value of the maximum fault bearing fixed material and the minimum bearing material load, and an error function is recorded as:
Figure 269506DEST_PATH_IMAGE060
(7)
in the formula (7), the first and second groups,
Figure 110423DEST_PATH_IMAGE061
represents the maximum fault allowance of the polymethyl methacrylate or high-density polyethylene of the test artificial bone,
Figure 322093DEST_PATH_IMAGE062
representing the data of the rated bearing material failure of the tested artificial bone,
Figure 578762DEST_PATH_IMAGE063
the minimum material load borne by the tested artificial bone is shown;
the formula (1) is combined with the formula (2) to convert the maximum fault information quantity of the tested artificial bone into a signal function, namely:
Figure 949700DEST_PATH_IMAGE065
(8)
in the formula (8), the first and second groups of the chemical reaction are shown in the specification,
Figure 836885DEST_PATH_IMAGE066
representing a function of testing the maximum fault information quantity of the artificial bone;
the first order input signal converted into recognizable by algorithmic programming is represented as:
Figure 660484DEST_PATH_IMAGE068
(9)
in the formula (9), the reaction mixture is,
Figure DEST_PATH_IMAGE069
a first order signal that represents an algorithm's programming recognition,
Figure 455265DEST_PATH_IMAGE070
representing simulated input data that satisfies the EMD algorithm conditions,
Figure DEST_PATH_IMAGE071
representing the simulated artificial bone fault data component.
The Empirical Mode Decomposition (EMD) algorithm is a method of decomposing a signal into characteristic modes. This has the advantage that no well-defined function is applied as a basis, but that the natural mode functions are generated adaptively from the analyzed signal. The method can be used for analyzing nonlinear and non-stationary signal sequences, and has high signal-to-noise ratio and good time-frequency focusing property.
There are several assumptions to be made about EMD decomposition:
1) the signal has at least two extreme points, one maximum and one minimum; 2) the time scale characteristic is determined by the time scale between two extreme points.
The purpose of EMD decomposition is to decompose a signal f (t) into N Intrinsic Mode Functions (IMFs) and a residual. Wherein each IMF needs to satisfy the following two conditions:
1) in the whole data range, the number of local extreme points and zero-crossing points must be equal, or the number of phase differences is at most 1; 2) At any time, the average of the envelope of the local maximum (upper envelope) and the envelope of the local minimum (lower envelope) must be zero.
Although specific embodiments of the present invention have been described above, it will be understood by those skilled in the art that these specific embodiments are merely illustrative and that various omissions, substitutions and changes in the form of the detail of the methods and systems described above may be made by those skilled in the art without departing from the spirit and scope of the invention. For example, it is within the scope of the present invention to combine the steps of the methods described above to perform substantially the same function in substantially the same way to achieve substantially the same result. Accordingly, the scope of the invention is to be limited only by the following claims.

Claims (6)

1. An artificial bone data analysis method based on image data processing is characterized in that: the method comprises the following steps:
step 1, acquiring data information of an artificial bone CT image;
obtaining image data information of the cross section of the artificial bone by a CT image scanning method; outputting the section image information of the artificial bone sagittal position or coronal position;
step 2, processing the data information of the artificial bone CT image;
denoising and converting low-frequency components of the acquired data information of the artificial bone CT image through a layered image fusion algorithm so as to realize the fusion of data information of different parts;
when fusion of data information of different parts is realized, firstly, an artificial bone CT image is split into a plurality of small image segments, and the small image segments are recorded as follows:
Figure 211819DEST_PATH_IMAGE001
（1）
in the formula (1), the first and second groups of the compound,P I represents a source image set of CT image information,prepresenting the split-up image segments after the split-up,nrepresents the number of segments;
classifying smooth and non-smooth segments based on a smooth threshold, wherein random segments and main direction segments both belong to non-smooth segments, and calculating the gradient of each artificial bone CT image pixel during classificationk ij （j=1，2，…，w），iA source image number is represented,i=（1，2，…，n），wis a gradientk ij The weight of (a) is calculated,k ij byxAndygradient of coordinatesg ij （x) Andg ij （y) Composition, image vectorv i Each pixel ink ij The gradient values of (A) are:
Figure 410850DEST_PATH_IMAGE002
（2）
then, decomposing the gradient value of each image segment, wherein the gradient value has the formula:
Figure 987325DEST_PATH_IMAGE003
（3）
in the formula (3), the first and second groups of the compound,G i a gradient of the image segment is represented,
Figure 179272DEST_PATH_IMAGE004
the abscissa and ordinate representing the gradient of the image segment,U i S i V i to representG i The resolution of the gradient values of (a) is performed,U i represents the transverse coordinate data information of the main direction of the CT image of the artificial bone,V i representing the longitudinal coordinate data information of the main direction vector of the artificial bone CT image;S i diagonal 2 x 2 matrix representing principal direction vectors；When obtainingS i Then, a principal direction measure can be calculatedR，RThe calculation method of (2) is shown in formula (4):
Figure 275535DEST_PATH_IMAGE005
（4）
in the formula (4), the first and second groups,Rthe smaller the artificial bone CT image vector, the more random it is, in this case, the threshold is calculatedRThe method comprises the steps of distinguishing random and main direction segments of an artificial bone CT image, using the segments as a low-pass filter, and filtering the information of the artificial bone CT image so as to improve the information quality of the artificial bone CT image;
and then, implementing smooth classification of data information by adopting a two-dimensional Gaussian fuzzy function, wherein the classification formula is as follows:
Figure 894735DEST_PATH_IMAGE006
（5）
in the formula (5), the first and second groups,xis the distance on the horizontal axis from the origin,yis the distance on the vertical axis from the origin, σ is the standard deviation of the Gaussian distribution, σ is a variable number between 0-3;
step 3, analyzing the data information of the artificial bone CT image; realizing artificial data information analysis in an image recombination mode;
and 4, calculating and outputting the data information of the artificial bone CT image.
2. The method for analyzing artificial bone data based on image data processing according to claim 1, wherein: the layered image fusion algorithm is also provided with image preprocessing, and the image preprocessing method comprises the following steps:
step (21), CT image information format conversion is carried out, and the DICOM image is converted into a BMP bitmap file or digital information;
step (22), CT image information preprocessing, wherein different noises are introduced in the formation of a CT image, the image is subjected to smoothing and noise removal preprocessing, and the image is subjected to smoothing processing by adopting median filtering, least mean square filtering or smoothing filtering;
step (23), segmenting CT image information, separating a bone tissue region, automatically selecting a threshold value through image binarization, carrying out image binarization to an Otsu method, and taking a gray value with the minimum variance and the maximum inter-class variance in the CT image information as an optimal threshold value;
step (24), contour extraction; the CT image information contour data is obtained through edge detection, the edge detection is to process the edge of the image to obtain the edge information of closed smoothness, the edge detection is carried out by utilizing an edge detection operator, the image vector contour data is the vectorization of a dot matrix pattern, the search is carried out along the boundary of the image, and the point coordinates on the searched contour line are recorded in a point column for storage.
3. The method for analyzing artificial bone data based on image data processing according to claim 1, wherein: adjusting image data information of the CT image data of the artificial bone according to the size, wherein the size is 1.0mm multiplied by 1.0mm, 0.5mm multiplied by 0.5mm or 0.25mm multiplied by 0.25 mm; the image data information of the artificial bone CT image data is adjusted according to the resolution, and the size is 1.0mm multiplied by 1.0mm, 0.5mm multiplied by 0.5mm or 0.25mm multiplied by 0.25 mm.
4. The method for analyzing artificial bone data based on image data processing according to claim 1, wherein: the artificial bone CT image data information processing also comprises a three-dimensional image recombination step, which comprises the following steps:
(1) curve reconstruction, namely smoothing the contour curve obtained by contour extraction, and performing curve fitting by utilizing an interpolation method or an approximation method to finish curve smoothing;
(2) generating a curved surface, and realizing the reconstruction of the artificial bone CT image data information curved surface by using a Prim algorithm model, wherein the code is realized as follows:
Figure 692927DEST_PATH_IMAGE007
establishing weighted undirected graph by using the model to model and complete the vertex and adjacency relation of the data information of the artificial bone CT image
Figure 439297DEST_PATH_IMAGE008
Wherein the artificial bone CT image data information topology framework vertex
Figure 904914DEST_PATH_IMAGE009
，
Figure 695015DEST_PATH_IMAGE010
Representing the set of all vertexes of the curved surface model of the artificial bone CT image;
Figure 731235DEST_PATH_IMAGE011
representing the data values after being continuously updated,
Figure 264985DEST_PATH_IMAGE012
the connection edge value after the data is continuously updated,
Figure 585108DEST_PATH_IMAGE013
representation model
Figure 562422DEST_PATH_IMAGE014
And model
Figure 600785DEST_PATH_IMAGE015
A connecting edge therebetween;
Figure 407067DEST_PATH_IMAGE016
representation model
Figure 598008DEST_PATH_IMAGE017
And model
Figure 729912DEST_PATH_IMAGE018
By the distance between the geodetic lines
Figure 989992DEST_PATH_IMAGE019
Calculating the minimum path of the model vertex to realize the curved surface reconstruction of the data information of the artificial bone CT image;
(3) the method comprises the following steps of surface materialization, wherein before materialization, the closure, continuity and geometric errors of the surface are detected, and after the surface is detected and modified, the materialization construction is carried out through a 3Dmine module, a three-dimensional GIS module and a BIMPro/E module.
5. The method for analyzing artificial bone data based on image data processing according to claim 1, wherein: the method for analyzing the data information of the artificial bone CT image is an EMD algorithm model.
6. The method for analyzing artificial bone data based on image data processing according to claim 1, wherein: the working method of the EMD algorithm model comprises the following steps:
the method comprises the following steps of converting an artificial bone CT image data information equation quantity into a signal formula, wherein the signal formula is as follows:
Figure 616277DEST_PATH_IMAGE020
(6)
in the formula (6)
Figure 910992DEST_PATH_IMAGE021
Representing the artificial bone CT image data information fault signal function,
Figure 213797DEST_PATH_IMAGE022
a summary of artificial bone input data representing simulated faults,
Figure 711906DEST_PATH_IMAGE023
representing normal artificial bone data;
EMD solution is carried out on a signal function of a formula (6), the maximum fault bearing fixed material and the minimum bearing material load of the artificial bone are calculated by fitting the state data information of each simulated artificial bone, the maximum allowable data error of the artificial bone is reflected according to the average value of the maximum fault bearing fixed material and the minimum bearing material load, and an error function is recorded as:
Figure 391149DEST_PATH_IMAGE024
(7)
in the formula (7), the first and second groups,
Figure 274791DEST_PATH_IMAGE025
represents the maximum allowable failure amount of polymethyl methacrylate or high-density polyethylene in the test artificial bone,
Figure 764810DEST_PATH_IMAGE026
representing the data of the rated bearing material failure of the tested artificial bone,
Figure 733903DEST_PATH_IMAGE027
indicating the minimum bearing material load of the artificial bone;
the formula (1) is combined with the formula (2) to convert the maximum fault information quantity of the tested artificial bone into a signal function, namely:
Figure 951257DEST_PATH_IMAGE028
(8)
in the formula (8), the first and second groups,
Figure 971297DEST_PATH_IMAGE029
representing a function of the maximum fault information quantity of the tested artificial bone;
the first order input signal converted into recognizable by algorithmic programming is represented as:
Figure 615905DEST_PATH_IMAGE030
(9)
in the formula (9), the reaction mixture,
Figure 72294DEST_PATH_IMAGE031
a first order signal that represents an algorithm's programming recognition,
Figure 844072DEST_PATH_IMAGE032
representing analog input data that satisfies the EMD algorithm conditions,
Figure 702307DEST_PATH_IMAGE033
representing the simulated artificial bone fault data component.
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