CN112070900B - Modeling method of 3-10 years old child head skull morphological model - Google Patents
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- 210000003625 skull Anatomy 0.000 title claims abstract description 54
- 238000000034 method Methods 0.000 title claims abstract description 22
- 230000000877 morphologic effect Effects 0.000 title abstract description 7
- 239000011159 matrix material Substances 0.000 claims abstract description 12
- 238000012847 principal component analysis method Methods 0.000 claims abstract description 4
- 238000000513 principal component analysis Methods 0.000 claims description 16
- 238000012545 processing Methods 0.000 claims description 7
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- 210000000988 bone and bone Anatomy 0.000 claims description 4
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- 239000012535 impurity Substances 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000007619 statistical method Methods 0.000 abstract description 2
- 206010019196 Head injury Diseases 0.000 description 4
- 208000014674 injury Diseases 0.000 description 4
- 208000027418 Wounds and injury Diseases 0.000 description 3
- 230000006378 damage Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
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- 206010033799 Paralysis Diseases 0.000 description 1
- 206010039203 Road traffic accident Diseases 0.000 description 1
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- 238000004364 calculation method Methods 0.000 description 1
- 230000002490 cerebral effect Effects 0.000 description 1
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- 238000009795 derivation Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000030400 head development Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007670 refining Methods 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
- 230000008733 trauma Effects 0.000 description 1
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
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Abstract
A modeling method of a 3-10 year old child head skull morphological model relates to a modeling method, which comprises the following steps: acquiring a head skull shape and thickness coordinate point set by adopting Mimics, geomagic and MATLAB software; and simplifying the coordinate point set by adopting a principal component analysis method, and establishing a regression relation between the principal component score matrix and the age and the head circumference. The invention establishes the model of the shape and thickness of the skull of the head of the child through medical CT images, statistical analysis and the like, and provides necessary data support for the study of the head of the child.
Description
Technical Field
The invention relates to a modeling method, in particular to a modeling method of a 3-10-year-old child head skull morphological model.
Background
Cerebral trauma in children is an important cause of paralysis and even death in children. Especially in traffic accidents, children are a more vulnerable group. An important means for reducing the head injury of children is to develop protection for the heads of the children's passengers, and the premise of developing protection researches is to figure out the head injury mechanism of the children and formulate the injury index and the corresponding limit value of the heads of the children. Because of the restriction in ethical aspects and the like, the lack of cadaver tests does not establish a recognized evaluation child head injury index and a corresponding limit value at home and abroad, the disclosed child head injury index and the limit value thereof are basically obtained by simple equal-proportion scaling from the adult injury limit value, and the influence of the specificity of the child head development characteristic on the injury limit value is not considered.
In recent years, domestic and foreign scholars build a finite element model of the head of a child, some of the models are simulated, the reliability of the models is verified, some of the models are not verified to be feasible through comparison with experiments, but the models still have defects, the models are rough, and effective high-quality skull morphological models of children in different age groups are still lacking. The difficulty is that the structures of different ages and different cranium bones are not very consistent.
Disclosure of Invention
The invention provides a modeling method of a 3-10-year-old child head skull morphological model, which is widely applied to the establishment of other biological structure finite element models with age-related differences and provides necessary data support for the child head research.
The technical scheme adopted for solving the technical problems is as follows:
a modeling method of a 3-10 year old child head skull morphological model comprises the following steps:
s1, acquiring a head skull shape and thickness coordinate point set by adopting Mimics, geomagic and MATLAB software;
s2, simplifying the coordinate point set by adopting a principal component analysis method, and establishing a regression relation between the principal component score matrix and the age and the head circumference.
Further, the specific steps of the step S1 are as follows:
s11, extracting and reconstructing a geometric model;
s12, repairing and adjusting the geometric model;
s13, intercepting a contour curve in a layering way;
s14, fitting a cubic spline curve;
s15, extracting a contour coordinate point set.
Further, the specific steps of the step S11 are as follows: preprocessing of skull model data is completed through medical image processing software chemicals, 3D forms of the skull are reconstructed by using CT data of each sample, bone structures of the skull are extracted according to different Hu values, and the generated skull three-dimensional model is converted into a form of discrete point cloud to be led out.
Further, the specific steps of the step S12 are as follows:
s121, performing preliminary smoothing on the obtained skull structure by using Geomagic software, removing impurity points in the model, and repairing some protruding and damaged positions;
s122, manually translating and rotating the gesture of each sample to adjust the gesture of each sample to be consistent;
s123, deleting the sample faces uniformly.
Further, the specific steps of the step S13 are as follows: taking the reference plane after the posture adjustment as a lowest plane, and taking a plane which passes through the highest point of the 3D skull model in the z-axis direction and is parallel to the reference plane as the highest plane; inserting 50 planes with equal space between the highest plane and the lowest plane, intersecting the created 50 cross sections with the model, wherein 49 groups of planes with lower height in the z-axis direction are intersected with the model to obtain 98 groups of curves, deleting contour lines at the intersecting positions of the reference planes of the manually repaired marks to obtain 48 groups of planes, intersecting the highest plane with the model to obtain a point, marking the point as a point A, processing 96 groups of curves, manually deleting the internal contour of the skull, retaining the external contour of the skull, dispersing each curve as a coordinate point, and deriving the coordinates of all coordinate points and the point A which form 96 curves.
Further, the specific steps of the step S14 are as follows: when Matlab is used for calculating a cubic spline function, a periodic boundary condition of 'periodic' is selected, namely, curves are connected end to end, second-order continuity is kept, and therefore the intercepted curves are re-fitted.
Further, the specific steps of the step S15 are as follows:
processing the obtained cubic spline curve so as to unify the number of the coordinate points of each sample, and simultaneously, establishing a one-to-one correspondence between the marked points on the same position of each sample; in each sample, taking a straight line which passes through the highest point of the model in the z-axis direction and is parallel to the z-axis as an axis, starting from 0 DEG in the x-axis direction, making a plane every 6 DEG, finding out the intersection point of each plane and the above fitted cubic spline curve, using the method, obtaining 120 intersection points on the inner and outer contours of each plane, and adding the coordinate of the highest point of the skull in the z-axis direction into the coordinate point set of the skull contour, so that all models have the same gesture and the same reference position.
Further, the specific steps of the step S2 are as follows:
s21, principal component analysis
By principal component analysis, it is possible to achieve main information in which the original data is replaced with a small amount of data. After the data obtained in the step S15 is subjected to principal component analysis, reproduction is performed with 50 dimensions, and higher accuracy is achieved.
S22, regression analysis
Establishing a regression relation between the principal component score matrix and head macroscopic parameters through principal component analysis, and taking the score matrix of 50 principal components to establish a regression relation with the age of the child and the circumference of the head of the child; extracting a head model with skin by adopting Mimics software, and cutting the maximum perimeter of the head in Geomagic so as to obtain the perimeter of the head of the child.
Further, the thickness measuring method for measuring the skull shape comprises the steps of selecting a certain coordinate point of the outer layer, selecting a plurality of inner side points which are closer to the outer side point, fitting the inner side points into a curved surface, dispersing the curved surface into more dense points, finding the inner side points which are closest to the outer side point from the dense points, and taking the distance between the inner side points as the thickness of the outer side point.
The beneficial technical effects of the invention are as follows: the invention establishes the fracture limit value of the head skull of the child through the methods of medical CT image, statistical analysis, parameterized finite element model and cadaver test reproduction, and provides necessary data support for head study of the child.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a schematic flow chart of acquiring contour form data coordinate points of the skull of a 3-10 year old child;
FIG. 3 is a schematic illustration of the thickness principle of the skull;
3-1 in FIG. 3 is a point taken from the outer side of the skull, 3-2 is a point plot of a discretized multi-point plot fitted to the inner surface of the skull;
FIG. 4 is a diagram showing the comparison between the first 50 principal components and the original model;
fig. 5 is a schematic representation of a predicted skull morphology model of a 10 year old child.
Detailed Description
As shown in fig. 1 to 5, a modeling method of a 3-10 year old child's head skull morphology model includes the steps of:
s1, acquiring a head skull shape and thickness coordinate point set by adopting Mimics, geomagic and MATLAB software.
Further refining, reconstructing a cervical vertebra geometric model by using a CT section scanning image, performing geometric simplification, repair and the like, performing posture position adjustment on the geometric model, obtaining a contour curve in a layering manner, and dispersing coordinate points corresponding to the contour curve one by one after re-fitting.
S2, simplifying a coordinate point set by adopting a principal component analysis method, and establishing a regression relation between a principal component score matrix and the age and the head circumference, so that the corresponding head outline shape and thickness of the child can be predicted according to the age and the head circumference of the child.
In at least one embodiment, the specific steps of the step S1 are as follows:
s11, geometric model extraction and reconstruction
Preprocessing of skull model data is accomplished by medical image processing software chemicals, and the 3D morphology of the skull is reconstructed using CT data of each sample. And extracting the bone structure of the skull according to different Hu values, and converting the generated skull three-dimensional model into a form of discrete point cloud for derivation.
S12, repairing and adjusting geometric model
The method comprises the following specific steps:
s121, performing preliminary smoothing on the obtained skull structure by using Geomagic software, removing impurity points in the model, and repairing some protruding and damaged positions;
s122, in order to enable each child head to have the same posture, correction and adjustment of each sample posture are needed. It is necessary to adjust the posture of each sample to be uniform by manual translation and rotation;
s123, the face is required to be removed uniformly because the face structure is not considered.
S13, intercepting contour curves in layers
Taking the reference plane after the posture adjustment as a lowest plane, and taking a plane which passes through the highest point of the 3D skull model in the z-axis direction and is parallel to the reference plane as the highest plane;
50 planes with equal spacing are inserted between the highest plane and the lowest plane, and the created 50 sections are intersected with the model, wherein 49 groups of planes with lower height in the z-axis direction are intersected with the model to obtain 98 groups of curves (the skull has thickness, so the curves cut by each plane have two).
The contour line at the intersection of the reference planes has a trace of manual repair in the previous process, and the curve is incomplete, so that the contour line is truncated, and finally 48 groups of planes are obtained.
The highest plane intersects the model to obtain a point, designated as point a.
And processing 96 groups of curves, manually deleting the inner contour of the skull, reserving the outer contour of the skull, dispersing each curve into coordinate points, and deriving the coordinates of all coordinate points and point A which form 96 curves.
S14, fitting a cubic spline curve
To facilitate subsequent processing, the truncated curves need to be re-fitted. The cubic spline curve is convenient to calculate and can ensure continuous change of the slope of the curve by passing through some control points, and the cubic spline curve is selected to finish re-fitting.
In the actual calculation process, the number of the mark points is large, and the curve can accurately reflect the original outline of the skull. The cubic spline curve is calculated here using the csape function provided by Matlab, while the curves herein are all closed.
When Matlab is used for calculating a cubic spline function, periodic boundary conditions of 'periodic' are selected, namely, curves are connected end to end, and second-order continuity is kept.
S15, extracting a contour coordinate point set
Processing the obtained cubic spline curve so as to unify the number of the coordinate points of each sample, and simultaneously, establishing a one-to-one correspondence between the marked points on the same position of each sample;
as shown in fig. 2, in each sample, a straight line passing through the highest point of the model in the z-axis direction and parallel to the z-axis is taken as an axis, a plane is made every 6 ° from the x-axis direction, and the intersection point of each plane and the above fitted cubic spline curve is found. In this way, 120 intersection points are obtained on the inner and outer contours of each plane, and the coordinates of the highest point of the skull in the z-axis direction are also added to the coordinate point set of the skull contour. So that all models have the same pose and the same reference position.
In at least one embodiment, the specific steps of the step S2 are as follows:
s21, principal component analysis
By principal component analysis, it is possible to achieve main information in which the original data is replaced with a small amount of data. After the data obtained in the step S15 is subjected to principal component analysis, reproduction is performed with 50 dimensions, and higher accuracy is achieved.
Schematic diagram of principal component analysis followed by comparison with original model, as shown in fig. 4:
by principal component analysis, almost all information of the original data can be replaced with a small amount of data. Different numbers of the first few principal components can represent the original data information. After principal component analysis, 50 dimensions are selected for reproduction, and higher accuracy is achieved.
S22, regression analysis
By the principal component analysis above, a regression relationship between the principal component score matrix and the head macroscopic parameters can be established. The method comprises the steps that a regression relation is established between a scoring matrix of 50 main components and the age of the child and the circumference of the head of the child;
the method of measuring the circumference of the child's head is essentially in line with hospitals. Extracting a head model with skin by adopting Mimics software, and cutting the maximum perimeter of the head in Geomagic so as to obtain the perimeter of the head of the child.
Based on the above scheme, as shown in fig. 3, the method for measuring the thickness of the skull morphology is to select a certain coordinate point of the outer layer, select several inner points closer to the outer point, fit the inner points into a curved surface, then discrete the curved surface into more dense points, and find the inner points closest to the outer point from the dense points, wherein the distance between the inner points is the thickness of the outer point.
A schematic diagram for establishing an age head circumference regression analysis to predict the head profile of a 10 year old child is shown in fig. 5:
by the principal component analysis above, a regression relationship between the principal component score matrix and the head macroscopic parameters can be established. The method comprises the steps that a regression relation is established between a scoring matrix of 50 main components and the age of the child and the circumference of the head of the child;
the method of measuring the circumference of the child's head is essentially in line with hospitals. Extracting a head model with skin by adopting Mimics software, and cutting the maximum perimeter of the head in Geomagic so as to obtain the perimeter of the head of the child.
Finally, it should be noted that: the foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the invention, and all equivalent structures or modifications made by the present invention and the accompanying drawings, or direct or indirect application in other relevant technical fields, are included in the scope of the present invention.
Claims (1)
1. The modeling method for the 3-10 year old child head skull morphology statistics is characterized by comprising the following steps of:
s1, acquiring a head skull shape and thickness coordinate point set by adopting Mimics, geomagic and MATLAB software;
s2, simplifying a coordinate point set by adopting a principal component analysis method, establishing a regression relation between a principal component score matrix and age and head circumference,
the specific steps of the step S1 are as follows:
s11, extracting and reconstructing a geometric model;
s12, repairing and adjusting the geometric model;
s13, intercepting a contour curve in a layering way;
s14, fitting a cubic spline curve;
s15, extracting a contour coordinate point set;
the specific steps of the step S11 are as follows: preprocessing of skull model data is completed through medical image processing software chemicals, 3D forms of the skull are reconstructed by using CT data of each sample, bone structures of the skull are extracted according to different Hu values, the generated skull three-dimensional model is converted into a form of discrete point cloud for guiding out,
the specific steps of the step S12 are as follows:
s121, performing preliminary smoothing on the obtained skull structure by using Geomagic software, removing impurity points in the model, and repairing some protruding and damaged positions;
s122, manually translating and rotating the gesture of each sample to adjust the gesture of each sample to be consistent;
s123, uniformly deleting the sample face,
the specific steps of the step S13 are as follows: taking the reference plane after the posture adjustment as a lowest plane, and taking a plane which passes through the highest point of the 3D skull model in the z-axis direction and is parallel to the reference plane as the highest plane; inserting 50 planes with equal space between the highest plane and the lowest plane, intersecting the created 50 cross sections with the model, wherein 49 groups of planes with lower height in the z-axis direction are intersected with the model to obtain 98 groups of curves, deleting contour lines at the intersection positions of the reference planes of the manually repaired marks to obtain 48 groups of planes, intersecting the highest plane with the model to obtain a point, marking the point as a point A, processing 96 groups of curves, manually deleting the internal contour of the skull, retaining the external contour of the skull and dispersing each curve as a coordinate point, deriving the coordinates of all coordinate points and the point A which form 96 curves,
the specific steps of the step S14 are as follows: when Matlab is used for calculating a cubic spline function, a periodic boundary condition of 'periodic' is selected, namely, curves are connected end to end, second-order continuity is kept, so that the intercepted curves are re-fitted,
the specific steps of the step S15 are as follows:
processing the obtained cubic spline curve so as to unify the number of the coordinate points of each sample, and simultaneously, establishing a one-to-one correspondence between the marked points on the same position of each sample; in each sample, taking a straight line which passes through the highest point of the model in the z-axis direction and is parallel to the z-axis as an axis, starting from 0 DEG in the x-axis direction, making a plane every 6 DEG, finding out the intersection point of each plane and the above fitted cubic spline curve, using the method, obtaining 120 intersection points on the inner and outer contours of each plane, adding the coordinate of the highest point of the skull in the z-axis direction into the coordinate point set of the skull contour, so that all models have the same gesture and the same reference position,
the specific steps of the step S2 are as follows:
s21, principal component analysis
By principal component analysis, it is possible to achieve main information in which the original data is replaced with a small amount of data. After the data obtained in the step S15 is subjected to principal component analysis, reproduction is carried out by using 50 dimensions;
s22, regression analysis
Establishing a regression relation between the principal component score matrix and head macroscopic parameters through principal component analysis, and taking the score matrix of 50 principal components to establish a regression relation with the age of the child and the circumference of the head of the child; extracting a head model with skin by adopting Mimics software, and cutting the maximum perimeter of the head in Geomagic so as to obtain the perimeter of the head of the child.
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| CN1399763A (en) * | 1999-03-03 | 2003-02-26 | 弗吉尼亚州立大学 | 3-D shape measurements using statistical curvature analysis |
| CN101882326A (en) * | 2010-05-18 | 2010-11-10 | 广州市刑事科学技术研究所 | Three-dimensional craniofacial reconstruction method based on overall facial structure shape data of Chinese people |
| KR20120023476A (en) * | 2010-09-01 | 2012-03-13 | 김도현 | Method of acquiring human head modeling data, and robot treatment system using data acquired by the method |
| CN102521875A (en) * | 2011-11-25 | 2012-06-27 | 北京师范大学 | Partial least squares recursive craniofacial reconstruction method based on tensor space |
| CN105405167A (en) * | 2015-11-05 | 2016-03-16 | 中国人民解放军第三军医大学第二附属医院 | Finite element modeling method based on complete human head |
| EP3335195A2 (en) * | 2015-08-14 | 2018-06-20 | Metail Limited | Methods of generating personalized 3d head models or 3d body models |
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- 2020-09-18 CN CN202010989450.3A patent/CN112070900B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1399763A (en) * | 1999-03-03 | 2003-02-26 | 弗吉尼亚州立大学 | 3-D shape measurements using statistical curvature analysis |
| CN101882326A (en) * | 2010-05-18 | 2010-11-10 | 广州市刑事科学技术研究所 | Three-dimensional craniofacial reconstruction method based on overall facial structure shape data of Chinese people |
| KR20120023476A (en) * | 2010-09-01 | 2012-03-13 | 김도현 | Method of acquiring human head modeling data, and robot treatment system using data acquired by the method |
| CN102521875A (en) * | 2011-11-25 | 2012-06-27 | 北京师范大学 | Partial least squares recursive craniofacial reconstruction method based on tensor space |
| EP3335195A2 (en) * | 2015-08-14 | 2018-06-20 | Metail Limited | Methods of generating personalized 3d head models or 3d body models |
| CN105405167A (en) * | 2015-11-05 | 2016-03-16 | 中国人民解放军第三军医大学第二附属医院 | Finite element modeling method based on complete human head |
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