CN108334899A - Quantify the bone age assessment method of information integration based on hand bone X-ray bone and joint - Google Patents

Quantify the bone age assessment method of information integration based on hand bone X-ray bone and joint Download PDF

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CN108334899A
CN108334899A CN201810080541.8A CN201810080541A CN108334899A CN 108334899 A CN108334899 A CN 108334899A CN 201810080541 A CN201810080541 A CN 201810080541A CN 108334899 A CN108334899 A CN 108334899A
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吴健
张久成
余柏翰
陆逸飞
应兴德
林志文
吴边
陈为
吴福理
吴朝晖
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Zhejiang University ZJU
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Abstract

The invention discloses a kind of bone age assessment methods quantifying information integration based on hand bone X-ray bone and joint, include the following steps:Step 1, the picture sample of hand bone X-ray is collected, and is classified to sample according to gender, age bracket, grouping information is obtained;Step 2, the bone of sample and joint position are labeled and are divided;Step 3, it is input in convolutional neural networks with actual position information after sample image being pre-processed, is iterated training, obtain bone and the location information and characteristic pattern in joint;Step 4, the morphological feature parameter of bone and joint in sample is calculated;Step 5, the characteristic pattern and morphological feature in bone and joint are fused to composite character information, are input to together with grouping information in convolutional neural networks model and are iterated training;Step 6, model training finishes, and carries out bone age assessment application.Using the present invention, can under the premise of reducing interference from human factor the more simple, fast assessment stone age.

Description

Quantify the bone age assessment method of information integration based on hand bone X-ray bone and joint
Technical field
The invention belongs to medical data excavation applications, are quantified based on hand bone X-ray bone and joint more particularly, to one kind The bone age assessment method of information integration.
Background technology
It, can be with both at home and abroad it is believed that judging that the maturity of development individual is a kind of effective method by the stone age More fully reflect body physiological state, be skeleton development process age description, represent specific normal population (race, Domain, age) in each all ages and classes minor skeleton development general state.The identification of Assessing Standards For Skeletal or stone age are usually to use X-ray after the same method to the hand bone at the same position of subject person shoot X ray image, then by skeletal form in image with General skeletal form is compared.
Stone age is the important indicator of human body maturity, can understand the growth and development potentiality of children early by the stone age, And it is sent out according to skeleton development Trend judgement if appropriate for a certain professional training specifically moved of receiving or to suitable bone itself The professional direction development for educating trend, also has very great help to the diagnosis of some diseases by bone-age determination.
Traditional bone age assessment method is to be compared or stone age X-ray and standard diagram according to epiphysis by expert Developmental testing gives a mark to complete.But it is more demanding to the professional knowledge for the people that scores, the bone to key position is needed when checking Bone is carefully compared.By artificial read tablet it is more or less there are some read tablet errors, with increasing for diagosis amount, diagosis doctor It is raw gradually can by the interference of the unfavorable factors such as fatigue the accuracy of impact evaluation.
Invention content
The present invention provides a kind of bone age assessment method quantifying information integration based on hand bone X-ray bone and joint, can be The more simple, fast assessment stone age under the premise of reduction interference from human factor.
A kind of bone age assessment method being quantified information integration based on hand bone X-ray bone and joint, is included the following steps:
Step 1, the picture sample of hand bone X-ray is collected, and is classified to sample according to gender, age bracket, is divided Group information;
Step 2, coordinate is labeled and preserved to the bone of sample and joint position, obtains actual position information, to mark The bone of note and joint are split;
Step 3, actual position information is input to by pretreated sample image in convolutional neural networks, is changed Generation training, obtains bone and the characteristic pattern in joint;
Step 4, the morphological feature parameter of bone and joint in sample is calculated;
Step 5, the characteristic pattern and morphological feature in bone and joint are fused to composite character information, with grouping information It is input to together in convolutional neural networks model and is iterated training;
Step 6, model training finishes, and carries out bone age assessment application.
The present invention is handled by convolutional neural networks opponent's bone X-ray of design, learns hand bone key bone in X-ray Simultaneously integrated information opponent's bon e formation more comprehensively recognizes characteristic information, to achieve the purpose that bone age assessment.
In step 2, the bone is mainly carpal bone.
Step 3 the specific steps are:
Step 3-1, after sample image is pre-processed and bone, joint markup information input convolution
Neural network carries out feature extraction.
Because in a practical situation, picture clarity that different film making equipment obtains and brightness etc. are without consistency, no Same film making mechanism has certain auxiliary photographic device, these devices have certain interference for picture quality, in order to locate The stability of reason needs to carry out certain pretreatment to input picture.
The pretreatment is to increase the contrast of image using gamma transformation method, and calculation is as follows:
I ' (x, y)=(I (x, y) * r)γ,r∈[0,1]
Wherein, I (x, y) is the gray value for inputting pixel, and I ' (x, y) is the gray value of output pixel, and γ is gamma factor, By changing the value of γ, increase the contrast of image.
The Convolution Formula that the feature extraction uses is as follows:
Wherein, f (x, y) is input picture, and g (x, y) is convolution kernel function, and m and n respectively represents the length and width of convolution kernel.
The purpose of feature extraction is to extract image key message, such as texture, shape.Traditional feature extracting method There is certain dependence to use environment, situation lacks certain adaptability for more complex in the case of.
Step 3-2, according to the feature of extraction through believing to the position in output bone and joint after transmission before convolutional neural networks Breath, the error between location information and the location information of mark by calculating convolutional neural networks output, is grasped in back transfer The parameter that convolutional layer is updated in work is trained the convolutional neural networks until model convergence, obtains bone and joint Location information and feature..
In step 4, according to the segmentation figure computation of morphology characteristic parameter of mark bone and joint, the morphological feature Parameter includes area, maximum gauge and Hu squares.
The maximum gauge uses Euclidean distance method, calculation formula as follows:
Wherein x, y are the coordinate values of pixel.
Hu squares have rotation, zooming and panning invariance to the feature description of two-dimensional bodies, and Hu squares are by normalizing center It is worth to away from 2 ranks of rear calculating and 3 ranks, the calculation formula of the centre-to-centre spacing is as follows:
Wherein x, y are the coordinate values of pixel,It is the coordinate of target centroid, m, n are respectively the length of image and wide, p It is 0,1,2,3 with the value range of q ..., p+q's and indicates rank square.
In step 5, the repetitive exercise is specially:Mistake between the prediction result and legitimate reading of continuous computation model Difference, by the weight parameter in error update convolutional neural networks, until the increase with training iterations and error no longer Reduce and illustrates that model is restrained.
The present invention is handled by convolutional neural networks opponent's bone X-ray of design, learns hand bone key bone in X-ray Simultaneously integrated information opponent's bon e formation more comprehensively recognizes characteristic information, to achieve the purpose that bone age assessment, can reduce artificially The more simple, fast assessment stone age under the premise of factor is interfered
Description of the drawings
Fig. 1 is the flow chart of the bone age assessment method of bone of the present invention and joint quantization information integration;
Fig. 2 is that bony segment and joint mark schematic diagram.
Specific implementation mode
To make technical scheme of the present invention and advantage be more clearly understood, the present invention is carried out below in conjunction with specific embodiment It is further to be described in detail.
As shown in Figure 1, a kind of bone age assessment method quantifying information integration based on hand bone X-ray bone and joint, including Following steps:
(1) according to relevant medical standard, the age of sample is limited to 0 to 18 years old, and a large amount of palms are collected according to the age X-ray picture.In view of the difference of male and female skeleton development maturity, male and female need to be distinguished when making a collection of specimens collection Sample.
(2) sample mark and bone segmentation.As shown in Fig. 2, needing to carry out the coordinate position of crucial bone and joint Mark, the positions such as the carpal bone of hand bone and joint mainly by being manually labeled and preserving coordinate information by the present embodiment.Segmentation A variety of methods, the present embodiment can be selected to select ostu methods to be split the bone of mark and joint, obtain sample image Bone segmentation image.
(3) image of opponent's bone X-ray pre-processes.Because in a practical situation, the figure that different film making equipment obtains Piece clarity and brightness etc. do not have consistency, and different film making mechanisms has certain auxiliary photographic device, these devices pair There is certain interference in picture quality, in order to which the stability of processing needs to carry out certain pretreatment to input picture.
In specific implementation, increase the contrast of image using gamma transformation method, calculation is as follows:
I ' (x, y)=(I (x, y) * r)γ,r∈[0,1]
Wherein, γ values are boundary with 1, and value is smaller, stronger to the extension effect of low gray portion, and value is bigger, to high ash The extension for spending part is stronger, and by different γ values, can reach enhances low gray scale or high gray portion to reach increase figure The contrast of picture.The intensity value ranges of image are limited in [0,255] section in the specific implementation.
15 γ values are randomly selected within section [0.5,2.0], and contrast is all carried out to every image using 15 γ Enhancing is handled and image is added in training set by treated.
(4) image characteristics extraction.Image data feature extraction is to carry out the basis of the advanced identification of image, feature extraction purpose It is to extract image key message, such as texture, shape.Traditional feature extracting method has centainly use environment Dependence, situation lacks certain adaptability for more complex in the case of.
The present embodiment extracts feature using convolution method, and the feature of extraction has certain regionality, filters out simultaneously Noise, the feature after multilayer convolution contain more semantic informations and have preferable space-invariance, calculate public Formula is as follows:
Wherein, f (x, y) is input picture, and g (x, y) is convolution kernel function, and x and y is pixel coordinate value, m and n generations respectively The length and width of table convolution kernel, the pixel that image is traversed with convolution kernel are calculated.Because difference can be calculated in different convolution kernels Feature, so different convolution kernel traverses image information to extract different characteristic informations.
(5) the position positioning of crucial bone and joint.The present invention in the specific implementation, passes through the feature of convolutional neural networks It extracts and is realized with the method for positioning.Image obtains the spy in image bone and joint by the feature extraction layer of convolutional neural networks Reference ceases, and is distinguished crucial bone and the characteristic information in joint with non-key bone and the characteristic information in joint using grader It opens, then can obtain the coordinate information of the characteristic information in entire characteristic pattern in crucial bone and joint.
In specific implementation, in convolutional neural networks opponent's bone X-ray at the feature extraction and classification in crucial bone and joint In the process of continuous training optimization, so its location information is also in continuous transformation and update.Because of the numerical value of target location coordinate With continuity, the location information and actual position information of measurement convolutional neural networks output are calculated using the mode of recurrence Error, calculation are as follows:
in which s.t.
Wherein, tuIndicate true location coordinate, v indicates the position coordinates of network output, using piecewise function, according to true The range of difference x between prediction coordinate selects its corresponding calculation.
Calculate the error between the coordinate information and the true coordinate information marked by hand of convolutional neural networks output, error It is smaller, illustrate that the positioning to target is more accurate.In the calculating in this stage, using above-mentioned loss function as convolutional Neural net The convergence of the location model of network, when with training iterations increase and loss parameter no longer decline when, illustrate model Convergence.
(6) computation of morphology characteristic parameter.The crucial bone of hand bone and joint are variant in different age level forms. In order to quantify difference and the variation of form, its morphological feature is calculated using morphologic method in the present invention.Because of bone and joint Form have diversity, selection calculate in physical characteristic comparatively apparent feature.
The bone obtained according to segmentation in specific implementation and joint, calculate the area, maximum gauge, Hu squares of target, The calculating of middle maximum gauge uses common Euclidean distance method, calculation formula as follows:
Wherein x, y are the coordinate values of pixel.
Hu squares have rotation, zooming and panning invariance to the feature description of two-dimensional bodies, after normalizing centre-to-centre spacing Calculate being worth to for 2 ranks and 3 ranks.Central moment is calculated first, then centre-to-centre spacing divided by 0 rank centre-to-centre spacing obtain normalization centre-to-centre spacing, The calculation formula of centre-to-centre spacing is as follows:
Wherein x, y are the coordinate values of pixel,It is the coordinate of target centroid, m, n are respectively the length and width of image.
In the concrete realization, it calculates the morphological feature parameter of target using the method and parameter is formed into one group of parameter Vector.
(7) comprehensive characteristics carry out model training.After step (5) is to crucial bone and joint orientation, while obtaining corresponding positions The characteristic pattern set.The characteristic pattern and morphological feature of crucial bone and joint, two kinds of information all represent palm key bone and Joint is showed in the feature of different viewing angles, each information all has important value.Both the above information is fused to Composite character information, and will enter into convolutional neural networks model and be iterated training, continuous computation model in training process Prediction result and legitimate reading between error, pass through the weight parameter in error update convolutional neural networks.When iteration time Number is continuously increased, and it is to illustrate that model is restrained, and Semantic feature and the bone age assessment of quantitative information are obtained that error, which no longer reduces, Model.
In specific application, the image of hand bone X-ray is passed to as input quantity in assessment models, is inputted automatically Amount pre-processes and exports assessment result.

Claims (9)

1. a kind of bone age assessment method quantifying information integration based on hand bone X-ray bone and joint, which is characterized in that including with Lower step:
Step 1, the picture sample of hand bone X-ray is collected, and is classified to sample according to gender, age bracket, grouping letter is obtained Breath;
Step 2, coordinate is labeled and preserved to the bone of sample and joint position, obtains actual position information, to mark Bone and joint are split;
Step 3, actual position information is input to by pretreated sample image in convolutional neural networks, is iterated instruction Practice, obtains bone and the characteristic pattern in joint;
Step 4, the morphological feature parameter of bone and joint in sample is calculated;
Step 5, the characteristic pattern and morphological feature in bone and joint are fused to composite character information, together with grouping information It is input in convolutional neural networks model and is iterated training;
Step 6, model training finishes, and carries out bone age assessment application.
2. the bone age assessment method according to claim 1 for quantifying information integration based on hand bone X-ray bone and joint, It is characterized in that, in step 2, the bone is carpal bone.
3. the bone age assessment method according to claim 1 for quantifying information integration based on hand bone X-ray bone and joint, Be characterized in that, step 3 the specific steps are:
Pretreated sample image and bone, joint markup information input convolutional neural networks are carried out feature and carried by step 3-1 It takes;
Step 3-2, according to the feature of extraction through, to the location information of output bone and joint after transmission, leading to before convolutional neural networks The error between the location information and the location information of mark that calculate convolutional neural networks output is crossed, in back transfer operation more The parameter of new convolutional layer is trained the convolutional neural networks until model convergence, obtains the position in bone and joint Information and feature.
4. the bone age assessment method according to claim 3 for quantifying information integration based on hand bone X-ray bone and joint, It is characterized in that, in step 3-1, the pretreatment is to increase the contrast of image using gamma transformation method, and calculation is such as Under:
I ' (x, y)=(I (x, y) * r)γ,γ∈[0,1]
Wherein, I (x, y) is the gray value for inputting pixel, and I ' (x, y) is the gray value of output pixel, and γ is gamma factor, is passed through The value for changing γ, increases the contrast of image.
5. the bone age assessment method according to claim 3 for quantifying information integration based on hand bone X-ray bone and joint, It is characterized in that, in step 3-1, the Convolution Formula that the feature extraction uses is as follows:
Wherein, f (x, y) is input picture, and g (x, y) is convolution kernel function, and m and n respectively represents the length and width of convolution kernel.
6. the bone age assessment side according to claim 1 for quantifying information integration based on hand bone X-ray key bone and joint Method, which is characterized in that in step 4, the morphological feature parameter includes area, maximum gauge and Hu squares.
7. the bone age assessment side according to claim 6 for quantifying information integration based on hand bone X-ray key bone and joint Method, which is characterized in that the maximum gauge uses Euclidean distance method, calculation formula as follows:
Wherein x, y are the coordinate values of pixel.
8. the bone age assessment side according to claim 6 for quantifying information integration based on hand bone X-ray key bone and joint Method, which is characterized in that the Hu squares after normalizing centre-to-centre spacing by calculating being worth to for 2 ranks and 3 ranks, the meter of the centre-to-centre spacing It is as follows to calculate formula:
Wherein x, y are the coordinate values of pixel,The coordinate of target centroid, m, n be respectively image length with it is wide, p and q's Value range is 0,1,2,3 ..., p+q and indicate rank square.
9. the bone age assessment side according to claim 1 for quantifying information integration based on hand bone X-ray key bone and joint Method, which is characterized in that in step 5, the repetitive exercise is specially:The prediction result of continuous computation model and legitimate reading it Between error, by the weight parameter in error update convolutional neural networks, until the increase with training iterations and mistake Difference no longer reduces, and illustrates that model is restrained.
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Application publication date: 20180727