CN111724357B - Arm bone density measurement method based on digital radiological image and support vector regression - Google Patents
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Abstract
The invention discloses an arm bone density measuring method based on digital radiological images and support vector regression, which comprises the following steps: s1, inputting a DR image and segmenting out a region of interest; s2, respectively extracting gray features and texture features of the DR image from the region of interest; s3, building a regression model by using the support vector: establishing a regression model by using an SVR algorithm, and carrying out regression by using the image characteristic values extracted in the S2 and the corresponding bone density label data; s4, calculating a bone mineral density predicted value and outputting a predicted result: and (3) predicting the bone density of the DR image of the new subject by using the regression model generated by training in the step S3. According to the invention, the segmentation of the region of interest and the extraction of the image features are carried out on the existing DR image, and the regression model is established by the SVR algorithm to measure the bone density of the DR image.
Description
Technical Field
The invention belongs to the technical field of computer vision and medical image processing, and particularly relates to an arm bone density measurement method based on digital radiological images and support vector regression.
Background
Osteoporosis is a common and frequently occurring disease that severely threatens the health of the middle-aged and elderly people. Over 2 million patients worldwide are currently in existence, and osteoporosis is an increasingly serious public health problem. Although diagnostic techniques are now advancing, diagnostic methods for osteoporosis remain unpopular. The main basis for diagnosing osteoporosis is to measure the decrease in bone density of the bones. Bone density is a quantitative diagnostic index for bone and is commonly used for diagnosing osteoporosis, predicting fracture risk and assessing treatment effect.
Currently, the Dual-energy X-ray absorption method (Dual X-ray Absorptiometry, DXA) for detecting bone density is a standard accepted by clinical diagnosis of osteoporosis, and is also the only unified index recommended by the world health organization to be used as a global diagnosis of osteoporosis. However, DXA is not very widely used because of the disadvantages of relatively expensive detection equipment, high maintenance costs, and the need for specialized training for professional operations.
In this context, the application of artificial intelligence technology to the medical field has made possible the development of new bone density measurement techniques.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides the arm bone density measuring method based on digital radiological image and support vector regression, which is used for segmenting the region of interest and extracting the image characteristics on the existing DR image, and the regression model is established through the SVR algorithm to measure the arm bone density, so that the errors caused by different bone density detectors are eliminated, and the accuracy of bone density assessment is improved.
The aim of the invention is realized by the following technical scheme: the arm bone density measuring method based on digital radiological image and support vector regression comprises the following steps:
s1, inputting a DR image and segmenting out a region of interest;
s2, extracting image features of the segmented regions, and respectively extracting gray features and texture features of the DR images of the segmented regions of interest;
s3, building a regression model by using the support vector: establishing a regression model by using an SVR algorithm, and carrying out regression by using the image characteristic values extracted in the S2 and the corresponding bone density label data;
s4, calculating a bone mineral density predicted value and outputting a predicted result: and (3) predicting the bone density of the DR image of the new subject by using the regression model generated by training in the step S3.
Further, the step S1 includes the following substeps:
s11, segmenting an interested region from an original DR image;
s12, carrying out normalization processing on the image obtained in the step S11, and normalizing the pixel value of the image to be between 0 and 255.
Further, the specific implementation method of the step S2 is as follows:
s21, extracting gray features: respectively extracting 5 gray features of mean, variance, standard deviation, energy and entropy by calculating a gray histogram of the DR image;
average value: reflecting the gray average value of a DR image:
h (i) represents the gray value of the i-th point, and L represents the gray level number of the image;
variance: reflecting the discrete distribution of gray scale of a DR image in terms of value:
standard deviation: is the square root of the variance;
energy: the uniformity of gray level distribution is reflected, and the calculation formula is as follows:
entropy: the uniformity of gray level histogram distribution is reflected, and the calculation formula is as follows:
s22, extracting texture features: extracting the texture features of the image by adopting a gray level co-occurrence matrix method, and respectively extracting 4 texture features of second moment, contrast, correlation and homogeneity;
second moment: reflects the smoothness of a DR image, and the calculation formula is as follows:
W 1 ＝∑ i ∑ j [m(i，j)] 2
m (i, j) represents an element in a gray level co-occurrence matrix, the gray level matrix being defined as a probability from a point of an original image having a gray level i to a point of the original image having a gray level j; i, j=0, 1,2,., L-1;
contrast ratio: the definition of a DR image is reflected, and the calculation formula is as follows:
W 2 ＝∑ i ∑ j (i-j) 2 m(i，j)
correlation coefficient: the linear correlation degree of rows and columns in the gray level co-occurrence matrix is reflected, and the calculation formula is as follows:
wherein,,
μ x ＝∑ i i∑ j m(i，j)
μ y ＝∑ j j∑ i m(i，j)
homogenization: the distribution of elements in the gray level co-occurrence matrix to the diagonal tightness degree is reflected, and the calculation formula is as follows:
s23, data summarizing is carried out on the extracted gray features and texture features, data normalization processing is carried out on feature values, and different features are scaled to map to a specific interval of [ -1,1 ];
the normalized formula is:
where x is the original eigenvalue, x * The normalized characteristic value; mean is the mean value of the feature data, max is the maximum value of the feature data, and min is the minimum value of the feature data.
Further, in the step S3, the bone mineral density label data is the real bone mineral density corresponding to the image.
The beneficial effects of the invention are as follows:
1. the method of the invention performs segmentation of the region of interest and extraction of image features on the existing DR image, and establishes a regression model through SVR algorithm to measure the bone density of the DR image.
2. The bone mineral density measuring result of the method disclosed by the invention is similar to the acquisition result of the existing bone mineral density detector, has small measuring error and high measuring precision, is favorable for improving the accuracy of bone mineral density evaluation, and can be used for clinical detection of the bone mineral density of human arms.
3. Compared with the traditional DXA method, the method disclosed by the invention is favorable for eliminating errors caused by different bone density detectors and improving the repeatability research of experiments, so that the method is expected to become one of the technologies for diagnosing osteoporosis with the most development prospect.
Drawings
FIG. 1 is a flow chart of an arm bone density measurement method based on digital radiological images and support vector regression according to the present invention;
FIG. 2 is a DR image acquired in the present embodiment;
fig. 3 is a region of interest image.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings.
As shown in fig. 1, the method for measuring the arm bone density based on digital radiological image and support vector regression of the invention comprises the following steps:
s1, inputting a DR image and segmenting out a region of interest; the method specifically comprises the following substeps:
s11, segmenting an interested region from an original DR image;
s12, carrying out normalization processing on the image obtained in the step S11, and normalizing the pixel value of the image to be between 0 and 255.
DR image data was acquired on forearms of 173 male and female subjects using an existing bone density detector, as shown in fig. 2; a diagnostic report containing clinical information such as bone density is collected, and the region of interest of the collected DR image data is manually segmented, effectively segmenting the region of interest of bone (ulna region, as shown in fig. 3).
S2, extracting image features of the segmented regions, and respectively extracting gray features and texture features of the DR images of the segmented regions of interest; the specific implementation method comprises the following steps:
s21, extracting gray features: respectively extracting 5 gray features of mean, variance, standard deviation, energy and entropy by calculating a gray histogram of the DR image;
average value: reflecting the gray average value of a DR image:
h (i) represents the gray value of the i-th point, and L represents the gray level number of the image;
variance: reflecting the discrete distribution of gray scale of a DR image in terms of value:
standard deviation: is the square root of the variance;
energy: the uniformity of gray level distribution is reflected, and the calculation formula is as follows:
entropy: the uniformity of gray level histogram distribution is reflected, and the calculation formula is as follows:
and 5 gray level features of the mean, the variance, the standard deviation, the energy and the entropy are extracted through the gray level histogram corresponding to the image, wherein the gray level features are 708619223, 5.03144983470948e+23, 709327134311.770, 286421983777.000 and 62393739.6779687 respectively.
S22, extracting texture features: extracting the texture features of the image by adopting a gray level co-occurrence matrix method, and respectively extracting 4 texture features of second moment, contrast, correlation and homogeneity;
second moment: reflects the smoothness of a DR image, and the calculation formula is as follows:
W 1 ＝∑ i ∑ j [m(i，j)] 2
m (i, j) represents an element in a gray level co-occurrence matrix, the gray level matrix being defined as a probability from a point of an original image having a gray level i to a point of the original image having a gray level j; i, j=0, 1,2,., L-1;
contrast ratio: the definition of a DR image is reflected, and the calculation formula is as follows:
W 2 ＝∑ i ∑ j (i-j) 2 m(i，j)
correlation coefficient: the linear correlation degree of rows and columns in the gray level co-occurrence matrix is reflected, and the calculation formula is as follows:
wherein,,
μ x ＝∑ i i∑ j m(i，j)
μ y ＝∑ j j∑ i m(i，j)
homogenization: the distribution of elements in the gray level co-occurrence matrix to the diagonal tightness degree is reflected, and the calculation formula is as follows:
the results of the extracted 4 features of the present embodiment are 0.350538853001145, 0.0203936063936064, 0.998352776124655, 0.989803196803197, respectively.
S23, data summarizing is carried out on the extracted gray features and texture features, data normalization processing is carried out on feature values, and different features are scaled to map to a specific interval of [ -1,1 ];
the normalized formula is:
wherein x is the originalEigenvalues, x * The normalized characteristic value; mean is the mean value of the feature data, max is the maximum value of the feature data, and min is the minimum value of the feature data. The normalization of this example resulted in-0.0676965, -0.151447, -0.0676965,0.248400, -0.965191, -0.196041, -0.115126,0.191109, 0.115126.
S3, building a regression model by using the support vector: a regression model is established by using SVR algorithm, and regression is performed by using the image characteristic values (-0.0676965, -0.151447, -0.0676965,0.248400, -0.965191, -0.196041, -0.115126,0.191109,0.115126) extracted in S2 and the bone density label (real bone density 0.788) of the image. The idea of the SVR algorithm is:
given a training sample, d= { (x) 1 ，y 1 )，(x 2 ，y 2 )，......，(x m ，y m )}，y i E R, a regression model is trained such that f (x) is as close as possible to y, w and b being the model parameters to be determined. Model parameters of the SVR are adjusted through a grid search method, so that an optimal model suitable for measuring bone mineral density is determined.
S4, calculating a bone mineral density predicted value and outputting a predicted result: the bone density of DR image of the new subject was predicted using the regression model generated by training in S3, and the predicted result in this example was 0.777952.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (3)
1. The arm bone density measuring method based on digital radiological image and support vector regression is characterized by comprising the following steps:
s1, inputting a DR image and segmenting out a region of interest;
s2, extracting image features of the segmented regions, and respectively extracting gray features and texture features of the DR images of the segmented regions of interest; the specific implementation method comprises the following steps:
s21, extracting gray features: respectively extracting 5 gray features of mean, variance, standard deviation, energy and entropy by calculating a gray histogram of the DR image;
average value: reflecting the gray average value of a DR image:
h (i) represents the gray value of the i-th point, and L represents the gray level number of the image;
variance: reflecting the discrete distribution of gray scale of a DR image in terms of value:
standard deviation: is the square root of the variance;
energy: the uniformity of gray level distribution is reflected, and the calculation formula is as follows:
entropy: the uniformity of gray level histogram distribution is reflected, and the calculation formula is as follows:
s22, extracting texture features: extracting the texture features of the image by adopting a gray level co-occurrence matrix method, and respectively extracting 4 texture features of second moment, contrast, correlation and homogeneity;
second moment: reflects the smoothness of a DR image, and the calculation formula is as follows:
W 1 ＝∑ i ∑ j [m(i-j)] 2
m (i, j) represents an element in the gray level co-occurrence matrix, i, j=0, 1,2, …, L-1;
contrast ratio: the definition of a DR image is reflected, and the calculation formula is as follows:
W 2 ＝∑ i ∑ j (i-j) 2 m(i，j)
correlation coefficient: the linear correlation degree of rows and columns in the gray level co-occurrence matrix is reflected, and the calculation formula is as follows:
wherein,,
μ x ＝∑ i i∑ j m(i，j)
μ y ＝∑ j j∑ i m(i，j)
homogenization: the distribution of elements in the gray level co-occurrence matrix to the diagonal tightness degree is reflected, and the calculation formula is as follows:
s23, data summarizing is carried out on the extracted gray features and texture features, data normalization processing is carried out on feature values, and different features are scaled to map to a specific interval of [ -1,1 ];
the normalized formula is:
where x is the original eigenvalue, x * The normalized characteristic value; mean is the mean value of the feature data, max is the maximum value of the feature data, and min is the minimum value of the feature data;
s3, building a regression model by using the support vector: establishing a regression model by using an SVR algorithm, and carrying out regression by using the image characteristic values extracted in the S2 and the corresponding bone density label data;
s4, calculating a bone mineral density predicted value and outputting a predicted result: and (3) predicting the bone density of the DR image of the new subject by using the regression model generated by training in the step S3.
2. The method for measuring arm bone density based on digital radiological image and support vector regression according to claim 1, wherein the step S1 comprises the sub-steps of:
s11, segmenting an interested region from an original DR image;
s12, carrying out normalization processing on the image obtained in the step S11, and normalizing the pixel value of the image to be between 0 and 255.
3. The method for measuring the bone mineral density of the arm based on the digital radiological image and the support vector regression according to claim 1, wherein the bone mineral density label data in the step S3 is the real bone mineral density corresponding to the image.
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