CN103325087A - Interpolation method for CT image layer data - Google Patents
Interpolation method for CT image layer data Download PDFInfo
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- CN103325087A CN103325087A CN2013102567271A CN201310256727A CN103325087A CN 103325087 A CN103325087 A CN 103325087A CN 2013102567271 A CN2013102567271 A CN 2013102567271A CN 201310256727 A CN201310256727 A CN 201310256727A CN 103325087 A CN103325087 A CN 103325087A
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Abstract
The invention provides an interpolation method for CT image layer data. Assuming that a CT image totally has N layers of CT pictures, interpolation operation is conducted on the N layers of CT pictures. The interpolation method for the CT image layer data comprises the following steps that an average picture between every two adjacent layers of pictures of the N layers of CT pictures is calculated, and N-1 average pictures are obtained; Gaussian smoothing processing is conducted on the N-1 average pictures, and high-frequency noise is filtered; interpolation rebuilding coefficients are established, and an interpolation rebuilding image is obtained. According to the interpolation method for the CT image layer data, and the artifact phenomenon caused by interpolation failure due to the fact that interlayer data are not aligned can be avoided. The interpolation method for the CT image layer data enables transition between every two layers of the CT images to be smoother, analyzing and positioning can be conducted more conveniently, and diagnosing accuracy can be effectively improved.
Description
Technical field
The present invention relates to medical image diagnostic techniques field, particularly, relate to a kind of interpolation method of CT image slices data.
Background technology
The CT image is the important evidence that patient's disease is diagnosed, because CT examination checks to patient's whole body that normally therefore the image data amount that obtains is very big.In the past, the CT image of finishing for shooting, all be directly to carry out medical diagnosis on disease by visual inspection by the doctor, the doctor is when diagnosing, because image data amount is big, add that human eye is limited to the recognition capability of image, various situations such as mistaken diagnosis inevitably can occur, fail to pinpoint a disease in diagnosis have stayed very big hidden danger to the accuracy of patient treatment.
In recent years, the technology of carrying out medical diagnosis on disease in conjunction with the auxiliary doctor of computer technology analysis CT image grows up gradually.At present, when Applied Computer Techniques carries out analyzing and processing to the CT image, for amount of compressed data, can compress processing to the CT image, yet, because original CT image acquisition interlamellar spacing may be bigger, the variation between layer and the layer is bigger like this, and the part of intercalary delection may be omitted important lesion region.And, when carrying out that image is cut apart etc. and handling, can occur two-layer between target data situation about differing greatly, influence the excessive smoothness of interlayer, be unfavorable for analyzing and positioning.
Summary of the invention
At defective of the prior art, the purpose of this invention is to provide a kind of interpolation method of CT image slices data.
According to an aspect of the present invention, provide a kind of interpolation method of CT image slices data, suppose CT image one total N layer CT picture, N layer CT picture is carried out interpolation operation, may further comprise the steps:
Step 1: obtain the mean value image between the every adjacent two-layer picture to N layer CT picture, obtain N-1 average image;
Step 2: N-1 average image carried out Gauss's smoothing processing, filter away high frequency noise;
Step 3: set up the interpolation reconstruction coefficient, obtain the interpolation reconstruction image.
Preferably, step 1 is specially: establishing ground floor source CT picture is I
1, second layer source CT picture is I
2... N layer source CT picture is I
N, the average image is: M
1=(I1+I2) * 0.5, M
2=(I2+I3) * 0.5 ... M
N-1=(IN-1+IN) * 0.5.
Preferably, step 2 a couple N-1 average image carries out 2-d gaussian filters respectively, and concrete what use is that a standard deviation is that 1.4 yardstick is 5 * 5 Gaussian convolution masterplate, and masterplate is kernel=[2, and 4,5,4,2; 4,9,12,9,4; 5,12,15,12,5; 4,9,12,9,4; 2,4,5,4,2], with this masterplate image is carried out convolution operation, may further comprise the steps:
Step 2.1: to M
1Carry out Gauss's 2-d gaussian filters, for certain pixel on the image (i, j), value after the convolution be G1 (i, j)=M1 (i-2, j-2) * 2+M1 (i-1, j-2) * 4+M1 (i, j-2) * 5+M1 (i+1, j-2) * 4+M1 (i+2, j-2) * 2+M1 (i-2, j-1) * 4+M1 (i-1, j-1) * 9+M1 (i, j-1) * 12+M1 (i+1, j-1) * 9+M1 (i+2, j-1) * 4+M1 (i-2, j) * 5+M1 (i-1, j) * 12+M1 (i, j) * 15+M1 (i+1, j) * 12+M1 (i+2, j) * 5+M1 (i-2, j+1) * 4+M1 (i-1, j+1) * 9+M1 (i, j+1) * 12+M1 (i+1, j+1) * 9+M1 (i+2, j+1) * 4+M1 (i-2, j+2) * 2+M1 (i-1, j+2) * 4+M1 (i, j+2) * 5+M1 (i+1, j+2) * 4+M1 (i+2, j+2) * 2; Average image after the convolution is: M
1'=G1/159;
Step 2.2: adopt the same method of step 2.1 respectively to M
2M
N-1Carry out Gauss's 2-d gaussian filters, obtain M
2' ... M
N-1'.
Preferably, step 3 may further comprise the steps:
Step 3.1: go the gaussian coefficient of standard to set up the coefficient of interpolation reconstruction, coefficient is that 1.6 yardstick is the one dimension Gauss smoothing factor of N-1 according to standard deviation, and concrete interpolation coefficient is p
1... ..p
N-1
Step 3.2: the N-1 that obtains according to step 2.2 opens the interpolation coefficient that average image and step 3.1 obtain and calculates interpolation image IG, IG=M
1' * p
1+ ... + M
N-1' * p
N-1
Compared with prior art, the present invention has following beneficial effect: interlayer data interpolating method provided by the invention utilizes the data interpolating of adjacent two layers to go out the middle layer data.Utilize the profile information of adjacent layer, can mate the location to bilevel interlayer data, positioned in alignment is finished, and carries out interpolation operation again, has avoided causing pseudo-shadow phenomenon because the interpolation that causes that do not line up of interlayer data is failed.And the present invention carries out making that transition is more level and smooth between each layer of CT image after the interpolation processing to the CT image, takes the transitions smooth between the later layer of interpolation and the layer, is convenient to analyzing and positioning, can effectively improve diagnosis accuracy.
Description of drawings
By reading the detailed description of non-limiting example being done with reference to the following drawings, it is more obvious that other features, objects and advantages of the present invention will become:
Fig. 1 is the method flow diagram of the interpolation method of CT image slices data of the present invention.
Embodiment
The present invention is described in detail below in conjunction with specific embodiment.Following examples will help those skilled in the art further to understand the present invention, but not limit the present invention in any form.Should be pointed out that to those skilled in the art, without departing from the inventive concept of the premise, can also make some distortion and improvement.These all belong to protection scope of the present invention.
See also Fig. 1, a kind of interpolation method of CT image slices data is supposed CT image one total N layer CT picture, and N layer CT picture is carried out interpolation operation, may further comprise the steps:
Step 1: obtain the mean value image between the every adjacent two-layer picture to N layer CT picture, obtain N-1 average image.
If ground floor source CT picture is I
1, second layer source CT picture is I
2... N layer source CT picture is I
N, the average image is: M
1=(I1+I2) * 0.5, M
2=(I2+I3) * 0.5 ... M
N-1=(IN-1+IN) * 0.5.
Step 2: N-1 average image carried out Gauss's smoothing processing, filter away high frequency noise.
This step is carried out 2-d gaussian filters respectively to N-1 average image, and concrete what use is that a standard deviation is that 1.4 yardstick is 5 * 5 Gaussian convolution masterplate, and masterplate is kernel=[2, and 4,5,4,2; 4,9,12,9,4; 5,12,15,12,5; 4,9,12,9,4; 2,4,5,4,2], with this masterplate image is carried out convolution operation, may further comprise the steps:
Step 2.1: to M
1Carry out Gauss's 2-d gaussian filters, for certain pixel on the image (i, j), value after the convolution be G1 (i, j)=M1 (i-2, j-2) * 2+M1 (i-1, j-2) * 4+M1 (i, j-2) * 5+M1 (i+1, j-2) * 4+M1 (i+2, j-2) * 2+M1 (i-2, j-1) * 4+M1 (i-1, j-1) * 9+M1 (i, j-1) * 12+M1 (i+1, j-1) * 9+M1 (i+2, j-1) * 4+M1 (i-2, j) * 5+M1 (i-1, j) * 12+M1 (i, j) * 15+M1 (i+1, j) * 12+M1 (i+2, j) * 5+M1 (i-2, j+1) * 4+M1 (i-1, j+1) * 9+M1 (i, j+1) * 12+M1 (i+1, j+1) * 9+M1 (i+2, j+1) * 4+M1 (i-2, j+2) * 2+M1 (i-1, j+2) * 4+M1 (i, j+2) * 5+M1 (i+1, j+2) * 4+M1 (i+2, j+2) * 2; Average image after the convolution is: M
1'=G1/159;
Step 2.2: adopt the same method of step 2.1 respectively to M
2M
N-1Carry out Gauss's 2-d gaussian filters, obtain M
2' ... M
N-1'.
Step 3: set up the interpolation reconstruction coefficient, obtain the interpolation reconstruction image.Specifically may further comprise the steps:
Step 3.1: go the gaussian coefficient of standard to set up the coefficient of interpolation reconstruction, coefficient is that 1.6 yardstick is the one dimension Gauss smoothing factor of N-1 according to standard deviation, and concrete interpolation coefficient is p
1... ..p
N-1
Step 3.2: the N-1 that obtains according to step 2.2 opens the interpolation coefficient that average image and step 3.1 obtain and calculates interpolation image IG, IG=M
1' * p
1+ ... + M
N-1' * p
N-1
More than specific embodiments of the invention are described.It will be appreciated that the present invention is not limited to above-mentioned specific implementations, those skilled in the art can make various distortion or modification within the scope of the claims, and this does not influence flesh and blood of the present invention.
Claims (4)
1. the interpolation method of CT image slices data is supposed CT image one total N layer CT picture, and N layer CT picture is carried out interpolation operation, it is characterized in that, may further comprise the steps:
Step 1: obtain the mean value image between the every adjacent two-layer picture to N layer CT picture, obtain N-1 average image;
Step 2: N-1 average image carried out Gauss's smoothing processing, filter away high frequency noise;
Step 3: set up the interpolation reconstruction coefficient, obtain the interpolation reconstruction image.
2. the interpolation method of CT image slices data according to claim 1, it is characterized in that step 1 is specially: establishing ground floor source CT picture is I
1, second layer source CT picture is I
2... N layer source CT picture is I
N, the average image is: M
1=(I1+I2) * 0.5, M
2=(I2+I3) * 0.5 ... M
N-1=(IN-1+IN) * 0.5.
3. the interpolation method of CT image slices data according to claim 2 is characterized in that, step 2 a couple N-1 average image carries out 2-d gaussian filters respectively, concrete what use is that a standard deviation is that 1.4 yardstick is 5 * 5 Gaussian convolution masterplate, and masterplate is kernel=[2,4,5,4,2; 4,9,12,9,4; 5,12,15,12,5; 4,9,12,9,4; 2,4,5,4,2], with this masterplate image is carried out convolution operation, may further comprise the steps:
Step 2.1: to M
1Carry out Gauss's 2-d gaussian filters, for certain pixel on the image (i, j), value after the convolution be G1 (i, j)=M1 (i-2, j-2) * 2+M1 (i-1, j-2) * 4+M1 (i, j-2) * 5+M1 (i+1, j-2) * 4+M1 (i+2, j-2) * 2+M1 (i-2, j-1) * 4+M1 (i-1, j-1) * 9+M1 (i, j-1) * 12+M1 (i+1, j-1) * 9+M1 (i+2, j-1) * 4+M1 (i-2, j) * 5+M1 (i-1, j) * 12+M1 (i, j) * 15+M1 (i+1, j) * 12+M1 (i+2, j) * 5+M1 (i-2, j+1) * 4+M1 (i-1, j+1) * 9+M1 (i, j+1) * 12+M1 (i+1, j+1) * 9+M1 (i+2, j+1) * 4+M1 (i-2, j+2) * 2+M1 (i-1, j+2) * 4+M1 (i, j+2) * 5+M1 (i+1, j+2) * 4+M1 (i+2, j+2) * 2; Average image after the convolution is: M
1'=G1/159;
Step 2.2: adopt the same method of step 2.1 respectively to M
2M
N-1Carry out Gauss's 2-d gaussian filters, obtain M
2' ... M
N-1'.
4. the interpolation method of CT image slices data according to claim 3 is characterized in that step 3 may further comprise the steps:
Step 3.1: go the gaussian coefficient of standard to set up the coefficient of interpolation reconstruction, coefficient is that 1.6 yardstick is the one dimension Gauss smoothing factor of N-1 according to standard deviation, and concrete interpolation coefficient is p
1... ..p
N-1
Step 3.2: the N-1 that obtains according to step 2.2 opens the interpolation coefficient that average image and step 3.1 obtain and calculates interpolation image IG, IG=M
1' * p
1+ ... + M
N-1' * p
N-1
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Inventor after: Hu Jie Inventor after: Wu Chunping Inventor after: Huang Haiqing Inventor after: Qi Jin Inventor after: Gu Chaochen Inventor after: Ma Jin Inventor after: Li Qin Inventor after: Peng Xun Inventor after: He Fei Inventor before: Hu Jie Inventor before: Huang Haiqing Inventor before: Qi Jin Inventor before: Gu Chaochen Inventor before: Ma Jin Inventor before: Li Qin Inventor before: Peng Xun Inventor before: He Fei |
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