CN105608702B - A kind of background suppression method and system of MRA brain image datas - Google Patents
A kind of background suppression method and system of MRA brain image datas Download PDFInfo
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Abstract
The present invention provides a kind of background suppression method and system of MRA brain image datas, method includes：Brightness of image normalized and picture smooth treatment are carried out to original MRA brain image datas, obtain the first processing MRA brain images；Profile threshold value is obtained, after carrying out thresholding processing to the first processing MRA brain images, extracts the outer profile chain code information of image；The highlight regions close to profile are obtained according to the outer profile chain code information selected seed point of image, and by region-growing method；Image template is established according to the information of highlight regions, image template corresponding data in original MRA brain image datas is removed, and carry out MIP reconstructions, obtains blood-vessel image.The present invention automatically removes the peripheral scalp information in TOF-MRA brain images, inhibit the ambient noise of MIP handling results, achieve the effect that improve vascular detail structure, prominent blood vessel structure form, reduce shutter noise, and has obtained clearly blood-vessel image.
Description
Technical field
The present invention relates to medical magnetic resonance Angiography field more particularly to a kind of backgrounds of MRA brain image datas
Suppressing method and system.
Background technology
Magnetic Resonance Angiography (Magnetic Resonance Angiography MRA) is to be based on magnetic resonance imaging skill
One group of Angiography of art, it is a kind of non-invasive, radiationless harm, without relying on contrast agent, safe and reliable blood
Pipe imaging technique plays important booster action in the diagnosis of the vascular diseases such as the cerebrovascular, neck blood vessel, angiocarpy.According to
Image-forming principle obtains mode there are mainly three types of the methods of MRA data：Time leaps method (Time of Flight MRA, TOF-
MRA), Phase contrast (Phase Contrast Angiography, PCA), Contrast enhanced method (Contrast Enhanced
MRA, CE-MRA) etc., it is relatively broad with TOF method clinical applications at present.
The cerebrovascular system of the mankind has complicated anatomical structure, by carrying out maximum intensity projection to MRA data
（Maximum Intensity Projection, MIP）Three-dimensional reconstruction processing, can be relatively easily independent from complicated brain
Blood vessel is extracted, the information such as cerebrovascular position, shape and topological structure are obtained.But MRA data SNRs are relatively low, and TOF numbers
Still there is signal according to middle static tissue, cause in image other than vessel information, there is also the back ofs the body of a large amount of brain tissues and its hetero-organization
Scape information.Simultaneously as 3D-TOF MRA use volume imagery, shutter noise is easy tod produce, influences blood vessel imaging effect, very
To the rate of correct diagnosis that may be decreased doctor.MIP three-dimensional reconstruction algorithms are projected to MRA data with some projection angle,
Take the pixel value of the image as a result of brightness maxima on every ray.Since MIP only takes maximum brightness to show, it is easy to cause blood
Pipe signal is mutually covered or is covered by other high-brightness signals.In consideration of it, there is an urgent need for explore a kind of to improve detailed structure, prominent blood
The background suppression method of pipe structural form, preferably to assist diagnosis.
TOF-MRA image datas are that T1 weights picture, in TOF-MRA images other than blood vessel is high RST, static tissue
Also still there are signal, especially scalp portions that there is stronger signal.Scalp is covered in entire cranium brain surrounding, be easy to cause by
After MIP processing, the led signal that scalp signal becomes in the background of MIP result images possibly even covers blood vessel signal sometimes.
Therefore, the prior art could be improved and develop.
Invention content
Place in view of above-mentioned deficiencies of the prior art, the purpose of the present invention is to provide a kind of back ofs the body of MRA brain image datas
Scape suppressing method and system, it is intended to when solving to carry out three-dimensional reconstruction processing to MRA data in the prior art, be easy to cause blood vessel letter
It number mutually covers or the problem of covered by other high-brightness signals, can not accurately extract blood vessel signal.
In order to achieve the above object, this invention takes following technical schemes：
A kind of background suppression method of MRA brain image datas, wherein the described method comprises the following steps：
A, brightness of image normalized and picture smooth treatment are carried out to original MRA brain image datas, obtained at first
Manage MRA brain images；
B, profile threshold value is obtained, after carrying out thresholding processing to the first processing MRA brain images, extracts the outer profile chain of image
Code information；
C, according to the outer profile chain code information selected seed point of image, and the height close to profile is obtained by region-growing method
Bright area；
D, image template is established according to the information of highlight regions, image template in original MRA brain image datas is corresponded into number
According to removal, and MIP reconstructions are carried out, obtains blood-vessel image.
The background suppression method of the MRA brain image datas, wherein the step A is specifically included：
A1, the maximum brightness value for obtaining original MRA brain image datas, withAs original MRA brains
The normalization factor of each slice image data in image data, normalizes each slice image data, obtains normalization MRA brain images
Data；Wherein,, N is the image sheet number of plies；
A2, it is smoothed according to cvSmooth function pairs normalization MRA brain image datas, obtains the first processing MRA
Brain image.
The background suppression method of the MRA brain image datas, wherein the step B is specifically included：
B1, it calculates and chooses profile threshold value, to the first processing MRA brain images into row threshold division, obtain the two-value of MRA data
Change outline data；
B2, contours extract is carried out to binaryzation outline data, obtains outer profile chain code information.
The background suppression method of the MRA brain image datas, wherein the step C is specifically included：
C1, horizontal grouping and vertical grouping, right boundary of the acquisition profile in each row are carried out to the profile chain code information
Point and each row up-and-down boundary point；
C2, to take a brightness of the left margin point in horizontal grouping line by line be more than the point of luminance threshold as seed
Point obtains the high-brightness region close to profile left margin, until obtaining all close profile left margins with region growth method
Left high-brightness region RL；
C3, to take a brightness of the right margin point in horizontal grouping line by line be more than the point of luminance threshold as seed
Point obtains the high-brightness region close to profile right margin, until obtaining all close profile right margins with region growth method
Right high-brightness region RR；
C4, to take a brightness of the coboundary point in horizontal grouping line by line be more than the point of luminance threshold as seed
Point obtains the high-brightness region close to profile coboundary with region growth method, until obtaining all close profile coboundaries
Upper high-brightness region RT；
C5, to take a brightness of the lower boundary point in horizontal grouping line by line be more than the point of luminance threshold as seed
Point obtains the high-brightness region close to profile lower boundary, until obtaining all close profile lower boundaries with region growth method
Lower high-brightness region RB.
The background suppression method of the MRA brain image datas, wherein the step D is specifically included：
D1, according to the left high-brightness region RL of acquisition, right high-brightness region RR, upper high-brightness region RT and lower high luminance area
Domain RB creates an image masterplate T；Wherein, T=RLRR RTRB；
D2, it will be scratched and removed with image template T corresponding datas in original MRA brain image datas according to image masterplate T, obtain second
Handle MRA brain images.
D3, MIP three-dimensional reconstruction processing is carried out to second processing MRA brain images, obtains blood-vessel image.
A kind of background suppression system of MRA brain image datas, wherein including：
Preprocessing module, for being carried out at brightness of image normalized and image smoothing to original MRA brain image datas
Reason, obtains the first processing MRA brain images；
Profile detection module after carrying out thresholding processing to the first processing MRA brain images, is carried for obtaining profile threshold value
Take the outer profile chain code information of image；
Peripheral high RST checks module, for the outer profile chain code information selected seed point according to image, and passes through region
Growth method obtains the highlight regions close to profile；
Module is rebuild, for establishing image template according to the information of highlight regions, by image in original MRA brain image datas
Template corresponding data removes, and carries out MIP reconstructions, obtains blood-vessel image.
The background suppression method of the MRA brain image datas, wherein the preprocessing module specifically includes：
Normalization unit, the maximum brightness value for obtaining original MRA brain image datas, withIt does
For the normalization factor of each slice image data in original MRA brain image datas, each slice image data are normalized, normalizing is obtained
Change MRA brain image datas；Wherein,, N is the image sheet number of plies；
Smoothing processing unit is smoothed for normalizing MRA brain image datas according to cvSmooth function pairs, obtains
To the first processing MRA brain images.
The background suppression system of the MRA brain image datas, wherein the profile detection module specifically includes：
Binary conversion treatment unit chooses profile threshold value for calculating, to the first processing MRA brain images into row threshold division,
Obtain the binaryzation outline data of MRA data；
Chain code information extraction unit obtains outer profile chain code information for carrying out contours extract to binaryzation outline data.
The background suppression system of the MRA brain image datas, wherein the periphery high RST checks that module specifically includes：
Boundary point acquiring unit obtains profile for carrying out horizontal grouping and vertical grouping to the profile chain code information
Left and right boundary point in each row and the up-and-down boundary point in each row；
Left high-brightness region acquiring unit takes a brightness of the left margin point in horizontal grouping super for choosing line by line
The point of luminance threshold is crossed as seed point, the high-brightness region close to profile left margin is obtained with region growth method, until obtaining
Obtain the left high-brightness region RL of all close profile left margins；
Right high-brightness region acquiring unit takes a brightness of the right margin point in horizontal grouping super for choosing line by line
The point of luminance threshold is crossed as seed point, the high-brightness region close to profile right margin is obtained with region growth method, until obtaining
Obtain the right high-brightness region RR of all close profile right margins；
Upper high-brightness region acquiring unit takes a brightness of the coboundary point in horizontal grouping super for choosing line by line
The point of luminance threshold is crossed as seed point, the high-brightness region close to profile coboundary is obtained with region growth method, until obtaining
Obtain the upper high-brightness region RT of all close profile coboundaries；
Lower high-brightness region acquiring unit takes a brightness of the lower boundary point in horizontal grouping super for choosing line by line
The point of luminance threshold is crossed as seed point, the high-brightness region close to profile lower boundary is obtained with region growth method, until obtaining
Obtain the lower high-brightness region RB of all close profile lower boundaries.
The background suppression system of the MRA brain image datas, wherein the reconstruction module specifically includes：
Template acquiring unit, for the left high-brightness region RL, right high-brightness region RR, upper high-brightness region according to acquisition
RT and lower high-brightness region RB creates an image masterplate T；Wherein, T=RLRRRTRB；
It scratches and removes unit, for will be scratched with image template T corresponding datas in original MRA brain image datas according to image masterplate T
It removes, obtains second processing MRA brain images.
MIP processing units obtain blood-vessel image for carrying out MIP three-dimensional reconstruction processing to second processing MRA brain images.
The background suppression method and system of MRA brain image datas of the present invention, method include：To original MRA mind maps
As data progress brightness of image normalized and picture smooth treatment, the first processing MRA brain images are obtained；Obtain profile threshold
Value extracts the outer profile chain code information of image after carrying out thresholding processing to the first processing MRA brain images；According to the outer of image
Profile chain code information selected seed point, and the highlight regions close to profile are obtained by region-growing method；According to highlight regions
Information establishes image template, image template corresponding data in original MRA brain image datas is removed, and carry out MIP reconstructions, is obtained
Blood-vessel image.The present invention automatically removes the peripheral scalp information in TOF-MRA brain images, it is suppressed that MIP handling results
Ambient noise, achieve the effect that improve vascular detail structure, prominent blood vessel structure form, reduce shutter noise, and obtain
Clearly blood-vessel image.
Description of the drawings
Fig. 1 is the flow chart of the background suppression method preferred embodiment of MRA brain image datas of the present invention.
Fig. 2 is TOF-MRA brain images.
Fig. 3 is the outline drawing of TOF-MRA brain images.
Fig. 4 is the peripheral scalp highlight regions schematic diagram close to profile.
Fig. 5 a are not carry out background to inhibit MIP handling result schematic diagrames.
Fig. 5 b are to carry out background to inhibit MIP handling result schematic diagrames.
Fig. 6 is the functional block diagram of the background suppression system preferred embodiment of MRA brain image datas of the present invention.
Specific implementation mode
The present invention provides a kind of background suppression method and system of MRA brain image datas, to make the purpose of the present invention, technology
Scheme and effect are clearer, clear, and the present invention is described in more detail for the embodiment that develops simultaneously referring to the drawings.It should manage
Solution, described herein specific examples are only used to explain the present invention, is not intended to limit the present invention.
Fig. 1 is referred to, is the flow of the background suppression method preferred embodiment of MRA brain image datas of the present invention
Figure.As shown in Figure 1, the background suppression method of the MRA brain image datas includes：
Step S100, brightness of image normalized and picture smooth treatment are carried out to original MRA brain image datas, obtained
First processing MRA brain images；
Step S200, profile threshold value is obtained, after carrying out thresholding processing to the first processing MRA brain images, extracts image
Outer profile chain code information；
Step S300, it according to the outer profile chain code information selected seed point of image, and is obtained by region-growing method close
The highlight regions of profile；
Step S400, image template is established according to the information of highlight regions, by image template in original MRA brain image datas
Corresponding data removes, and carries out MIP reconstructions, obtains blood-vessel image.
In the present invention, the peripheral scalp information in TOF-MRA brain images as shown in Figure 2 is automatically removed, is inhibited
Background information in MIP three-dimensional reconstruction handling results, acquisition are more clear accurate blood-vessel image.
Preferably, being specifically included in the step S100：
Step S101, the maximum brightness value of original MRA brain image datas is obtained, withAs original
The normalization factor of each slice image data in MRA brain image datas, normalizes each slice image data, obtains normalization MRA
Brain image data；Wherein,, N is the image sheet number of plies.
Step S102, MRA brain image datas are normalized according to cvSmooth function pairs to be smoothed, is obtained at first
Manage MRA brain images.
Specifically, the cvSmooth function pairs in the libraries OpenCV is used to normalize MRA brain image datas in step s 102
It is smoothed.
Brightness of image normalized makes the brightness of image data more balanced, avoids since partial plies image is excessively bright
Or secretly influence processing result image excessively.Moreover, doing smoothing processing to image, influence of noise is effectively reduced, enhancing edge connects
Continuous property.
Preferably, being specifically included in the step S200：
Step S201, it calculates and chooses profile threshold value, to the first processing MRA brain images into row threshold division, obtain MRA data
Binaryzation outline data；
Step S202, contours extract is carried out to binaryzation outline data, obtains outer profile chain code information.
More specifically, the fritter area for choosing four angles that profile threshold value constantly first takes image data is calculated in step s 201
Domain calculates separately the maximum brightness value of four pockets,, takeLocal maxima brightness value
Profile threshold value as image；
Profile threshold value is utilized in step s 201To through step S100 treated first processing MRA brain images（I.e.
TOF-MRA brain images）Into row threshold division, if the current pixel point gray value of image>It is then 1, is otherwise 0, obtains
The binaryzation outline data of MRA data；
With in the libraries OpenCV when in step S202 to binaryzation outline data progress contours extract
CvFindContours functions obtain outer profile chain code information（That is the profile information of TOF-MRA brain images）, such as Fig. 3 institutes
Show.
Preferably, being specifically included in the step S300：
Step S301, horizontal grouping and vertical grouping are carried out to the profile chain code information, obtains profile on a left side for each row
Right margin point and up-and-down boundary point in each row；
Step S302, it is more than that the point of luminance threshold is made to take a brightness of the left margin point in horizontal grouping line by line
For seed point, the high-brightness region close to profile left margin is obtained with region growth method, until it is left to obtain all close profiles
The left high-brightness region RL on boundary；
Step S303, it is more than that the point of luminance threshold is made to take a brightness of the right margin point in horizontal grouping line by line
For seed point, the high-brightness region close to profile right margin is obtained with region growth method, until it is right to obtain all close profiles
The right high-brightness region RR on boundary；
Step S304, it is more than that the point of luminance threshold is made to take a brightness of the coboundary point in horizontal grouping line by line
For seed point, the high-brightness region close to profile coboundary is obtained with region growth method, until obtaining on all close profiles
The upper high-brightness region RT on boundary；
Step S305, it is more than that the point of luminance threshold is made to take a brightness of the lower boundary point in horizontal grouping line by line
For seed point, the high-brightness region close to profile lower boundary is obtained with region growth method, until obtaining under all close profiles
The lower high-brightness region RB on boundary.
Specifically, in step S302- steps S305, one closer horizontal point is chosen in certain row (such as center row)
It is left in group（Right, up or down）One high brightness point on boundary obtains left close to profile as seed point with region growth method
（Right, up or down）The high-brightness region on boundary, if the region meets all the points all close to left（Right, up or down）Boundary, i.e., away from
From D<Td(wherein：, TdFor specified threshold), then it is assumed that the region is peripheral scalp highlight regions, marks the area
Domain.
Preferably, being specifically included in the step S400：
Step S401, according to the left high-brightness region RL of acquisition, right high-brightness region RR, upper high-brightness region RT and lower height
Luminance area RB creates an image masterplate T；Wherein, T=RLRRRTRB；
Step S402, it will be scratched and removed with image template T corresponding datas in original MRA brain image datas according to image masterplate T, obtained
To second processing MRA brain images.
Step S403, MIP three-dimensional reconstruction processing is carried out to second processing MRA brain images, obtains blood-vessel image.
The image masterplate T created in step S401（I.e. close to the peripheral scalp highlight regions of profile）As shown in Figure 4.Root
The corresponding data of image template T in original MRA brain image datas are scratched according to image template T and are removed, that is, finding image template T intermediate values is
1 position sets to 0 its pixel intensity, obtains second processing MRA brain images；MIP tri- is carried out to second processing MRA brain images again
Reconstruction processing is tieed up, blood-vessel image is obtained, adjustment perspective view carries out MIP processing, can obtain the blood-vessel image in each orientation, such as
Shown in Fig. 5 b；Comparison be not used this method carry out comparison inhibition as a result, as shown in Figure 5 a, improvement with obvious effects.
As it can be seen that the present invention automatically removes the peripheral scalp information in TOF-MRA brain images, it is suppressed that MIP processing
As a result ambient noise achievees the effect that improve vascular detail structure, prominent blood vessel structure form, reduces shutter noise, and
Clearly blood-vessel image is obtained.
Based on above method embodiment, the present invention also provides a kind of background suppression systems of MRA brain image datas.Such as figure
Shown in 6, the background suppression system of the MRA brain image datas includes：
Preprocessing module 100, for carrying out brightness of image normalized and image smoothing to original MRA brain image datas
Processing, obtains the first processing MRA brain images；
Profile detection module 200, for obtaining profile threshold value, after carrying out thresholding processing to the first processing MRA brain images,
Extract the outer profile chain code information of image；
Peripheral high RST checks module 300, for the outer profile chain code information selected seed point according to image, and passes through area
Domain growth method obtains the highlight regions close to profile；
Rebuilding module 400 will be in original MRA brain image data for establishing image template according to the information of highlight regions
Image template corresponding data removes, and carries out MIP reconstructions, obtains blood-vessel image.
Further, in the background suppression system of the MRA brain image datas, the preprocessing module 100 is specifically wrapped
It includes：
Normalization unit, the maximum brightness value for obtaining original MRA brain image datas, withIt does
For the normalization factor of each slice image data in original MRA brain image datas, each slice image data are normalized, normalizing is obtained
Change MRA brain image datas；Wherein,, N is the image sheet number of plies；
Smoothing processing unit is smoothed for normalizing MRA brain image datas according to cvSmooth function pairs, obtains
To the first processing MRA brain images.
Further, in the background suppression system of the MRA brain image datas, the profile detection module 200 is specific
Including：
Binary conversion treatment unit chooses profile threshold value for calculating, to the first processing MRA brain images into row threshold division,
Obtain the binaryzation outline data of MRA data；
Chain code information extraction unit obtains outer profile chain code information for carrying out contours extract to binaryzation outline data.
Further, in the background suppression system of the MRA brain image datas, the periphery high RST checks module
300 specifically include：
Boundary point acquiring unit obtains profile for carrying out horizontal grouping and vertical grouping to the profile chain code information
Left and right boundary point in each row and the up-and-down boundary point in each row；
Left high-brightness region acquiring unit takes a brightness of the left margin point in horizontal grouping super for choosing line by line
The point of luminance threshold is crossed as seed point, the high-brightness region close to profile left margin is obtained with region growth method, until obtaining
Obtain the left high-brightness region RL of all close profile left margins；
Right high-brightness region acquiring unit takes a brightness of the right margin point in horizontal grouping super for choosing line by line
The point of luminance threshold is crossed as seed point, the high-brightness region close to profile right margin is obtained with region growth method, until obtaining
Obtain the right high-brightness region RR of all close profile right margins；
Upper high-brightness region acquiring unit takes a brightness of the coboundary point in horizontal grouping super for choosing line by line
The point of luminance threshold is crossed as seed point, the high-brightness region close to profile coboundary is obtained with region growth method, until obtaining
Obtain the upper high-brightness region RT of all close profile coboundaries；
Lower high-brightness region acquiring unit takes a brightness of the lower boundary point in horizontal grouping super for choosing line by line
The point of luminance threshold is crossed as seed point, the high-brightness region close to profile lower boundary is obtained with region growth method, until obtaining
Obtain the lower high-brightness region RB of all close profile lower boundaries.
Further, in the background suppression system of the MRA brain image datas, the reconstruction module 400 specifically includes：
Template acquiring unit, for the left high-brightness region RL, right high-brightness region RR, upper high-brightness region according to acquisition
RT and lower high-brightness region RB creates an image masterplate T；Wherein, T=RLRRRTRB；
It scratches and removes unit, for will be scratched with image template T corresponding datas in original MRA brain image datas according to image masterplate T
It removes, obtains second processing MRA brain images.
MIP processing units obtain blood-vessel image for carrying out MIP three-dimensional reconstruction processing to second processing MRA brain images.
In conclusion the present invention provides a kind of background suppression method and system of MRA brain image datas, method includes：
Brightness of image normalized and picture smooth treatment are carried out to original MRA brain image datas, obtain the first processing MRA mind maps
Picture；Profile threshold value is obtained, after carrying out thresholding processing to the first processing MRA brain images, extracts the outer profile chain code information of image；
The highlight regions close to profile are obtained according to the outer profile chain code information selected seed point of image, and by region-growing method；Root
Image template is established according to the information of highlight regions, image template corresponding data in original MRA brain image datas is removed, and carries out
MIP is rebuild, and obtains blood-vessel image.The present invention automatically removes the peripheral scalp information in TOF-MRA brain images, inhibits
The ambient noises of MIP handling results, reaching improves vascular detail structure, prominent blood vessel structure form, reduces shutter noise
Effect, and obtained clearly blood-vessel image.
It, can according to the technique and scheme of the present invention and this hair it is understood that for those of ordinary skills
Bright design is subject to equivalent substitution or change, and all these changes or replacement should all belong to the guarantor of appended claims of the invention
Protect range.
Claims (4)
1. a kind of background suppression method of MRA brain image datas, which is characterized in that the described method comprises the following steps：
A, brightness of image normalized and picture smooth treatment are carried out to original MRA brain image datas, obtains the first processing MRA
Brain image；
B, profile threshold value is obtained, after carrying out thresholding processing to the first processing MRA brain images, extracts the outer profile chain code letter of image
Breath；
C, according to the outer profile chain code information selected seed point of image, and the highlight bar close to profile is obtained by region-growing method
Domain；
D, image template is established according to the information of highlight regions, image template corresponding data in original MRA brain image datas is gone
It removes, and carries out MIP reconstructions, obtain blood-vessel image；
The step A is specifically included：
A1, the maximum brightness value MG for obtaining original MRA brain image datasi, withAs original MRA brain images number
The normalization factor of each slice image data in, normalizes each slice image data, obtains normalization MRA brain image datas；
Wherein, i=0,1 ..., N-1, N are the image sheet number of plies；
A2, it is smoothed according to cvSmooth function pairs normalization MRA brain image datas, obtains the first processing MRA mind maps
Picture；
The step B is specifically included：
B1, it calculates and chooses profile threshold value, to the first processing MRA brain images into row threshold division, obtain the binaryzation wheel of MRA data
Wide data；
B2, contours extract is carried out to binaryzation outline data, obtains outer profile chain code information；
The pocket that four angles of image data are first taken when calculating selection profile threshold value, calculates separately four pockets most
Big brightness value RGi, i=0,1,2,3, take Min (RGi) profile threshold value T of the local maxima brightness value as imagec；
Utilize profile threshold value TcTo treated first processing MRA brain images into row threshold division, if the current pixel point of image
Gray value>TcIt is then 1, is otherwise 0, obtains the binaryzation outline data of MRA data；
It is obtained with the cvFindContours functions in the libraries OpenCV when carrying out contours extract to binaryzation outline data
Outer profile chain code information；
The step C is specifically included：
C1, horizontal grouping and vertical grouping are carried out to the profile chain code information, obtain profile in the left and right boundary point of each row and
In the up-and-down boundary point of each row；
C2, a brightness for choosing the left margin point in horizontal grouping line by line are more than the point of luminance threshold as seed point, fortune
The high-brightness region close to profile left margin is obtained with region growth method, until the left side for obtaining all close profile left margins is highlighted
Spend region RL；
C3, a brightness for choosing the right margin point in horizontal grouping line by line are more than the point of luminance threshold as seed point, fortune
The high-brightness region close to profile right margin is obtained with region growth method, until the right side for obtaining all close profile right margins is highlighted
Spend region RR；
C4, a brightness for choosing the coboundary point in horizontal grouping line by line are more than the point of luminance threshold as seed point, fortune
The high-brightness region close to profile coboundary is obtained with region growth method, until obtaining the upper highlighted of all close profile coboundaries
Spend region RT；
C5, a brightness for choosing the lower boundary point in horizontal grouping line by line are more than the point of luminance threshold as seed point, fortune
The high-brightness region close to profile lower boundary is obtained with region growth method, until obtaining the lower highlighted of all close profile lower boundaries
Spend region RB；
A high brightness point close to horizontal grouping middle left and right, up or down boundary is chosen in center row as seed point,
Left and right, up or down boundary the high-brightness region close to profile is obtained with region growth method, if the region meets all the points all
Close to left and right, up or down boundary, i.e. distance D<Td, wherein：Td is specified threshold, then it is assumed that the region is that peripheral scalp is highlighted
Region marks the region.
2. the background suppression method of MRA brain image datas according to claim 1, which is characterized in that the step D is specifically wrapped
It includes：
D1, according to the left high-brightness region RL of acquisition, right high-brightness region RR, upper high-brightness region RT and lower high-brightness region RB
Create an image masterplate T；Wherein, T=RL ∪ RR ∪ RT ∪ RB；
D2, it will be scratched and removed with image template T corresponding datas in original MRA brain image datas according to image masterplate T, obtain second processing
MRA brain images；
D3, MIP three-dimensional reconstruction processing is carried out to second processing MRA brain images, obtains blood-vessel image.
3. a kind of background suppression system of MRA brain image datas, which is characterized in that including：
Preprocessing module is obtained for carrying out brightness of image normalized and picture smooth treatment to original MRA brain image datas
To the first processing MRA brain images；
Profile detection module, for obtaining profile threshold value, after carrying out thresholding processing to the first processing MRA brain images, extraction figure
The outer profile chain code information of picture；
Peripheral high RST checks module, for the outer profile chain code information selected seed point according to image, and passes through region growing
Method obtains the highlight regions close to profile；
Module is rebuild, for establishing image template according to the information of highlight regions, by image template in original MRA brain image datas
Corresponding data removes, and carries out MIP reconstructions, obtains blood-vessel image；
The preprocessing module specifically includes：
Normalization unit, the maximum brightness value MG for obtaining original MRA brain image datasi, withAs original
The normalization factor of each slice image data in MRA brain image datas, normalizes each slice image data, obtains normalization MRA
Brain image data；Wherein, i=0,1 ..., N-1, N are the image sheet number of plies；
Smoothing processing unit is smoothed for normalizing MRA brain image datas according to cvSmooth function pairs, obtains the
One processing MRA brain images；
The profile detection module specifically includes：
Binary conversion treatment unit chooses profile threshold value for calculating, and to the first processing MRA brain images into row threshold division, obtains
The binaryzation outline data of MRA data；
Chain code information extraction unit obtains outer profile chain code information for carrying out contours extract to binaryzation outline data；
The pocket that four angles of image data are first taken when calculating selection profile threshold value, calculates separately four pockets most
Big brightness value RGi, i=0,1,2,3, take Min (RGi) profile threshold value T of the local maxima brightness value as imagec；
Utilize profile threshold value TcTo treated first processing MRA brain images into row threshold division, if the current pixel point of image
Gray value>TcIt is then 1, is otherwise 0, obtains the binaryzation outline data of MRA data；
It is obtained with the cvFindContours functions in the libraries OpenCV when carrying out contours extract to binaryzation outline data
Outer profile chain code information；
The periphery high RST checks that module specifically includes：
Boundary point acquiring unit obtains profile each for carrying out horizontal grouping and vertical grouping to the profile chain code information
Capable left and right boundary point and the up-and-down boundary point in each row；
Left high-brightness region acquiring unit, a brightness for choosing the left margin point in horizontal grouping line by line are more than brightness
The point of threshold value obtains the high-brightness region close to profile left margin as seed point, with region growth method, until being owned
Close to the left high-brightness region RL of profile left margin；
Right high-brightness region acquiring unit, a brightness for choosing the right margin point in horizontal grouping line by line are more than brightness
The point of threshold value obtains the high-brightness region close to profile right margin as seed point, with region growth method, until being owned
Close to the right high-brightness region RR of profile right margin；
Upper high-brightness region acquiring unit, a brightness for choosing the coboundary point in horizontal grouping line by line are more than brightness
The point of threshold value obtains the high-brightness region close to profile coboundary as seed point, with region growth method, until being owned
Upper high-brightness region RT close to profile coboundary；
Lower high-brightness region acquiring unit, a brightness for choosing the lower boundary point in horizontal grouping line by line are more than brightness
The point of threshold value obtains the high-brightness region close to profile lower boundary as seed point, with region growth method, until being owned
Close to the lower high-brightness region RB of profile lower boundary；
A high brightness point close to horizontal grouping middle left and right, up or down boundary is chosen in center row as seed point,
Left and right, up or down boundary the high-brightness region close to profile is obtained with region growth method, if the region meets all the points all
Close to left and right, up or down boundary, i.e. distance D<Td, wherein Td are specified threshold, then it is assumed that the region is peripheral scalp highlight bar
Domain marks the region.
4. the background suppression system of MRA brain image datas according to claim 3, which is characterized in that the reconstruction module tool
Body includes：
Template acquiring unit, for according to the left high-brightness region RL of acquisition, right high-brightness region RR, upper high-brightness region RT and
Lower high-brightness region RB creates an image masterplate T；Wherein, T=RL ∪ RR ∪ RT ∪ RB；
It scratches and removes unit, removing, obtaining for will be scratched with image template T corresponding datas in original MRA brain image datas according to image masterplate T
To second processing MRA brain images；
MIP processing units obtain blood-vessel image for carrying out MIP three-dimensional reconstruction processing to second processing MRA brain images.
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