CN112508878A - Cerebrovascular image registration and fusion method - Google Patents
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Abstract
The invention relates to a cerebrovascular image registration and fusion method, which comprises the following steps: acquiring a bright blood image and a black blood image of a cerebral blood vessel; taking the black blood image as a reference image and the bright blood image as a floating image, carrying out coordinate transformation on the bright blood image, and simultaneously carrying out interpolation processing on the bright blood image by adopting a bilinear interpolation method; calculating the similarity of the bright blood image and the black blood image after difference processing by utilizing similarity measurement; finding the optimal similarity measurement by utilizing a search strategy, and stopping iteration when the similarity measurement reaches the optimal value; performing coordinate conversion on the bright blood image when the similarity measurement reaches the optimum value according to the spatial transformation matrix to obtain a first registration image; extracting the same scanning area in the black blood image according to the scanning area of the bright blood image in the first registration image to obtain a second registration image; and performing image fusion on the two images in the second registration image by adopting a pyramid decomposition image fusion algorithm. The method of the invention can assist doctors in accurate intracranial disease diagnosis.
Description
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to a cerebrovascular image registration and fusion method.
Background
With the development of medical imaging technology, various acquisition devices and imaging modes of medical images are emerging continuously, so that the requirement for effectively combining medical images from different medical devices or obtained in different acquisition modes of the same medical device is brought, and the development of medical image registration and fusion technology is promoted.
Especially, the intracranial artery blood vessel comes from carotid artery and vertebral artery, is anastomosed into Willis ring at the bottom of brain, has special structural form and zigzag, and the wall of the artery is extremely thin and is similar to veins with the same thickness at other parts outside the cranium; for the evaluation of the degree of intracranial vascular lesion with complex structure and the degree of vascular stenosis, magnetic resonance vascular imaging technology is generally adopted. By means of the magnetic resonance blood vessel imaging technology, the path of the intracranial artery blood vessel can be clearly described. According to the brightness degree of blood in a magnetic resonance image, a scanning sequence can be divided into a bright blood sequence and a black blood sequence, but the scanning directions of the black blood sequence and the bright blood sequence are different, so that the final magnetic resonance imaging layers are different. Therefore, when the doctor observes the two images, the doctor needs to perform necessary spatial imagination for understanding, which undoubtedly increases the processing difficulty and processing time, and is not beneficial for the doctor to easily and quickly obtain the comprehensive information required by diagnosis.
Therefore, for intracranial vascular images, an image registration and fusion method is urgently needed to facilitate more accurate diagnosis of intracranial diseases for doctors.
Disclosure of Invention
In order to solve the above problems in the prior art, the present invention provides a method for registration and fusion of cerebrovascular images. The technical problem to be solved by the invention is realized by the following technical scheme:
the embodiment of the invention provides a method for registering and fusing cerebrovascular images, which comprises the following steps:
acquiring a bright blood image and a black blood image of a cerebral blood vessel;
taking the black blood image as a reference image and the bright blood image as a floating image, performing coordinate transformation on the bright blood image, and performing interpolation processing on the bright blood image by adopting a bilinear interpolation method;
calculating the similarity between the bright blood image and the black blood image after difference processing by utilizing similarity measurement;
finding the optimal similarity measurement by utilizing a search strategy;
performing coordinate conversion on the bright blood image when the similarity measurement reaches the optimum value according to the spatial transformation matrix to obtain a first registration image; the first registered image comprises the black blood image and the bright blood image after coordinate transformation;
extracting the same scanning area in the black blood image according to the scanning area of the bright blood image in the first registration image to obtain a second registration image; the second registration image comprises a bright blood image after coordinate transformation and an extracted black blood image having the same scanning area as the bright blood image.
In one embodiment of the invention, the black blood image is an enhanced black blood image using a contrast agent.
In an embodiment of the present invention, the performing coordinate transformation on the bright blood image and performing interpolation processing on the bright blood image by using a bilinear interpolation method while using the black blood image as a reference image and the bright blood image as a floating image includes:
acquiring DICOM orientation label information of the bright blood image and the black blood image;
according to the DICOM orientation label information, taking the black blood image coordinate system as a standard coordinate system, and carrying out coordinate transformation on the bright blood image coordinate system to the standard coordinate system;
and simultaneously, carrying out interpolation processing on the bright blood image by adopting a bilinear interpolation method.
In an embodiment of the present invention, the bilinear interpolation method obtains a pixel value interpolated by the current coordinate by calculating a weighted pixel average of 4 coordinate points closest to the current coordinate and assigning the weighted pixel average to the current coordinate point. In one embodiment of the invention, the similarity measure is measured using a root mean square error.
In one embodiment of the invention, the search strategy employs a (1+1) -ES evolution strategy.
In an embodiment of the present invention, the same scanning area in the black blood image is extracted according to the scanning area of the bright blood image in the first registration image, so as to obtain a second registration image; the second registration image includes a bright blood image after coordinate transformation, and an extracted black blood image having the same scanning area as the bright blood image, and includes:
inputting the bright blood image and the black blood image;
using a Sobel edge detection method for the bright blood image to obtain edge contour information of cerebral vessels in the bright blood image;
respectively extracting a minimum abscissa value, a maximum abscissa value, a minimum ordinate value and a maximum ordinate value in the edge profile information as initial extraction frames;
expanding the initial extraction frame outwards within the size of the size boundary of the bright blood image to serve as a final extraction frame;
and performing image region-of-interest extraction on the black blood image by using the final extraction frame to obtain the second registration image.
In an embodiment of the invention, the outward expansion range of the initial extraction frame is 10-30 pixels.
In an embodiment of the present invention, the performing image fusion on two images in the second registration image by using a pyramid decomposition image fusion algorithm includes:
and analyzing and fusing two modal images on different spatial frequency bands by using a multi-scale analysis method and carrying out pyramid decomposition on two images in the second registration image with a fixed scale.
In an embodiment of the present invention, the performing image fusion on two images in the second registration image by using a pyramid decomposition image fusion algorithm includes:
decomposing two images in the second registration image into different spatial frequency bands, and respectively storing the two images into a bright blood image decomposition subset and a black blood image decomposition subset;
performing image fusion according to a corresponding high-frequency information fusion rule and a corresponding low-frequency information fusion rule respectively aiming at the bright blood image decomposition subset and the black blood image decomposition subset of each decomposition layer to obtain a cerebral vessel fusion image subset;
and carrying out pyramid inverse decomposition on the cerebrovascular fusion image subset to obtain a final fusion result.
The invention has the beneficial effects that: the invention selects a proper method for the bright blood image and the black blood image of the cerebral vessels to carry out primary registration, extracts a common interested region after the primary registration to carry out secondary registration, and then fuses the two images into one image by a pyramid decomposition image fusion algorithm.
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Drawings
Fig. 1 is a flowchart of a method for registration and fusion of cerebrovascular images according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a bilinear interpolation method according to an embodiment of the present invention;
FIG. 3 is a diagram of an image registration framework provided by an embodiment of the present invention;
FIG. 4 is a graph of the registration results for a bright blood image and a black blood image using different search strategies;
FIG. 5 is a schematic diagram of spatial coordinate transformation provided by an embodiment of the present invention;
FIG. 6 is a flowchart of a common region of interest extraction provided by the embodiment of the present invention;
fig. 7 is a common region of interest map of the bright blood image and the black blood image provided by the embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but the embodiments of the present invention are not limited thereto.
Referring to fig. 1, fig. 1 is a flowchart of a cerebrovascular image registration and fusion method according to an embodiment of the present invention, and as shown in fig. 1, the cerebrovascular image registration and fusion method according to the embodiment of the present invention includes:
and S1, acquiring a bright blood image and a black blood image of the cerebral blood vessel.
Currently, in clinical evaluation of cerebrovascular lesion degree and vascular stenosis degree, lumen imaging based methods such as Digital Subtraction Angiography (DSA), CT Angiography (CTA), and High-Resolution Magnetic Resonance Angiography (HRMRA) are generally used. The image of the embodiment of the invention is preferably an image obtained by HRMRA imaging, the HRMRA serving as a non-invasive imaging method for a human body can clearly detect and analyze the structure of the blood vessel wall, the magnetic resonance image obtained by scanning has high resolution ratio for soft tissues, no bone artifacts and good image quality, and the tissue structures with different imaging characteristics can be obtained by using multiple sequences for scanning, so that the HRMRA imaging method has obvious superiority in displaying blood vessels.
Obtaining a bright blood image by using bright blood sequence scanning, obtaining a black blood image by using black blood sequence scanning, wherein in the bright blood image, blood is shown as bright color, and the blood vessel wall and background tissues show low signals; in the black blood image, blood appears black, and the blood vessel wall and the background tissue show high signals. The bright blood sequence can suppress the background, better display and quantitatively analyze the blood flow information, but due to the pollution of lumen signals, the blood vessel wall area close to the lumen is difficult to be reliably quantified by the bright blood sequence. In order to solve the problem that the thin vascular wall cannot be evaluated by the bright blood technology, a black blood sequence is often used, the strong contrast between signals of the black blood and the bright vascular wall can be provided, the degree of lesion of the vascular wall is evaluated by inhibiting blood signals and enhancing the signals of the vascular wall, and the symptom of atherosclerotic plaque is detected, so that the method is an effective method for evaluating the thin vascular wall. However, the contamination of the blood signal at the artery bends, including the proximal bends and the vicinity of the tip, may cause poor description of the image on the blood vessels and the surrounding tissues, and the flow-space artifact formed by the contaminated signal may simulate the wall thickening or plaque appearance of normal individuals, thereby affecting the diagnosis of doctors, and these reasons make the detection and stenosis analysis of the blood vessel wall of the magnetic resonance image very difficult. Therefore, further processing is required for the bright blood image and the black blood image.
The black blood image according to the embodiment of the present invention may be a normal black blood image obtained by HRMRA imaging, but is preferably an enhanced black blood image obtained by performing a sequential scan of black blood after injecting a contrast medium. In the enhanced black blood image, the blood signal suppression is better, the enhanced display of the vessel wall is realized, and the vessel wall structure is more clearly represented.
And S2, using the black blood image as a reference image and the bright blood image as a floating image, performing coordinate transformation on the bright blood image, and simultaneously performing interpolation processing on the bright blood image by a bilinear interpolation method.
Illustratively, this step may include:
and S21, acquiring DICOM orientation label information of the bright blood image and the black blood image.
Only with the information of the medical image file, the accurate processing of the medical image can be realized, and the expected effect is achieved. Digital Imaging and Communications in Medicine (DICOM) has become one of the most popular standards in the medical community. When medical images based on the DICOM3.0 standard are processed, the DICOM images are inevitably imported for file analysis.
The DICOM file is an image storage format for medical devices such as CT and nuclear magnetic resonance, and the contents stored in the DICOM standard include personal data of patients, image layer thickness, time stamp, medical device information, and the like, in addition to image information. Object Information Definitions (IODs) are core data of medical images, which describe image data and Information related to the image data, and each attribute data in the Object Information Definitions has a respective specific meaning. IODs are mainly composed of four categories, namely, Patient, student, Series and Image, wherein the Patient describes personal information such as name, sex, birth date and the like; study describes the date, location, type of examination, etc.; series mainly comprises attributes such as image position, azimuth, layer thickness, and interval between layers; image describes Image pixels, pixel pitch, intercept, slope, etc.
In this step, the bright blood image and the black blood image are imported to perform file analysis, so as to obtain the orientation label information of each of the bright blood image and the black blood image, where the orientation label information is data related to the imaging direction in the DICOM3.0 format image file, and is the image orientation attribute shown in table 1 below. This information gives the positional relationship between the patient and the imaging apparatus.
TABLE 1DICOM image orientation Attribute
| Attribute name | Tag | VM |
| Patient Position | (0008,5100) | 1 |
| Image Position(Patient) | (0020,0032) | 3 |
| Image Orientation(Patient) | (0020,0037) | 6 |
| Pixel Spacing | (0028,0030) | 2 |
| Slice Thickness | (0018,0050) | 1 |
And S22, according to the DICOM orientation label information, taking the black blood image coordinate system as a standard coordinate system, and performing coordinate transformation on the bright blood image coordinate system to the standard coordinate system.
The bright blood image and the black blood image are registered, and actually, each coordinate position in the bright blood image corresponds to the black blood image through a mapping relation. The embodiment of the invention preferably adopts a rigid body transformation mode to carry out space coordinate transformation.
The rigid body transformation realizes image registration through translation and rotation, and the distance between any two points in the images before and after registration is unchanged as shown in formula (1), (x)1,y1) As the original coordinates, (x)2,y2) For the image coordinates obtained after rotation by an angle theta, tx,tyThe displacement of the original image on the x-axis and the y-axis is shown.
S23, the bright blood image is interpolated by a bilinear interpolation method.
In the process of spatial coordinate transformation, the pixel coordinates of the bright blood image after coordinate transformation do not completely coincide with the sampling grid of the original image, that is, the pixel coordinate points which are originally integers may not be integers any more after coordinate transformation, so that interpolation processing needs to be performed on the bright blood image to determine the gray values of the pixel coordinate points of the image after transformation again.
The embodiment of the invention adopts a bilinear interpolation method to carry out interpolation processing. Specifically, a weighted pixel average value of 4 coordinate points closest to the current coordinate is calculated by a bilinear interpolation method, and is assigned to the current coordinate point, so that a pixel value interpolated by the current coordinate is obtained.
Referring to fig. 2, fig. 2 is a schematic diagram of a bilinear interpolation method according to an embodiment of the present invention. The method needs to perform linear interpolation in two directions respectively, wherein the sequence of the directions is not important, and the bilinear interpolation algorithm considers four pixel values closest to a coordinate point, so that the gray discontinuity caused by a nearest neighbor interpolation method is basically overcome, a point P is a current coordinate, a point Q is four adjacent points, and f (x, y) is a pixel value obtained through interpolation calculation.
It should be noted that the steps S23 and S22 may be performed alternately.
By carrying out simulation experiments on the image interpolation method, the original image is firstly reduced by 50%, then an effect image with the same size as the original image is obtained by using different interpolation algorithms, and the effect image is compared with the original image. The data shown in table 2 is the average value of the results of repeating the interpolation operation for 100 times, and 5 evaluation indexes, namely root mean square error RMSE, peak signal-to-noise ratio PSNR, normalized cross-correlation coefficient NCC, normalized mutual information NMI, and Time consumption, are set in the experiment.
TABLE 2 image interpolation results
From the whole experimental data, the image interpolation effect by adopting the bilinear interpolation method is better than that by adopting the nearest neighbor interpolation, and the gray discontinuity caused by the nearest neighbor interpolation method is overcome.
And S3, calculating the similarity between the bright blood image and the black blood image after the difference processing by utilizing the similarity measurement.
The scale for measuring the feature similarity between the two images is the similarity measurement, and the selection of the proper similarity measurement can improve the registration accuracy, effectively inhibit noise and the like, and has very important function in the registration of the images.
Illustratively, a similarity measure provided by the embodiment of the present invention is measured by Root Mean Square Error (RMSE), and the Root Mean Square Error (RMSE) formula is shown in (2), and the smaller the RMSE value, the more similar the two images are.
Of course, the mean square error can also be used to measure its similarity. The Mean Square Error (MSE) formula is shown in (43), and the smaller the value of MSE, the smaller the Error between two images, and the more similar the images.
And S4, finding the optimal similarity measurement by utilizing the search strategy.
Referring to fig. 3, fig. 3 is a diagram of an image registration framework according to an embodiment of the present invention. In fig. 3, the floating image is a bright blood image, the reference image is a black blood image, the bright blood image is subjected to coordinate transformation and difference value processing, and the similarity between the bright blood image and the black blood image after difference value processing is calculated by using the similarity measure; and then finding the optimal similarity measurement by utilizing a search strategy, and iteratively solving the optimization by using a coordinate transformation-interpolation-similarity measurement-search strategy loop until the similarity measurement reaches the optimal value, and stopping iteration.
Image registration is essentially a multi-parameter optimization problem, namely, spatial coordinate change is performed on images by using a certain search strategy, and finally, the similarity measurement of the two images is optimized, wherein the search strategy and the spatial coordinate change are performed in a mutual intersection manner in the actual calculation process. The algorithm idea is to calculate the similarity measure between two images in each iteration, adjust the floating image through the operations of translation, rotation and other space coordinate transformation, and interpolate the images at the same time until the measure value of the two images is maximum.
As a kind of fruitIn an embodiment, the search strategy adopts a (1+1) -ES evolution strategy. Evolution Strategy (ES) analyzes and solves problems by simulating the process of genetic variation of organisms, which provides a series of parameter optimization algorithms for evaluating candidate solutions to a problem. The evolutionary strategy takes real values as genes and follows a gaussian distribution of N (0, σ) to generate new individuals. (1+1) -ES has only one parent, and only one child is generated at a time, and the better one of the two individuals is selected by comparing the mutated individual with the parent. As shown in formula (4), wherein XtFor the tth generation individuals, N (0, σ) is a normal distribution with a mean of 0 and a standard deviation of σ.
Xt+1＝Xt+N(0,σ) (4)
The key steps of the evolutionary strategy are crossover, variation in the degree of variation, and selection. Wherein the genes of the new individuals of the filial generation are recombined by crossing and exchanging the genes of the two parents. The variance is to add a new individual component generated by N (0, σ) to each selected component, where σ is the degree of variance, and σ is not fixed but larger at the beginning until the algorithm becomes smaller when approaching convergence, and the maximum number of iterations of convergence can be specified to prevent the search algorithm from falling into local extrema. And finally, selecting the optimal individual from the parent individuals and the child individuals as the optimal solution.
The experiment was performed using the (1+1) -ES search strategy while comparing the experimental results of the gradient descent optimizer.
The search strategies respectively register 160 bright blood images and 160 enhanced black blood images of corresponding scanning layers, wherein the enhanced black blood images are reference images, the bright blood images are floating images, the registration result is shown in fig. 4, and fig. 4 is a registration result diagram of the bright blood images and the black blood images by adopting different search strategies. Fig. 4(a) shows the results of two image pairwise registration without using the optimizer, fig. 4(b) shows the results of image pairwise registration using the gradient descent optimizer, and fig. 4(c) shows the results of image pairwise registration using the (1+1) -ES optimizer. The image display adopts a montage effect, and enhances a black blood image and a bright blood image by using pseudo-color transparency processing, wherein purple is the enhanced black blood image, and green is the bright blood image (colors are not shown in the figure because the image processing is a gray image). As can be seen from the figure, in the images which are not registered by using the optimizer, the enhanced black blood image and the bright blood image are not overlapped and have more shadows; when the gradient descent optimizer is used for registering images, although the registration effect is better than that of fig. 4(a), the obvious misalignment phenomenon still occurs at the gray brain matter; in the image using the (1+1) -ES optimizer, the registration result is accurate, and the misaligned shadow part in the image completely disappears. The data shown in table 3 are 3 evaluation indexes of the registration result, namely normalized mutual information NMI, normalized cross correlation coefficient NCC, and algorithm Time.
TABLE 3 analysis of results under different search strategies
aThe value in (1) is based on the mean value of the evaluation indexes of the registration of 160 bright blood images and 160 enhanced black blood images +/-mean square error
From the experimental result graph, the registration image effect of (1+1) -ES is displayed more clearly and is better than that of a gradient descent optimizer; from experimental data, the three evaluation indexes show the good performance of the (1+1) -ES optimizer, so that the embodiment of the invention preferentially selects the (1+1) -ES as the search strategy.
S5, performing coordinate transformation on the bright blood image when the similarity measurement reaches the optimum value according to the spatial transformation matrix to obtain a first registration image; the first registered image includes a black blood image, and a bright blood image after coordinate transformation.
And when the similarity measurement reaches the optimum, stopping iteration, and performing coordinate transformation on the bright blood image again according to the optimum value of the similarity measurement to realize complete registration of the bright blood image and the black blood image in the same coordinate system.
Referring to fig. 5, fig. 5 is a schematic diagram of spatial coordinate transformation according to an embodiment of the present invention. In fig. 5, the left image is a black blood image (enhanced black blood image) which is imaged by coronal scan; the upper right image is an original bright blood image which is imaged according to an axial surface; the difference of the sequence scanning direction causes the difference of the final magnetic resonance imaging layer, so the magnetic resonance images of different imaging layers need to be observed under a standard reference coordinate system through space coordinate transformation. The lower right image is a bright blood image subjected to spatial transformation; it can be seen that the bright blood image and the black blood image after spatial transformation are already in the same coordinate system, and magnetic resonance images of different imaging slices can be observed.
S6, extracting the same scanning area in the black blood image according to the scanning area of the bright blood image in the first registration image to obtain a second registration image; the second registration image includes a bright blood image after coordinate transformation, and an extracted black blood image having the same scanning area as the bright blood image.
Because the scanning ranges of the cerebrovascular imaging of a patient in different magnetic resonance sequences are different, and after the bright blood images are subjected to image coordinate transformation, the information of the coronal plane of the bright blood images is not rich in the information of the enhanced black blood images, so that the same region under the two sequences can be registered more quickly and accurately, the same scanning region can be extracted from the enhanced black blood images according to the scanning region of the bright blood images, and the two images after registration reflect the functional information of different sections in the cranium on the same reference without realizing image space transformation by a doctor through self imagination, so that the doctor can understand and utilize the new comprehensive information conveniently.
Referring to fig. 6, fig. 6 is a flowchart illustrating a common roi extraction process according to an embodiment of the present invention. As shown in fig. 6, this step may include, for example:
s61, inputting a bright blood image and a black blood image;
s62, obtaining edge contour information of the cerebral vessels in the bright blood image by using a Sobel edge detection method for the bright blood image;
s63, respectively extracting a minimum abscissa value, a maximum abscissa value, a minimum ordinate value and a maximum ordinate value in the edge profile information as an initial extraction frame;
s64, expanding the initial extraction frame outwards within the size of the size boundary of the bright blood image to serve as a final extraction frame;
after the spatial coordinate transformation is carried out, the scanning areas of the bright blood image and the enhanced black blood image cannot be completely overlapped, so that the initial extraction frame needs to be expanded outwards within the size of the size boundary of the bright blood image to be used as a final extraction frame; and finally, extracting the common region of interest of the enhanced black blood image by using the extraction frame. The outward expansion range of the initial extraction frame can be 10-30 pixel sizes, and is preferably 20 pixel sizes.
And S65, extracting the image region of interest of the black blood image by using the final extraction frame to obtain a common region of interest of the bright blood image and the black blood image, wherein the common region of interest is the second registration image.
Referring to fig. 7, fig. 7 is a common region of interest map of a bright blood image and a black blood image provided by an embodiment of the present invention. In the figure, the left image is a bright blood image after spatial coordinate transformation, and the right image is a black blood image (enhanced black blood image), wherein an image in a selected range of a red rectangular frame (the red rectangular frame is displayed as a gray rectangular frame in the figure due to image processing as a gray image) on the black blood image is an image corresponding to the bright blood image after spatial coordinate transformation, and is referred to as a common region of interest of the bright blood image and the black blood image. By extracting the images of the common interested areas on the black blood image, the obtained bright blood image and the black blood image can not only display different image information, but also be in the same coordinate system, and the interested areas are the same, so that a doctor can check the two registered images in a targeted manner, or the fusion range of subsequent further images is narrowed.
And S7, carrying out image fusion on the two images in the second registration image by adopting a pyramid decomposition image fusion algorithm.
On the basis of obtaining the second registration image of the common region of interest, in order to further facilitate direct viewing by a doctor, the respective information of the two registered images is embodied on one image, and the two images in the second registration image need to be subjected to image fusion.
The embodiment of the invention adopts a pyramid decomposition image fusion algorithm, and the algorithm principle is that a multi-scale analysis method is utilized, two images in a second registration image with a fixed scale are decomposed by a pyramid, and two modal images are analyzed and fused on different spatial frequency bands.
For example, the image fusion of the two images in the second registration image by the pyramid decomposition image fusion algorithm adopted in the embodiment of the present invention may specifically include:
s71, decomposing the two images in the second registration image into different spatial frequency bands, and respectively storing the two images into a bright blood image decomposition subset and a black blood image decomposition subset;
s72, aiming at the bright blood image decomposition subset and the black blood image decomposition subset of each decomposition layer, respectively carrying out image fusion according to the corresponding high-frequency information fusion rule and low-frequency information fusion rule to obtain a cerebral blood vessel fusion image subset;
and S73, carrying out pyramid inverse decomposition on the cerebrovascular fusion image subset to obtain a final fusion result.
The embodiment of the invention adopts a Laplacian pyramid image fusion method, and the purpose of the Laplacian pyramid decomposition and fusion is to decompose an original image into different spatial frequency bands respectively, wherein the different spatial frequency bands contain different characteristics and details. Different fusion rules are adopted for the decomposition layers on different frequency bands, the purpose of highlighting the features and the details on the specific frequency band can be achieved, and finally pyramid decomposition inverse transformation is carried out on each fused decomposition layer to obtain a final fusion image.
The embodiment of the invention divides the pyramid decomposition layers of the Laplacian image into two types: top layer, other layers. For the laplacian pyramid top-layer image, the present invention first calculates the average gradient of each pixel region as mxn, which is calculated as formula (5):
wherein Δ Ix ═ f (x, y) -f (x-1, y); Δ Iy ═ f (x, y) -f (x, y-1); Δ Ix and Δ Iy are the first order differences of the pixel f (x, y) in the x and y directions, respectively.
After the region average gradient of each pixel is obtained, fusion is performed according to the pixel region average gradient values of the corresponding decomposition layers of the two fusion images, and the fusion method is shown as formula (6).
Where F (i, j) represents the fused pixel gray scale value, Io (i, j) and Ii (i, j) represent the pixel gray scale values of the bright blood image and the black blood image, respectively, and Go (i, j) and Gi (i, j) represent the region average gradient values of the bright blood image and the black blood image at the pixel position (i, j), respectively.
For the fusion rule of other layers, the region energy of each corresponding decomposition layer pixel is calculated, and the calculation method is shown in formula (7).
Where Ro (i, j) and Ri (i, j) respectively denote the regional energy values of the bright blood image and the black blood image at the pixel position (i, j), and p, q, λ are intermediate variables, in the embodiment of the present invention, p ═ q ═ 1,
the other layer pixels are fused according to the pixel region energy value, and the calculation is shown as the formula (8).
According to the scheme provided by the embodiment of the invention, the appropriate method is selected for the bright blood image and the black blood image of the cerebral vessels for preliminary registration, the common region of interest is extracted after the preliminary registration for the secondary registration, and then the two images are fused into one image through the pyramid decomposition image fusion algorithm.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (10)
1. A cerebrovascular image registration and fusion method is characterized by comprising the following steps:
acquiring a bright blood image and a black blood image of a cerebral blood vessel;
taking the black blood image as a reference image and the bright blood image as a floating image, performing coordinate transformation on the bright blood image, and performing interpolation processing on the bright blood image by adopting a bilinear interpolation method;
calculating the similarity between the bright blood image and the black blood image after difference processing by utilizing similarity measurement;
finding the optimal similarity measurement by utilizing a search strategy;
performing coordinate conversion on the bright blood image when the similarity measurement reaches the optimum value according to the spatial transformation matrix to obtain a first registration image; the first registered image comprises the black blood image and the bright blood image after coordinate transformation;
extracting the same scanning area in the black blood image according to the scanning area of the bright blood image in the first registration image to obtain a second registration image; the second registration image comprises a bright blood image after coordinate transformation and an extracted black blood image which has the same scanning area with the bright blood image;
and performing image fusion on the two images in the second registration image by adopting a pyramid decomposition image fusion algorithm.
2. The cerebrovascular image registration and fusion method according to claim 1, wherein the black blood image is an enhanced black blood image using a contrast agent.
3. The cerebrovascular image registration and fusion method according to claim 1, wherein the taking the black blood image as a reference image and the bright blood image as a floating image, performing coordinate transformation on the bright blood image, and simultaneously performing interpolation processing on the bright blood image by using a bilinear interpolation method includes:
acquiring DICOM orientation label information of the bright blood image and the black blood image;
according to the DICOM orientation label information, taking the black blood image coordinate system as a standard coordinate system, and carrying out coordinate transformation on the bright blood image coordinate system to the standard coordinate system;
and simultaneously, carrying out interpolation processing on the bright blood image by adopting a bilinear interpolation method.
4. The cerebrovascular image registration and fusion method according to claim 1, wherein the bilinear interpolation method obtains the interpolated pixel value of the current coordinate by calculating the weighted pixel average of 4 coordinate points closest to the current coordinate and assigning the weighted pixel average to the current coordinate point.
5. The cerebrovascular image registration and fusion method of claim 1, wherein the similarity measure is measured using a root mean square error.
6. The cerebrovascular image registration and fusion method according to claim 1, wherein the search strategy employs a (1+1) -ES evolution strategy.
7. The method for registering and fusing cerebrovascular images according to claim 1, wherein the same scanning area in the black blood image is extracted according to the scanning area of the bright blood image in the first registered image to obtain a second registered image; the second registration image includes a bright blood image after coordinate transformation, and an extracted black blood image having the same scanning area as the bright blood image, and includes:
inputting the bright blood image and the black blood image;
using a Sobel edge detection method for the bright blood image to obtain edge contour information of cerebral vessels in the bright blood image;
respectively extracting a minimum abscissa value, a maximum abscissa value, a minimum ordinate value and a maximum ordinate value in the edge profile information as initial extraction frames;
expanding the initial extraction frame outwards within the size of the size boundary of the bright blood image to serve as a final extraction frame;
and performing image region-of-interest extraction on the black blood image by using the final extraction frame to obtain the second registration image.
8. The cerebrovascular image registration and fusion method according to claim 7, wherein the initial extraction frame expands outward in a range of 10-30 pixels.
9. The method for registering and fusing cerebrovascular images according to claim 1, wherein the image fusing two images in the second registered image by using a pyramid decomposition image fusion algorithm comprises:
and analyzing and fusing two modal images on different spatial frequency bands by using a multi-scale analysis method and carrying out pyramid decomposition on two images in the second registration image with a fixed scale.
10. The method for registering and fusing cerebrovascular images according to claim 9, wherein the image fusing two images in the second registered image by using a pyramid decomposition image fusion algorithm comprises:
decomposing two images in the second registration image into different spatial frequency bands, and respectively storing the two images into a bright blood image decomposition subset and a black blood image decomposition subset;
performing image fusion according to a corresponding high-frequency information fusion rule and a corresponding low-frequency information fusion rule respectively aiming at the bright blood image decomposition subset and the black blood image decomposition subset of each decomposition layer to obtain a cerebral vessel fusion image subset;
and carrying out pyramid inverse decomposition on the cerebrovascular fusion image subset to obtain a final fusion result.
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