CN117726561B - Intravascular ultrasound image processing method, related device and storage medium - Google Patents
Intravascular ultrasound image processing method, related device and storage medium Download PDFInfo
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Abstract
The application discloses an intravascular ultrasound image processing method, a related device and a storage medium, wherein the method comprises the following steps: acquiring a multi-frame target image of a blood vessel; the target image refers to a two-dimensional intravascular ultrasound sequence signal image; respectively carrying out multi-scale analysis on each frame of target image to obtain a sub-band signal diagram corresponding to each frame of target image; combining each frame of target image and each corresponding frame of sub-band signal diagram into a multi-dimensional feature set; reconstructing the multi-dimensional feature set into Casorati data matrix, and extracting space-time frequency domain feature information to obtain current feature value matrix; extracting the characteristic value distribution data of the blood signal from the current characteristic value matrix through the self-adaptive band-pass filter matrix; and carrying out gray value adjustment on an image area corresponding to the characteristic value distribution data of the blood signals in the image to be processed in the target image to obtain the processed intravascular ultrasound image, so that the inhibition of blood spots is realized, and the contrast ratio of blood and tissues is improved.
Description
Technical Field
The present application relates to the field of image processing technologies, and in particular, to an intravascular ultrasound image processing method, an intravascular ultrasound image processing device, and a storage medium.
Background
Intravascular ultrasound imaging is an imaging modality that utilizes a miniature ultrasound transducer mounted on the tip of a cardiac catheter to display a two-dimensional cross-section of a blood vessel in real time by transmitting and receiving ultrasound. In order to obtain better tissue resolution, image acquisition is generally performed with higher ultrasound transmission frequencies. However, high frequency ultrasound has large signal attenuation, low signal to noise ratio, and the higher the ultrasound frequency, the stronger the back scattering from the blood, so that the contrast between the blood and the vessel wall in the lumen is significantly reduced.
In order to improve the contrast between blood and blood vessel wall, one way currently adopted is to divide the image into different areas through the gray value range of the image, and then adjust the gray values of the different areas by adjusting the gain coefficient of the time gain supplement, thereby improving the contrast of the image target. Another way is to send dual-frequency ultrasound through the transducer, detect the blood signal by the difference of the dual-frequency echoes of tissue and blood, and then adjust accordingly to improve contrast.
However, the first method depends on the fact that the difference exists between the energy scattering intensity of blood and the scattering intensity of tissue, and the energy scattering of the blood and the tissue is almost consistent under high-frequency ultrasound, so that the blood and the tissue signals cannot be accurately distinguished at this time, and the contrast ratio of the blood and the tissue signals cannot be effectively improved. The other mode needs to transmit ultrasonic waves twice and compare echo signals, so that the amplitude values of the two echo signals need to be aligned effectively, the requirements on the bandwidth performance and consistency of the transducer are very high, errors are easy to occur, and therefore blood and tissue signals cannot be distinguished accurately, and the contrast ratio of the two signals cannot be improved effectively.
Disclosure of Invention
Based on the defects of the prior art, the application provides an intravascular ultrasound image processing method, a related device and a storage medium, and aims to solve the problem that the prior art cannot effectively and accurately distinguish blood and tissues, so that the contrast of the blood and the tissue cannot be effectively ensured to be improved.
In order to achieve the above object, the present application provides the following technical solutions:
the first aspect of the present application provides a method for processing an intravascular ultrasound image, comprising:
acquiring a multi-frame target image of a blood vessel; wherein the target image refers to a two-dimensional intravascular ultrasound sequence signal image;
respectively carrying out multi-scale analysis on the target images of each frame to obtain multi-frame sub-band signal diagrams with different resolutions corresponding to the target images of each frame;
Combining the target image of each frame and the sub-band signal image of each frame corresponding to each frame to obtain a multi-dimensional feature set;
reconstructing the multi-dimensional feature set into a current Casorati data matrix;
Extracting space-time frequency domain characteristic information of the current Casorati data matrix to obtain a current characteristic value matrix;
Extracting characteristic value distribution data of the blood signal from the current characteristic value matrix through a self-adaptive band-pass filter matrix; the adaptive band-pass filter matrix is used for adaptively adjusting cut-off filter parameters for distinguishing blood from other components based on the current eigenvalue matrix;
Adjusting the gray value of an image area corresponding to the characteristic value distribution data of the blood signals in the image to be processed to obtain a processed intravascular ultrasound image; the image to be processed is an image in the target image of each frame.
Optionally, in the intravascular ultrasound image processing method, the performing multi-scale analysis on the target image of each frame to obtain multi-frame subband signal diagrams with different resolutions corresponding to the target image of each frame includes:
And respectively carrying out continuous conversion on the target image for multiple times by utilizing a preset conversion mode aiming at each frame of the target image, and carrying out downsampling on the image after each conversion to obtain multi-frame sub-band signal diagrams with different resolutions corresponding to the target image.
Optionally, in the intravascular ultrasound image processing method, combining the target image of each frame and the subband signal map of each frame corresponding to the target image to obtain the multi-dimensional feature set includes:
Respectively aiming at each frame of the target image, sequentially splicing the sub-band signal diagrams of each frame of the target image according to the size to obtain a sub-band signal combination diagram of the target image with the same size as the target image;
splicing the target image and the sub-band signal combination diagram corresponding to the target image to obtain a combination diagram corresponding to the target image;
and splicing the combined images corresponding to the target images of each frame in sequence according to the frame numbers of the target images of each frame to obtain the multi-dimensional feature set.
Optionally, in the intravascular ultrasound image processing method, before extracting the feature value distribution data of the blood signal from the current feature value matrix through the adaptive band-pass filter matrix, the method further includes:
Extracting characteristic values on diagonal lines of the current characteristic value matrix, and sorting the characteristic values in descending order according to the size to obtain a characteristic value distribution matrix;
Taking the minimum value in the distance from each characteristic value in the characteristic value distribution matrix to the origin of coordinates as a first current cut-off filtering parameter for distinguishing blood from tissue;
Calculating the average value of all the characteristic values in the characteristic value distribution matrix, and determining a second current cut-off filtering parameter for distinguishing blood from noise according to the calculated average value;
and adjusting the adaptive band-pass filter matrix by utilizing the first current cut-off filter parameter and the second current cut-off filter parameter.
Optionally, in the intravascular ultrasound image processing method, the adjusting the gray value of the image area corresponding to the characteristic value distribution data of the blood signal in the image to be processed to obtain the processed intravascular ultrasound image includes:
Constructing a weight matrix with the same size as the gray value matrix of the image to be processed according to the characteristic value distribution data of the blood signals; wherein, the numerical value of each position corresponding to the characteristic value distribution data of the blood signal in the weight matrix is a target weight value, and the numerical values of other positions are 1;
Multiplying the weight matrix with the gray value matrix of the image to be processed to obtain an adjusted gray value matrix of the image to be processed;
and rendering the image to be processed by using the adjusted gray value matrix of the image to be processed to obtain a processed intravascular ultrasound image.
Optionally, in the intravascular ultrasound image processing method, the adjusting the gray value of the image area corresponding to the characteristic value distribution data of the blood signal in the image to be processed to obtain the processed intravascular ultrasound image includes:
Generating a blood gray value matrix with the same size as the gray value matrix of the image to be processed according to the characteristic value distribution data of the blood signals; wherein, the numerical value of each position corresponding to the characteristic value distribution data of the blood signal in the blood gray value matrix is a target gray value, and the numerical values of other positions are 0;
Subtracting the blood gray value matrix from the gray value matrix of the image to be processed to obtain an adjusted gray value matrix of the image to be processed;
and rendering the image to be processed by using the adjusted gray value matrix of the image to be processed to obtain a processed intravascular ultrasound image.
A second aspect of the present application provides an intravascular ultrasound image processing device comprising:
an acquisition unit for acquiring a multiframe target image of a blood vessel; wherein the target image refers to a two-dimensional intravascular ultrasound sequence signal image;
the analysis unit is used for respectively carrying out multi-scale analysis on the target images of all frames to obtain multi-frame sub-band signal diagrams with different resolutions corresponding to the target images of all frames;
The combination unit is used for combining the target image of each frame and the sub-band signal diagram of each frame corresponding to the target image to obtain a multi-dimensional feature set;
a reconstruction unit, configured to reconstruct the multi-dimensional feature set into a current Casorati data matrix;
The decomposition unit is used for extracting the space-time frequency domain characteristic information of the current Casorati data matrix to obtain a current characteristic value matrix;
The filtering unit is used for extracting the characteristic value distribution data of the blood signal from the current characteristic value matrix through the self-adaptive band-pass filtering matrix; the adaptive band-pass filter matrix is used for adaptively adjusting cut-off filter parameters for distinguishing blood from other components based on the current eigenvalue matrix;
The adjusting unit is used for adjusting the gray value of the image area corresponding to the characteristic value distribution data of the blood signals in the image to be processed to obtain a processed intravascular ultrasound image; the image to be processed is an image in the target image of each frame.
Optionally, in the intravascular ultrasound image processing device described above, the analysis unit includes:
And the analysis subunit is used for respectively carrying out continuous conversion on the target image for multiple times by utilizing a preset conversion mode for each frame of the target image, and carrying out downsampling on the image after each conversion to obtain multi-frame subband signal diagrams with different resolutions corresponding to the target image.
Optionally, in the intravascular ultrasound image processing device described above, the combining unit includes:
The first splicing unit is used for splicing the sub-band signal diagrams of each frame of the target image according to the size in sequence for each frame of the target image to obtain a sub-band signal combination diagram of the target image consistent with the size of the target image;
The second splicing unit is used for splicing the target image and the sub-band signal combination graph corresponding to the target image to obtain a combination graph corresponding to the target image;
And the third splicing unit is used for splicing the combined images corresponding to the target images of each frame in sequence according to the frame numbers of the target images of each frame to obtain the multi-dimensional feature set.
Optionally, in the intravascular ultrasound image processing device described above, the intravascular ultrasound image processing device further includes:
the sorting unit is used for extracting the characteristic values on the diagonal of the current characteristic value matrix, and sorting the characteristic values in descending order according to the size to obtain a characteristic value distribution matrix;
A first determining unit, configured to use a minimum value in a distance from each of the feature values in the feature value distribution matrix to an origin of coordinates as a first current cutoff filtering parameter for distinguishing blood from tissue;
the second determining unit is used for calculating the average value of the characteristic values in the characteristic value distribution matrix and determining a second current cut-off filtering parameter for distinguishing blood from noise according to the calculated average value;
And the adaptive unit is used for adjusting the adaptive band-pass filter matrix by utilizing the first current cut-off filter parameter and the second current cut-off filter parameter.
Optionally, in the intravascular ultrasound image processing device described above, the adjusting unit includes:
The first construction unit is used for constructing a weight matrix with the same size as the gray value matrix of the image to be processed according to the characteristic value distribution data of the blood signals; wherein, the numerical value of each position corresponding to the characteristic value distribution data of the blood signal in the weight matrix is a target weight value, and the numerical values of other positions are 1;
The first calculation unit is used for multiplying the weight matrix with the gray value matrix of the image to be processed to obtain an adjusted gray value matrix of the image to be processed;
The first rendering unit is used for rendering the image to be processed by utilizing the adjusted gray value matrix of the image to be processed, so as to obtain the processed intravascular ultrasound image.
Optionally, in the intravascular ultrasound image processing device described above, the adjusting unit includes:
The second construction unit is used for generating a blood gray value matrix with the same size as the gray value matrix of the image to be processed according to the characteristic value distribution data of the blood signals; wherein, the numerical value of each position corresponding to the characteristic value distribution data of the blood signal in the blood gray value matrix is a target gray value, and the numerical values of other positions are 0;
the second calculation unit is used for subtracting the blood gray value matrix from the gray value matrix of the image to be processed to obtain an adjusted gray value matrix of the image to be processed;
and the second rendering unit is used for rendering the image to be processed by utilizing the adjusted gray value matrix of the image to be processed to obtain the processed intravascular ultrasound image.
A third aspect of the present application provides an electronic device, comprising:
A memory and a processor;
Wherein the memory is used for storing programs;
The processor is configured to execute the program, and when the program is executed, the program is specifically configured to implement the intravascular ultrasound image processing method according to any one of the above.
A fourth aspect of the present application provides a computer storage medium storing a computer program for implementing the intravascular ultrasound image processing method according to any one of the preceding claims when executed.
The embodiment of the application provides an intravascular ultrasound image processing method, which is used for acquiring a multiframe target image of a blood vessel so as to extract sufficient characteristics from the multiframe image. Wherein the target image refers to a two-dimensional ultrasound sequence signal image. And then respectively carrying out multi-scale analysis on each frame of target image to obtain multi-frame sub-band signal diagrams with different resolutions corresponding to each frame of target image, and then combining each frame of target image and each frame of sub-band signal diagram corresponding to each frame of target image to obtain a multi-dimensional feature set, so that feature extraction can be carried out from multiple scales. And reconstructing the multi-dimensional feature set into a current Casorati data matrix, and extracting space-time frequency domain feature information of the current Casorati data matrix to obtain a current feature value matrix, so that multi-scale feature analysis is realized. And extracting the characteristic value distribution data of the blood signal from the current characteristic value matrix through the self-adaptive band-pass filter matrix. The self-adaptive band-pass filter matrix is used for self-adaptively adjusting and distinguishing cut-off filter parameters of blood and other components based on the current characteristic value matrix, so that the characteristic value distribution data of the blood can be accurately extracted, and the information of tissues is prevented from being extracted. Finally, the gray value of an image area corresponding to the characteristic value distribution data of the blood signals in the image to be processed in the target image is adjusted to obtain the processed intravascular ultrasound image, so that the blood spot signals are accurately extracted through multi-scale characteristic decomposition and adjusted, and further, the contrast ratio of blood and tissues is effectively improved under the condition that the tissue information is not lost.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for processing an intravascular ultrasound image according to an embodiment of the present application;
FIG. 2 is a flowchart of a method for combining target images of frames and subband signal diagrams of frames according to an embodiment of the present application;
FIG. 3 is a flowchart of a method for adaptively adjusting an adaptive bandpass filter matrix according to an embodiment of the present application;
FIG. 4 is a flowchart of a method for adjusting gray values of a blood signal region according to an embodiment of the present application;
FIG. 5 is a flowchart of another method for adjusting gray values of a blood signal region according to an embodiment of the present application;
Fig. 6 is a schematic diagram of an architecture of an intravascular ultrasound image processing device according to an embodiment of the present application;
Fig. 7 is a schematic diagram of an architecture of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In the present application, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The application provides an intravascular ultrasound image processing method, which is shown in figure 1 and specifically comprises the following steps:
s101, acquiring a multi-frame target image of a blood vessel.
The target image refers to a two-dimensional intravascular ultrasound sequence signal image, and generally refers to a high-frequency two-dimensional intravascular ultrasound sequence signal image, because the contrast between blood vessels and tissues in the high-frequency two-dimensional intravascular ultrasound sequence signal image is poor, and the contrast between the blood vessels and tissues needs to be improved. Of course, the image of the high-frequency intravascular two-dimensional ultrasonic sequence signal is not required, and although the contrast ratio is relatively high, the image can be processed in the usual subsequent steps, so that the contrast ratio of blood and tissues in the image is further improved.
Specifically, the high-frequency two-dimensional intravascular ultrasound sequence signal image of the blood vessel can be acquired through intravascular ultrasound imaging equipment.
In order to obtain more features and accurately identify blood based on the obtained features, in the embodiment of the application, a multi-frame target image of a blood vessel needs to be obtained.
Alternatively, the ultrasound imaging device is typically constantly moving during the examination process, so that the acquired target images of different locations of the blood vessel can also facilitate the acquisition of more differentiated blood and other constituent features. Of course, the target image of a certain position can be acquired under different angles.
It should be noted that the multiple frame target image may be obtained by including a frame of target image currently collected and multiple frames of target images collected before the current target image, so as to perform contrast processing on the current collected target image through subsequent steps based on the previous collected target image. Of course, the multiple frames of the target images can be all acquired currently, so that a frame of the processed target image can be obtained through subsequent steps based on the multiple frames of the current acquired images.
S102, respectively carrying out multi-scale analysis on each frame of target image to obtain multi-frame sub-band signal diagrams with different resolutions corresponding to each frame of target image.
Because the features of the objects in the images are different under different scales, in order to obtain the features with more dimensions and identify the blood signals, in the embodiment of the application, the target images of each frame are respectively analyzed in multiple scales, so that sub-band signal diagrams with different resolutions under each scale, namely sub-graphs with different resolutions of the target objects of each frame, are obtained.
Optionally, in another embodiment of the present application, a specific implementation of step S102 includes:
and respectively carrying out continuous conversion on each frame of target image for a plurality of times by using a preset conversion mode, and carrying out downsampling on each converted image to obtain multi-frame subband signal diagrams with different resolutions corresponding to the target image.
Specifically, a target image is transformed once by using a preset transformation mode, and then the transformed image is downsampled to obtain a frame of subband signal diagram. Then, the image obtained by the previous transformation is transformed again, and the image after the transformation is also downsampled, so as to obtain a second frame subband signal diagram. The transformation and the downsampling are repeated continuously, so that multi-frame subband signal diagrams with different resolutions of one frame of target image can be obtained.
Alternatively, the preset change mode may be one of a laplacian pyramid transform, a wavelet transform, a ridge wave transform, a curvelet transform, a contour wave transform, and the like.
S103, combining each frame of target image and each frame of sub-band signal diagram corresponding to each frame of target image to obtain a multi-dimensional feature set.
In order to facilitate the subsequent processing, all the images to be processed are combined into one body. And as the signal image which also belongs to one resolution in each frame of target image is initially acquired, the signal image also contains certain characteristics, all the target images and each frame of self-contained signal image corresponding to all the target images are combined to obtain a multi-dimensional characteristic set.
Optionally, in another embodiment of the present application, a specific implementation of step S103, as shown in fig. 2, specifically includes the following steps:
s201, respectively aiming at each frame of target image, splicing the sub-band signal diagrams of each frame of the target image in sequence according to the size of the target image to obtain a sub-band signal combination diagram of the target image with the same size as the target image.
S202, splicing the target image and the sub-band signal combination diagram corresponding to the target image to obtain the combination diagram corresponding to the target image.
And S203, sequentially splicing the combined images corresponding to the target images of each frame according to the frame numbers of the target images of each frame to obtain a multi-dimensional feature set.
The frame number is the sequence number of the sequence of collecting the target images.
S104, reconstructing the multi-dimensional feature set into a current Casorati data matrix.
In order to decompose the space-time frequency domain feature information later, in the embodiment of the application, the multi-dimensional feature set is reconstructed into a current Casorati data matrix, and then the current Casorati data matrix is further analyzed. Specifically, relevant data of each frame of target image in the multidimensional feature set can be combined according to frames to obtain a current Casorati data matrix.
S105, extracting space-time frequency domain characteristic information of the current Casorati data matrix to obtain a current characteristic value matrix.
Specifically, by decomposing the current Casorati data matrix, corresponding space-time frequency domain characteristic information can be obtained, and the information is the current characteristic value matrix.
Optionally, the manner of Casorati data matrix eigen decomposition includes, but is not limited to, eigenvalue decomposition (EVD) or Singular Value Decomposition (SVD). Taking singular value decomposition as an example, performing singular value decomposition on Casorati data matrices can decompose the matrices into 3 matrix products, namely a singular value matrix, a left singular value matrix and a right singular value matrix. The values in the singular value matrix represent the magnitude of the signal energy.
S106, extracting the characteristic value distribution data of the blood signal from the current characteristic value matrix through the self-adaptive band-pass filter matrix.
Specifically, after the current eigenvalue matrix is obtained, specific values belonging to the blood signals are extracted from the current eigenvalue matrix obtained by decomposition in a filtering mode, so that eigenvalue distribution data of the blood signals are obtained.
However, to accurately extract the characteristic value distribution data of the blood signal, a corresponding cut-off filter parameter for distinguishing blood from other components needs to be set. Thus, in embodiments of the present application, the adaptive bandpass filter matrix adaptively adjusts cutoff filter parameters that distinguish blood from other components based on the current eigenvalue matrix.
Optionally, in another embodiment of the present application, there is provided a method for adaptively adjusting an adaptive bandpass filter matrix, as shown in fig. 3, including the steps of:
S301, extracting characteristic values on diagonal lines of a current characteristic value matrix, and sorting the characteristic values in descending order according to the size to obtain a characteristic value distribution matrix.
In the current eigenvalue matrix, the eigenvalues are located on the diagonal lines of the matrix, so that the eigenvalues on the diagonal lines of the current eigenvalue matrix are extracted, and are sorted in descending order according to the size, so as to obtain an eigenvalue distribution matrix.
S302, taking the minimum value in the distance from each characteristic value in the characteristic value distribution matrix to the origin of coordinates as a first current cut-off filtering parameter for distinguishing blood from tissue.
S303, calculating the average value of all the characteristic values in the characteristic value distribution matrix, and determining a second current cut-off filtering parameter for distinguishing blood from noise according to the calculated average value.
S304, adjusting the adaptive band-pass filter matrix by using the first current cut-off filter parameter and the second current cut-off filter parameter.
And S107, adjusting the gray value of the image area corresponding to the characteristic value distribution data of the blood signals in the image to be processed to obtain the processed intravascular ultrasound image.
The image to be processed is an image in each frame of target image. Optionally, if the contrast of the currently acquired target image is adjusted, the image to be processed is the currently acquired target image. If the processed intravascular ultrasound image is obtained through the multi-frame target image, the image to be processed can be one frame in each frame of target object. Of course, the image to be processed may be a target image of each frame.
Specifically, when the characteristic value distribution data of the blood signal is obtained, the region of the blood signal in the image to be processed can be obtained, and then the contrast ratio of blood and tissues can be effectively improved by adjusting the gray value of the region.
Alternatively, if the adjustment is performed for the high-frequency two-dimensional ultrasound sequence signal image, the region of the blood signal will be mainly suppressed, i.e. the gray value of the region of the blood signal is reduced. If the two-dimensional ultrasonic sequence signal image is to be processed, the gray value of the region needs to be correspondingly enhanced.
Alternatively, in another embodiment of the present application, a specific implementation of step S107, as shown in fig. 4, includes the following steps:
S401, constructing a weight matrix with the same size as the gray value matrix of the image to be processed according to the characteristic value distribution data of the blood signals.
The value of each position corresponding to the characteristic value distribution data of the blood signal in the weight matrix is a target weight value, and the values of other positions are 1. The target weight value can be set to be smaller than 1, so that the gray value of blood can be restrained, and the gray value of the other positions can be kept unchanged when the gray value of the other positions are set to be 1, and the effect of improving the contrast ratio of the blood and the tissues is achieved.
Alternatively, the same target weight may be used for each position, and it is of course also possible to use, as the target weight value corresponding to each point, a ratio of the absolute value of the difference between the average value of each point and all points in the blood region and the average value of all points.
S402, multiplying the weight matrix by the gray value matrix of the image to be processed to obtain the adjusted gray value matrix of the image to be processed.
Specifically, the weight matrix is multiplied by the gray value matrix of the image to be processed, so that the gray value of the blood area in the gray value matrix of the image to be processed is calculated with the corresponding target weight value in the weight matrix, the gray value of the blood area is adjusted, and the gray values of other positions are multiplied by 1, so that the gray value is kept unchanged.
And S403, rendering the image to be processed by using the gray value matrix of the adjusted image to be processed, and obtaining the processed intravascular ultrasound image.
Alternatively, in another embodiment of the present application, another specific implementation of step S107, as shown in fig. 5, includes the following steps:
s501, generating a blood gray value matrix with the same size as the gray value matrix of the image to be processed according to the characteristic value distribution data of the blood signals.
Wherein, the numerical value of each position corresponding to the characteristic value distribution data of the blood signal in the blood gray value matrix is a target gray value, and the numerical values of other positions are 0.
S502, subtracting the blood gray value matrix from the gray value matrix of the image to be processed to obtain the adjusted gray value matrix of the image to be processed.
And S503, rendering the image to be processed by using the gray value matrix of the adjusted image to be processed, and obtaining the processed intravascular ultrasound image.
The embodiment of the application provides an intravascular ultrasound image processing method, which is used for acquiring intravascular multi-frame target images so as to extract sufficient characteristics from the multi-frame images. Wherein the target image refers to a two-dimensional ultrasound sequence signal image. And then respectively carrying out multi-scale analysis on each frame of target image to obtain multi-frame sub-band signal diagrams with different resolutions corresponding to each frame of target image, and then combining each frame of target image and each frame of sub-band signal diagram corresponding to each frame of target image to obtain a multi-dimensional feature set, so that feature extraction can be carried out from multiple scales. And reconstructing the multi-dimensional feature set into a current Casorati data matrix, and extracting space-time frequency domain feature information of the current Casorati data matrix to obtain a current feature value matrix, so that multi-scale feature analysis is realized. And extracting the characteristic value distribution data of the blood signal from the current characteristic value matrix through the self-adaptive band-pass filter matrix. The self-adaptive band-pass filter matrix is used for self-adaptively adjusting and distinguishing cut-off filter parameters of blood and other components based on the current characteristic value matrix, so that the characteristic value distribution data of the blood can be accurately extracted, and the information of tissues is prevented from being extracted. Finally, the gray value of an image area corresponding to the characteristic value distribution data of the blood signals in the image to be processed in the target image is adjusted to obtain the processed intravascular ultrasound image, so that the blood spot signals are accurately extracted through multi-scale characteristic decomposition and adjusted, and further, the contrast ratio of blood and tissues is effectively improved under the condition that the tissue information is not lost.
Another embodiment of the present application provides an intravascular ultrasound image processing device, as shown in fig. 6, comprising:
An acquisition unit 601 is configured to acquire a multiframe target image of a blood vessel.
Wherein the target image refers to a two-dimensional intravascular ultrasound sequence signal image.
The analysis unit 602 is configured to perform multi-scale analysis on each frame of target image, so as to obtain multi-frame sub-band signal diagrams with different resolutions corresponding to each frame of target image.
The combining unit 603 is configured to combine each frame of target image and each corresponding frame of subband signal map to obtain a multi-dimensional feature set.
A reconstruction unit 604, configured to reconstruct the multi-dimensional feature set into the current Casorati data matrix.
And the decomposition unit 605 is configured to extract the space-time frequency domain feature information of the current Casorati data matrix to obtain a current feature value matrix.
The filtering unit 606 is configured to extract the eigenvalue distribution data of the blood signal from the current eigenvalue matrix through the adaptive bandpass filtering matrix. The adaptive band-pass filter matrix is used for adaptively adjusting cut-off filter parameters for distinguishing blood from other components based on the current eigenvalue matrix.
The adjusting unit 607 is configured to adjust a gray value of an image area corresponding to the feature value distribution data of the blood signal in the image to be processed, so as to obtain a processed intravascular ultrasound image. The image to be processed is an image in each frame of target image.
Optionally, in the intravascular ultrasound image processing device provided in another embodiment of the present application, the analysis unit includes:
And the analysis subunit is used for respectively carrying out continuous conversion on the target image for multiple times by utilizing a preset conversion mode aiming at each frame of target image, and carrying out downsampling on the image after each conversion to obtain multi-frame subband signal diagrams with different resolutions corresponding to the target image.
Optionally, in the intravascular ultrasound image processing device provided in another embodiment of the present application, the combining unit includes:
The first splicing unit is used for splicing the sub-band signal diagrams of each frame of the target image according to the size in sequence for each frame of the target image to obtain a sub-band signal combination diagram of the target image with the same size as the target image.
And the second splicing unit is used for splicing the target image and the sub-band signal combination diagram corresponding to the target image to obtain the combination diagram corresponding to the target image.
And the third splicing unit is used for splicing the combined images corresponding to the target images of each frame in sequence according to the frame numbers of the target images of each frame to obtain a multi-dimensional feature set.
Optionally, in the intravascular ultrasound image processing device provided in another embodiment of the present application, the intravascular ultrasound image processing device further includes:
And the sorting unit is used for extracting the characteristic values on the diagonal of the current characteristic value matrix, and sorting the characteristic values in descending order according to the size to obtain a characteristic value distribution matrix.
A first determining unit, configured to use a minimum value in a distance from each feature value in the feature value distribution matrix to the origin of coordinates as a first current cut-off filtering parameter for distinguishing blood from tissue.
And the second determining unit is used for calculating the average value of all the characteristic values in the characteristic value distribution matrix and determining a second current cut-off filtering parameter for distinguishing blood from noise according to the calculated average value.
And the self-adaptive unit is used for adjusting the self-adaptive band-pass filter matrix by utilizing the first current cut-off filter parameter and the second current cut-off filter parameter.
Optionally, in the intravascular ultrasound image processing device provided in another embodiment of the present application, the adjusting unit includes:
The first construction unit is used for constructing a weight matrix consistent with the gray value matrix of the image to be processed in size according to the characteristic value distribution data of the blood signals. The value of each position corresponding to the characteristic value distribution data of the blood signal in the weight matrix is a target weight value, and the values of other positions are 1.
The first calculation unit is used for multiplying the weight matrix with the gray value matrix of the image to be processed to obtain the adjusted gray value matrix of the image to be processed.
The first rendering unit is used for rendering the image to be processed by utilizing the gray value matrix of the adjusted image to be processed, and obtaining the processed intravascular ultrasound image.
Optionally, in the intravascular ultrasound image processing device provided in another embodiment of the present application, the adjusting unit includes:
And the second construction unit is used for generating a blood gray value matrix with the same size as the gray value matrix of the image to be processed according to the characteristic value distribution data of the blood signals. Wherein, the numerical value of each position corresponding to the characteristic value distribution data of the blood signal in the blood gray value matrix is a target gray value, and the numerical values of other positions are 0.
The second calculation unit is used for subtracting the blood gray value matrix from the gray value matrix of the image to be processed to obtain the adjusted gray value matrix of the image to be processed.
And the second rendering unit is used for rendering the image to be processed by utilizing the gray value matrix of the adjusted image to be processed to obtain the processed intravascular ultrasound image.
It should be noted that, for the specific working process of each unit provided in the above embodiment of the present application, reference may be made to the implementation process of the corresponding step in the above embodiment accordingly, which is not repeated herein.
Another embodiment of the present application provides an electronic device, as shown in fig. 7, including:
a memory 701 and a processor 702.
Wherein the memory 701 is used for storing a program.
The processor 702 is configured to execute a program stored in the memory 701, and when the program is executed, the program is specifically configured to implement the intravascular ultrasound image processing method provided in any one of the embodiments described above.
Another embodiment of the present application provides a computer storage medium storing a computer program for implementing the intravascular ultrasound image processing method according to any one of the above, when the computer program is executed.
Computer storage media, including both non-transitory and non-transitory, removable and non-removable media, may be implemented in any method or technology for storage of information. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, read only compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by the computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. An intravascular ultrasound image processing method, comprising:
acquiring a multi-frame target image of a blood vessel; wherein the target image refers to a two-dimensional intravascular ultrasound sequence signal image;
respectively carrying out multi-scale analysis on the target images of each frame to obtain multi-frame sub-band signal diagrams with different resolutions corresponding to the target images of each frame;
Respectively aiming at each frame of the target image, sequentially splicing the sub-band signal diagrams of each frame of the target image according to the size to obtain a sub-band signal combination diagram of the target image with the same size as the target image;
splicing the target image and the sub-band signal combination diagram corresponding to the target image to obtain a combination diagram corresponding to the target image;
The combined images corresponding to the target images of all frames are spliced in sequence according to the frame numbers of the target images of all frames, so that a multi-dimensional feature set is obtained;
reconstructing the multi-dimensional feature set into a current Casorati data matrix;
Extracting space-time frequency domain characteristic information of the current Casorati data matrix to obtain a current characteristic value matrix;
Extracting characteristic values on diagonal lines of the current characteristic value matrix, and sorting the characteristic values in descending order according to the size to obtain a characteristic value distribution matrix;
Taking the minimum value in the distance from each characteristic value in the characteristic value distribution matrix to the origin of coordinates as a first current cut-off filtering parameter for distinguishing blood from tissue;
Calculating the average value of all the characteristic values in the characteristic value distribution matrix, and determining a second current cut-off filtering parameter for distinguishing blood from noise according to the calculated average value;
adjusting the adaptive band-pass filter matrix by utilizing the first current cut-off filter parameter and the second current cut-off filter parameter;
Extracting characteristic value distribution data of the blood signal from the current characteristic value matrix through a self-adaptive band-pass filter matrix; the adaptive band-pass filter matrix is used for adaptively adjusting cut-off filter parameters for distinguishing blood from other components based on the current eigenvalue matrix;
Adjusting the gray value of an image area corresponding to the characteristic value distribution data of the blood signals in the image to be processed to obtain a processed intravascular ultrasound image; the image to be processed is an image in the target image of each frame.
2. The method according to claim 1, wherein the performing multi-scale analysis on the target image of each frame to obtain a multi-frame subband signal map with different resolutions corresponding to the target image of each frame includes:
And respectively carrying out continuous conversion on the target image for multiple times by utilizing a preset conversion mode aiming at each frame of the target image, and carrying out downsampling on the image after each conversion to obtain multi-frame sub-band signal diagrams with different resolutions corresponding to the target image.
3. The method according to claim 1, wherein the adjusting the gray value of the image area corresponding to the characteristic value distribution data of the blood signal in the image to be processed to obtain the processed intravascular ultrasound image includes:
Constructing a weight matrix with the same size as the gray value matrix of the image to be processed according to the characteristic value distribution data of the blood signals; wherein, the numerical value of each position corresponding to the characteristic value distribution data of the blood signal in the weight matrix is a target weight value, and the numerical values of other positions are 1;
Multiplying the weight matrix with the gray value matrix of the image to be processed to obtain an adjusted gray value matrix of the image to be processed;
and rendering the image to be processed by using the adjusted gray value matrix of the image to be processed to obtain a processed intravascular ultrasound image.
4. The method according to claim 1, wherein the adjusting the gray value of the image area corresponding to the characteristic value distribution data of the blood signal in the image to be processed to obtain the processed intravascular ultrasound image includes:
Generating a blood gray value matrix with the same size as the gray value matrix of the image to be processed according to the characteristic value distribution data of the blood signals; wherein, the numerical value of each position corresponding to the characteristic value distribution data of the blood signal in the blood gray value matrix is a target gray value, and the numerical values of other positions are 0;
Subtracting the blood gray value matrix from the gray value matrix of the image to be processed to obtain an adjusted gray value matrix of the image to be processed;
and rendering the image to be processed by using the adjusted gray value matrix of the image to be processed to obtain a processed intravascular ultrasound image.
5. An intravascular ultrasound image processing device, comprising:
an acquisition unit for acquiring a multiframe target image of a blood vessel; wherein the target image refers to a two-dimensional intravascular ultrasound sequence signal image;
the analysis unit is used for respectively carrying out multi-scale analysis on the target images of all frames to obtain multi-frame sub-band signal diagrams with different resolutions corresponding to the target images of all frames;
The combination unit is used for combining the target image of each frame and the sub-band signal diagram of each frame corresponding to the target image to obtain a multi-dimensional feature set;
a reconstruction unit, configured to reconstruct the multi-dimensional feature set into a current Casorati data matrix;
The decomposition unit is used for extracting the space-time frequency domain characteristic information of the current Casorati data matrix to obtain a current characteristic value matrix;
The sorting unit is used for extracting the characteristic values on the diagonal of the current characteristic value matrix, and sorting the characteristic values in descending order according to the size to obtain a characteristic value distribution matrix;
a first determining unit, configured to use a minimum value in a distance from each feature value in the feature value distribution matrix to the origin of coordinates as a first current cut-off filtering parameter for distinguishing blood from tissue;
The second determining unit is used for calculating the average value of all the characteristic values in the characteristic value distribution matrix and determining a second current cut-off filtering parameter for distinguishing blood from noise according to the calculated average value;
The self-adaptive unit is used for adjusting the self-adaptive band-pass filter matrix by utilizing the first current cut-off filter parameter and the second current cut-off filter parameter;
The filtering unit is used for extracting the characteristic value distribution data of the blood signal from the current characteristic value matrix through the self-adaptive band-pass filtering matrix; the adaptive band-pass filter matrix is used for adaptively adjusting cut-off filter parameters for distinguishing blood from other components based on the current eigenvalue matrix;
The adjusting unit is used for adjusting the gray value of the image area corresponding to the characteristic value distribution data of the blood signals in the image to be processed to obtain a processed intravascular ultrasound image; wherein the image to be processed is an image in the target image of each frame;
the combination unit is specifically configured to splice the sub-band signal diagrams of each frame of the target image according to the size in sequence for each frame of the target image, so as to obtain a sub-band signal combination diagram of the target image with the same size as the target image; splicing the target image and the sub-band signal combination diagram corresponding to the target image to obtain a combination diagram corresponding to the target image; and splicing the combined images corresponding to the target images of each frame in sequence according to the frame numbers of the target images of each frame to obtain the multi-dimensional feature set.
6. The apparatus according to claim 5, wherein the analysis unit comprises:
And the analysis subunit is used for respectively carrying out continuous conversion on the target image for multiple times by utilizing a preset conversion mode for each frame of the target image, and carrying out downsampling on the image after each conversion to obtain multi-frame subband signal diagrams with different resolutions corresponding to the target image.
7. An electronic device, comprising:
A memory and a processor;
Wherein the memory is used for storing programs;
the processor is configured to execute the program, which when executed is specifically configured to implement the intravascular ultrasound image processing method according to any one of claims 1 to 4.
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