CN113040826A - Noise reduction module for performing noise reduction processing on ultrasonic image - Google Patents
Noise reduction module for performing noise reduction processing on ultrasonic image Download PDFInfo
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Abstract
The present disclosure describes a noise reduction module for noise reduction of ultrasound images, being included in an intravascular ultrasound system, which acquires ultrasound images; the noise reduction module is used for carrying out noise reduction processing on the ultrasonic image; the method for the noise reduction module to perform noise reduction processing comprises the following steps: selecting a first pixel area, and calculating the average gray value of the first pixel area; comparing the original gray value with the average gray value of the first pixel region to obtain a comparison result, and marking an interference pixel point according to the comparison result; selecting a second pixel area, respectively calculating the original gray value of the selected interference pixel point, the relative deviation between the median of the second pixel area and the average gray value of the first pixel area, and if the relative deviation between the median and the average gray value is smaller, replacing the original gray value of the selected interference pixel point by the median. According to the method and the device, the noise reduction processing process can be optimized, and the image quality of the ultrasonic image can be effectively improved.
Description
The application is filed as18 days in 2019, 10 monthsApplication No. is201910996383.5The invention is named asBlood vessel Image noise reduction method of internal ultrasonic systemDivisional application of the patent application.
Technical Field
The present disclosure relates to a noise reduction module for performing noise reduction processing on an ultrasound image.
Background
An Intravascular ultrasound (IVUS) system collects ultrasound images of a region to be examined in a blood vessel of a patient through an ultrasound probe, thereby assisting a doctor in diagnosing and treating whether and what kind of lesion exists in the region to be examined. Specifically, the intravascular ultrasound system has an ultrasound probe that can emit an ultrasound beam, which is emitted, for example, inside a human blood vessel by using the ultrasound probe to acquire an ultrasound image for displaying the tissue structure and geometry of the human blood vessel.
In the working process of the intravascular ultrasound system, the intravascular ultrasound system is easily interfered by strong electromagnetic pulses of other equipment in an operating room, such as a respirator, and the like, so that noise is generated on an ultrasound image, and the reading diagnosis of a doctor is influenced. To reduce the adverse effect of such noise on the diagnosis, the ultrasound image needs to be subjected to noise reduction processing to improve the image quality thereof. In the prior art, an image to be processed and a plurality of images adjacent to the image to be processed are generally selected, and a median filtering is performed on each pixel point in the image to be processed based on the adjacent images to perform a noise reduction process.
However, the above-mentioned prior art needs to use multiple images as processing objects in the processing process of the image to be processed, which increases the complexity of the system; and an undifferentiated median filtering mode is adopted, and the filtering mode is difficult to effectively perform noise reduction on the interfered pixel points in the image to be processed, so that the image quality of the ultrasonic image cannot be effectively improved.
Disclosure of Invention
The present disclosure is made in view of the above-mentioned state of the art, and an object of the present disclosure is to provide an image noise reduction method for an intravascular ultrasound system, which can optimize a noise reduction process and effectively reduce noise of interfering pixels in an ultrasound image.
To this end, the first aspect of the present disclosure provides an image noise reduction method for an intravascular ultrasound system, which is characterized in that an ultrasound image from an intravascular is acquired by moving an ultrasound probe of the intravascular ultrasound system along a rotation direction and a length direction of a blood vessel while rotating; traversing each pixel point in the ultrasonic image, and acquiring an original gray value of each pixel point; selecting a first pixel area comprising the pixel point, and calculating the average value of the original gray values of all the pixel points in the first pixel area as the average gray value of the first pixel area; comparing the original gray value of each pixel point with the average gray value of the first pixel area comprising the pixel point to obtain a comparison result, and marking an interference pixel point according to the comparison result; and traversing each interference pixel point in the ultrasonic image, selecting a second pixel area comprising the interference pixel point, calculating a median of original gray values of the pixel points in the second pixel area, and replacing the original gray value of the interference pixel point with the median, wherein the second pixel area comprises a specified pixel row which contains the interference pixel point and is along the rotation direction and a specified pixel column which contains the interference pixel point and is along the radial direction of the blood vessel.
In the first aspect of the disclosure, by comparing the original gray value of each pixel point in the ultrasound image with the average gray value of the first pixel region including the pixel point, the interference pixel point in the ultrasound image can be accurately identified, and the identified interference pixel point can be subjected to median filtering processing in a targeted manner to reduce noise caused by interference, so that noise reduction processing can be optimized and noise reduction can be effectively performed on the interference pixel point in the ultrasound image.
In the image noise reduction method according to the first aspect of the present disclosure, optionally, a comparison result between the original gray scale value of each pixel point and the average gray scale value of the first pixel region including the pixel point is obtained by calculating a ratio between the original gray scale value of each pixel point and the average gray scale value of the first pixel region including the pixel point. Therefore, the interference pixel points in the ultrasonic image can be accurately identified.
In the image noise reduction method according to the first aspect of the present disclosure, optionally, when the ratio is greater than a first predetermined threshold or smaller than a second predetermined threshold, the pixel is marked as the interference pixel, where the first predetermined threshold is greater than the second predetermined threshold. In this case, the ratio is set in the predetermined interval, so that the interference pixel points in the ultrasound image can be more conveniently identified.
In the image noise reduction method according to the first aspect of the present disclosure, optionally, when a first pixel region including the pixel point is selected, the pixel point is used as a center point of the first pixel region. Therefore, stronger correlation can be achieved between the pixel point and the average gray value of the first pixel region comprising the pixel point.
In the image noise reduction method according to the first aspect of the present disclosure, optionally, the number of the pixels in the predetermined pixel row is 3 to 9. Under the condition, the appropriate pixel row is selected by considering the disturbed pixel points of the ultrasonic image in the rotation direction, so that the noise of the ultrasonic image in the rotation direction can be effectively reduced.
In the image noise reduction method according to the first aspect of the present disclosure, optionally, the first pixel region is the same as the second pixel region. Under the condition, the average gray value and the median filtering of each interference pixel point are based on the same pixel region, so that the noise reduction processing can be more effectively carried out on the image.
In the image noise reduction method according to the first aspect of the present disclosure, optionally, when a pixel point of the ultrasound image is close to an edge and the first pixel region cannot be selected, the edge is used as a symmetry axis to perform mirror processing to select the first pixel region. Under the condition, the target pixel point and the mirror image pixel point are considered, so that a proper first pixel region can be selected when the pixel point is close to the edge.
In the image noise reduction method according to the first aspect of the present disclosure, optionally, when an interference pixel of the ultrasound image is close to an edge and the second pixel region cannot be selected, the edge is used as a symmetry axis to perform mirror processing to select the second pixel region. Under the condition, the target pixel point and the mirror image pixel point are considered, so that a proper second pixel area can be selected when the interference pixel point is close to the edge.
In the image noise reduction method according to the first aspect of the present disclosure, optionally, in the second pixel region, the interference pixel point is located in the center of the predetermined pixel row. Therefore, median filtering can be accurately carried out on each interference pixel point, and therefore noise reduction processing can be effectively carried out on the image.
A second aspect of the present disclosure provides an intravascular ultrasound system, which is characterized in that an ultrasound image acquired by intravascular ultrasound is subjected to noise reduction processing by using the image noise reduction method provided by the first aspect of the present disclosure.
According to the method and the device, the interference pixel points in the ultrasonic image can be accurately identified, and the identified interference pixel points can be subjected to median filtering processing in a targeted manner to reduce noise caused by interference, so that the noise reduction processing process can be optimized, and noise reduction can be effectively performed on the interference pixel points in the ultrasonic image.
Drawings
The disclosure will now be explained in further detail by way of example only with reference to the accompanying drawings, in which:
fig. 1 is a schematic diagram illustrating an application scenario of an intravascular ultrasound system according to an example of the present disclosure.
Fig. 2 is a schematic diagram illustrating an ultrasound probe according to an example of the present disclosure emitting an ultrasound sound beam within a blood vessel.
Fig. 3 is a block diagram schematic diagram illustrating an intravascular ultrasound system 1 according to an example of the present disclosure.
Fig. 4(a) is a real-time diagram example showing an ultrasound image according to an example of the present disclosure, and fig. 4(b) is an expanded view showing the real-time diagram example shown in fig. 4 (a).
Fig. 5(a) is a pixel point diagram showing an example of the live view shown in fig. 4(a), and fig. 5(b) is a pixel point diagram showing an expanded view shown in fig. 4 (b).
Fig. 6 is a flowchart illustrating an image noise reduction method according to an example of the present disclosure.
Fig. 7 is a schematic diagram showing an example of a first pixel region according to an example of the present disclosure.
Fig. 8 is a diagram illustrating an ultrasound image being mirrored to select a first pixel region according to an example of the present disclosure.
Fig. 9 is a schematic diagram showing an example of the second pixel region according to the example of the present disclosure.
Fig. 10 is a diagram illustrating an ultrasound image being mirrored to select a second pixel region according to an example of the present disclosure.
Description of reference numerals:
1 … intravascular ultrasound system, 10 … ultrasound probe, 20 … synthesis module, 30 … noise reduction module, 40 … display module, 2 … blood vessel, P1 … first pixel region, P2 … second pixel region.
Detailed Description
Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In the following description, the same components are denoted by the same reference numerals, and redundant description thereof is omitted. The drawings are schematic and the ratio of the dimensions of the components and the shapes of the components may be different from the actual ones.
It is noted that the terms "comprises," "comprising," and "having," and any variations thereof, in this disclosure, for example, a process, method, system, article, or apparatus that comprises or has a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include or have other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In addition, the headings and the like referred to in the following description of the present disclosure are not intended to limit the content or scope of the present disclosure, but merely serve as a reminder for reading. Such a subtitle should neither be understood as a content for segmenting an article, nor should the content under the subtitle be limited to only the scope of the subtitle.
The disclosure relates to an image noise reduction method of an intravascular ultrasound system, which can optimize noise reduction processing and effectively reduce noise of interference pixel points in an ultrasound image. The intravascular ultrasound system may be referred to as the IVUS system for short, and the image noise reduction method of the intravascular ultrasound system may be referred to as the image noise reduction method for short. The image denoising method is described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic diagram illustrating an application scenario of an intravascular ultrasound system 1 according to an example of the present disclosure. Fig. 2 is a schematic diagram illustrating an ultrasound probe 10 according to an example of the present disclosure emitting an ultrasound sound beam within a blood vessel 2. Fig. 3 is a block diagram schematic diagram illustrating an intravascular ultrasound system 1 according to an example of the present disclosure.
In the present embodiment, as shown in fig. 1, 2 and 3, the intravascular ultrasound system 1 may include an ultrasound probe 10, a synthesis module 20, a noise reduction module 30 and a display module 40. The ultrasound probe 10 may be rotated within the blood vessel 2 simultaneously or time-divisionally and/or translated along the length direction of the blood vessel 2, e.g. rotated in a rotational direction R as shown in fig. 2, translated in a length direction L as shown in fig. 2. Also, the ultrasound probe 10, when rotated or translated, may emit an ultrasound beam within the blood vessel 2 and may receive reflected waves of the ultrasound beam within the blood vessel 2, such as reflected waves of the ultrasound beam as it propagates within the blood vessel 2 to the vessel wall. Then, the synthesis module 20 may generate an ultrasound image for displaying the tissue structure and geometry of the blood vessel 2 based on the electrical signal formed by the reflected wave conversion. Then, the noise reduction module 30 may perform noise reduction processing on the ultrasound image by using the image noise reduction method according to the present disclosure, so as to reduce noise caused by pulse interference generated by a ventilator, for example, and effectively improve the image quality of the ultrasound image. Finally, a display module 40 may be used to conveniently display the ultrasound image.
In the present embodiment, the ultrasound probe 10 may convert an electric signal into an acoustic signal, or may convert an acoustic signal into an electric signal. In particular, the ultrasound probe 10 may convert an electrical signal of the intravascular ultrasound system 1 into an acoustic signal, thereby emitting an ultrasound beam within the blood vessel 2. The ultrasound probe 10 may also receive reflected waves of the ultrasound beam within the blood vessel 2 and convert the reflected waves into electrical signals, which the intravascular ultrasound system 1 may further process to generate the ultrasound image.
In addition, in some examples, the center frequency of the ultrasonic sound beam emitted by the ultrasonic probe 10 may be 20MHz to 60 MHz. In some examples, the center frequency of the ultrasonic sound beam emitted by the ultrasonic probe 10 may be preferably 40MHz to 60 MHz. Thereby, an ultrasound image can be acquired with high quality in the blood vessel 2.
Fig. 4(a) is a real-time diagram example showing an ultrasound image according to an example of the present disclosure, and fig. 4(b) is an expanded view showing the real-time diagram example shown in fig. 4 (a).
In some examples, as shown in fig. 4(a), a real-time image of the ultrasound image acquired by the ultrasound probe 10 in the blood vessel 2, for example, a real-time image displayed by the display module 40, may be a cross-sectional view of the blood vessel 2, as shown in the figure, the direction R is a rotation direction of the ultrasound probe 10 in the blood vessel 2, as shown in the figure, the direction D is a radial direction of the blood vessel 2, and the cross-sectional view includes information of a section (cross-section) of a wall and a lumen of the blood vessel 2. With this cross-sectional view, a doctor or the like can easily know information such as a cross-sectional shape and a blood flow pattern of the blood vessel 2. In addition, in some examples, to facilitate, for example, noise reduction processing on the cross-sectional view shown in fig. 4(a), the cross-sectional view shown in fig. 4(a) may be expanded to obtain an expanded view shown in fig. 4 (b).
Fig. 5(a) is a pixel point diagram showing an example of the live view shown in fig. 4(a), and fig. 5(b) is a pixel point diagram showing an expanded view shown in fig. 4 (b).
In some examples, as shown in fig. 5(a), the ultrasound image acquired by the ultrasound probe 10 within the blood vessel 2 may have a pixel row along the rotation direction of the ultrasound probe 10, for example, pixel point B1, pixel point B2, and pixel point B3 are formed along the rotation direction R as shown in fig. 5 (a). In addition, the ultrasound image may further have a pixel column along the radial direction of the blood vessel 2, for example, a pixel point B3, a pixel point B4, and a pixel point B5 are formed along the radial direction D as shown in fig. 5(a), wherein the radial direction of the blood vessel 2 refers to a direction that penetrates along one side of the wall of the blood vessel 2 to the opposite side. In addition, in some examples, the pixel schematic shown in fig. 5(a) may be expanded to obtain the pixel schematic shown in fig. 5(b), so as to facilitate, for example, noise reduction processing on the cross-sectional view shown in fig. 4 (a).
Fig. 6 is a flowchart illustrating an image noise reduction method according to an example of the present disclosure. Fig. 7 is a schematic diagram illustrating an example of the first pixel region P1 according to an example of the present disclosure. Fig. 8 is a schematic diagram illustrating the mirror processing of the ultrasound image to select the first pixel region P1 according to the example of the present disclosure. Fig. 9 is a schematic diagram illustrating an example of the second pixel region P2 according to an example of the present disclosure. Fig. 10 is a schematic diagram illustrating the mirroring process of the ultrasound image to select the second pixel region P2 according to the example of the present disclosure.
As shown in fig. 6, the image noise reduction method according to the present embodiment may include the steps of: traversing each pixel point in the ultrasonic image, and acquiring an original gray value of each pixel point (step S100); selecting any pixel point, selecting a first pixel region P1 including the pixel point, and calculating the average value of the original gray values of all the pixel points in the first pixel region P1 as the average gray value of the first pixel region P1 (step S200); comparing the original gray value of each pixel point with the average gray value of the first pixel region P1 including the pixel point to obtain a comparison result (step S300); marking interference pixel points in the ultrasonic image according to the comparison result (step S400); traversing each interference pixel point in the ultrasonic image, selecting a second pixel region P2 including the interference pixel point, and calculating the median of the original gray value of each pixel point in the second pixel region P2 (step S500); in step S500, the second pixel region P2 may include a specific pixel row along the rotation direction and a specific pixel row along the radial direction of the blood vessel and including the interference pixel point, and the original gray scale value of the interference pixel point is replaced by the median (step S600).
In the embodiment, by comparing the original gray value of each pixel point in the ultrasound image with the average gray value of the first pixel region P1 including the pixel point, the interference pixel point in the ultrasound image can be accurately identified, and the identified interference pixel point can be subjected to median filtering processing in a targeted manner to reduce noise generated due to pulse interference, so that the noise reduction processing process can be optimized and noise reduction can be effectively performed on the interference pixel point in the ultrasound image. The image denoising method of the embodiment is used for denoising the ultrasonic image, so that the quality of the ultrasonic image can be effectively improved, and doctors and the like can be helped to make more accurate diagnosis on patients.
In some examples, the intravascular ultrasound system 1 may use the above-mentioned image noise reduction method, i.e., through the above-mentioned steps S100 to S600, to implement the noise reduction processing on the ultrasound image.
(step S100)
In this embodiment, as described above, in step S100, each pixel point in the ultrasound image may be traversed, and the original gray value of each pixel point may be obtained.
In this embodiment, the ultrasound image is typically a grayscale image. The gray image refers to the fact that the luminance levels of R, G, B channels of each pixel point in the image are equal, for example, RGB (100, 100, 100), and the value "100" is the gray value of the pixel point. In addition, in this embodiment, the original gray value refers to the gray value of the pixel point of the ultrasound image without any filtering processing.
In some examples, when traversing each pixel point in the ultrasound image, the scanning may be performed row by row along a pixel row in the rotation direction, or may be performed column by column along a pixel column in the radial direction. Under the condition, the possibility of missing pixel points in the ultrasonic image can be effectively reduced through regular browsing.
In addition, in some examples, the number of the pixel points included in the ultrasound image may be obtained in advance before obtaining the original gray value of each pixel point. Under the condition, whether the pixel points in the ultrasonic image are omitted or not when the original gray value is obtained can be judged simply and conveniently by comparing the number of the pixel points with the number of the obtained original gray values.
In addition, in some examples, after obtaining the original gray scale value of each pixel point, the original gray scale value may be recorded on the corresponding pixel point. Therefore, the original gray value of each pixel point can be conveniently read.
Then, after recording the original gray value of each pixel point in the ultrasound image, step S200 is executed.
(step S200)
In this embodiment, as described above, in step S200, any one pixel point may be selected, the first pixel region P1 including the selected pixel point is selected, and the average of the original gray-scale values of the pixel points in the first pixel region P1 is calculated as the average gray-scale value of the first pixel region P1.
In this embodiment, as shown in fig. 7, any pixel point 700 is selected in the ultrasound image, and a first pixel region P1 including the pixel point is selected. Calculating the average value of the original gray values of the pixels in the first pixel region P1, that is, calculating the average values of the original gray values of the pixels 700, 701, 702, 703, 704, 705, 706, 707, and 708, and using the average values as the average gray value of the first pixel region P1.
In some examples, the first pixel region P1 may be the first pixel region P1, i.e., a 3 × 3 matrix of pixels, as shown in fig. 7. However, the example of the embodiment is not limited to this, and other manners may be used to select the first pixel region P1, such as a 3 × 5 pixel matrix, a 5 × 5 pixel matrix, a 7 × 7 pixel matrix, and the like.
In addition, in some examples, it is preferable that, as shown in fig. 7, when the first pixel region P1 including the selected pixel point is selected, the pixel point may be a center point of the first pixel region P1. In this case, a stronger correlation can be obtained between the selected pixel point and the average gray value of the first pixel region P1 including the pixel point, so that the interference pixel point in the ultrasound image can be more accurately marked.
Specifically, in the image noise reduction method according to the present disclosure, the original gray value of the selected pixel point is compared with the average gray value of the first pixel region P1 including the pixel point, and if the relative deviation between the original gray value and the average gray value is large, it can be determined that the pixel point is greatly influenced by the impulse interference. Based on this theory, the stronger the correlation between the selected pixel point and the average gray value of the first pixel region P1 including the pixel point, the more accurate the marking of the interfering pixel point. Thus, in the present embodiment, when the first pixel region P1 including the selected pixel point is selected, the pixel point is preferably set as the center point of the first pixel region P1.
In addition, in some examples, as shown in fig. 8, when the selected pixel point 800 is close to an edge of the ultrasound image and the first pixel region P1 cannot be selected, a mirror image process may be performed to select the first pixel region P1 with the edge as a symmetry axis. In this case, by considering the selected pixel point and the mirror pixel point, it is possible to select an appropriate first pixel region P1 (see fig. 8) when the pixel point is close to the edge of the ultrasound image.
The following describes in detail the selection of the first pixel region P1 when the pixel point is close to the edge of the ultrasound image with reference to fig. 8:
in some examples, as shown in fig. 8, when the selected pixel 800 is close to the edge of the ultrasound image, if the first pixel region P1 is selected in a 3 × 3 pixel matrix manner, the first pixel region P1 cannot be selected because the pixel regions around the pixel 800 are not enough to form the 3 × 3 pixel matrix. In this case, after the mirroring process is performed to reasonably enlarge the pixel area around the pixel 800, for example, with a-a' shown in fig. 8 as a symmetry axis, the pixel area around the pixel 800 is effectively and reasonably enlarged, and the mirrored pixel of the pixel 800 is 800 c. Thereby, an appropriate first pixel region P1 can be selected. However, the embodiment is not limited to this, and in other examples, when the selected pixel point is close to the edge of the ultrasound image, the first pixel region P1 may be selected based on the ultrasound image adjacent to the edge of the ultrasound image.
Next, after selecting a suitable first pixel region P1 based on each pixel point and calculating the average gray-scale value of the first pixel region P1, step S300 is performed.
(step S300)
In the present embodiment, as described above, in step S300, the original gray-scale value of each pixel point may be compared with the average gray-scale value of the first pixel region P1 including the pixel point to obtain a comparison result.
As described above, in the image noise reduction method according to the present embodiment, the original tone value of the selected pixel and the average tone value of the first pixel region P1 including the pixel are compared, and if the relative deviation between the original tone value of the selected pixel and the average tone value of the first pixel region P1 including the pixel is large, it can be determined that the pixel is greatly affected by impulse interference.
In some examples, the comparison result of the original gray value of each pixel point and the average gray value of the first pixel region P1 including the pixel point may be obtained by calculating a ratio of the original gray value of each pixel point and the average gray value of the first pixel region P1 including the pixel point. Thus, the relative deviation between the original gray scale value of each pixel point and the average gray scale value of the first pixel region P1 including the pixel point can be calculated easily.
In addition, in some examples, the original gray value of each pixel point and the average gray value of the first pixel region P1 including the pixel point may be compared by calculating a difference between the original gray value of each pixel point and the average gray value of the first pixel region P1 including the pixel point, and a comparison result is obtained. Thus, the absolute deviation between the original gradation value of the selected pixel and the average gradation value of the first pixel region P1 including the pixel can be calculated easily.
In the present embodiment, it is preferable that the comparison result is a ratio of the original tone value of each pixel to the average tone value of the first pixel region P1 including the pixel.
Next, after obtaining the comparison result between the original gray-scale value of the selected pixel point and the average gray-scale value of the first pixel region P1 including the pixel point, step S400 is executed.
(step S400)
In the present embodiment, as described above, in step S400, the interference pixel points in the ultrasound image may be marked according to the comparison result.
In some examples, when the ratio value representing the comparison result is greater than a first predetermined threshold or less than a second predetermined threshold, the pixel is marked as an interference pixel, wherein the first predetermined threshold is greater than the second predetermined threshold. In this case, the ratio is set in the predetermined interval, so that the interference pixel points in the ultrasound image can be more conveniently identified.
In other words, when the deviation of the original gray value of the selected pixel point compared with the average gray value of the first pixel region including the pixel point is large, that is, the ratio is greater than the first predetermined threshold or less than the second predetermined threshold, it can be determined that the pixel point is greatly affected by the impulse interference, and thus the pixel point can be marked as an interference pixel point.
In some examples, the interference pixels are noise that is typically caused by impulsive interference generated by various devices (e.g., ventilators, displays, etc.) disposed within the operating room, and such noise is often characterized by non-persistence, with frequencies on the order of the ultrasound operating frequency, often appearing as salt-and-pepper noise (impulsive noise) on the ultrasound images. Moreover, such impulse noise is apt to last 3-5 pixel points in the rotation direction of the ultrasonic probe 10, and last a plurality of pixel points in the radial direction of the blood vessel 2, which has a large influence on the gray scale. In consideration of the influence of impulse noise on the gray scale of the ultrasound image, in some examples, the first predetermined threshold may be 1.3, and the second predetermined threshold may be 0.7. For consideration of impulse noise selectivity, for example, considering processing impulse noise of different degrees, preferably, the first predetermined threshold may be 1.1, and the second predetermined threshold may be 0.9. Under the condition, the interference pixel points in the ultrasonic image can be more accurately identified by setting a reasonable preset interval, namely a specific deviation range.
Next, after the interference pixel points in the ultrasound image are marked, step S500 is executed.
(step S500)
In this embodiment, the step S500 may include traversing each interference pixel in the ultrasound image, selecting the second pixel region P2 including the interference pixel, and calculating a median of the original gray scale values of each pixel in the second pixel region P2.
In this embodiment, the second pixel region P2 may include a predetermined pixel row along the rotation direction including the interference pixel and a predetermined pixel column along the radial direction of the blood vessel including the interference pixel.
In addition, in the present embodiment, as shown in fig. 9, any interference pixel 900 is selected in the ultrasound image, and the second pixel region P2 including the interference pixel is selected. And calculating the median of the original gray values of all the pixel points in the second pixel region P2. That is, the original gray values of the pixel 900, the pixel 901, the pixel 902, the pixel 903, and the pixel 904 are sorted according to their sizes, and a middle value, i.e., a median value, is selected.
In some examples, as shown in fig. 9, the second pixel region P2 may be one pixel row including the selected interference pixel point. Preferably, in the second pixel region P2, the selected interference pixel point may be located at the center of the pixel row. Therefore, median filtering can be accurately carried out on each interference pixel point, and therefore noise reduction processing can be effectively carried out on the ultrasonic image.
Additionally, in some examples, the number of pixels specifying a row of pixels may be 3 to 9. Under the condition, pixels influenced by noise interference in the rotation direction of the ultrasonic image are considered to select a reasonable number of pixel points, so that the noise of the ultrasonic image formed in the rotation direction can be effectively reduced.
In clinical practice, in general, noise generated by an ultrasound image due to pulse interference can last for 3-5 pixel points in a rotation direction and can last for a plurality of pixel points in a radial direction. In this case, selecting the second pixel region P2 in the manner described in this example can effectively select a plurality of undisturbed or disturbed pixels, so that the calculated median value can be closer to the original gray value of the undisturbed or disturbed pixels.
In some examples, the second pixel region P2 may be the same as the first pixel region P1. Under the condition, the average gray value and the median filtering of each interference pixel point are based on the same pixel region, so that the noise reduction processing can be more effectively carried out on the ultrasonic image.
In addition, in some examples, the second pixel region P2 may be selected multiple times, and the second pixel region P2 selected last time is taken as a reference. Specifically, after the first selection of the second pixel region P2, the number of interfering pixels and the number of non-interfering pixels in the first selected second pixel region P2 are compared. If the number of the disturbing pixels is greater than or equal to the number of the non-disturbing pixels, the second pixel region P2 is selected for the second time, for example, the number of pixels in the predetermined pixel row is appropriately increased by extending the left and right ends in the rotation direction synchronously. And comparing the number of interference pixel points and the number of non-interference pixel points in the second selected second pixel region P2. And repeating the steps until the number of interference pixel points in the second pixel region P2 selected for the last time is smaller than the number of non-interference pixel points. The second pixel region P2 selected last is used as the pixel region to be processed in step S500.
In some examples, as described in step S200 with respect to selecting the first pixel region P1, as shown in fig. 10, when the selected interference pixel 1000 is close to the edge of the ultrasound image and the second pixel region P2 cannot be selected, a mirror process may be performed to select the second pixel region P2 with the edge as a symmetry axis. In this case, by considering the selected interference pixel point and the mirror image pixel point, an appropriate second pixel region P2 can be selected when the pixel point is close to the edge of the ultrasound image.
The following describes in detail the selection of the second pixel region P2 when the pixel point is close to the edge of the ultrasound image with reference to fig. 10:
specifically, as shown in fig. 10, when the selected interference pixel point 1000 is close to the edge of the ultrasound image, if the second pixel region P2 is selected in a manner of a specified pixel row including 5 pixel points, the second pixel region P2 cannot be selected because the pixel regions around the pixel point 1000 are not enough to form the specified pixel row including 5 pixel points. In this case, after the pixel region around the pixel 1000 is reasonably enlarged by the mirror image processing, for example, after the mirror image processing is performed with B-B' shown in fig. 10 as a symmetry axis, the pixel region around the pixel 1000 is effectively and reasonably enlarged, and the mirror image pixel of the pixel 1000 is 1000 c. Thereby, an appropriate second pixel region P2 can be selected. However, the embodiment is not limited to this, and in other examples, when the selected pixel point is close to the edge of the ultrasound image, the first pixel region P2 may be selected based on the ultrasound image adjacent to the edge of the ultrasound image.
After selecting a suitable second pixel region P2 based on each interference pixel point and calculating the median of the original gray scale values of each pixel point in the second pixel region P2, step S600 is executed.
(step S600)
In this embodiment, step S600 may include replacing the original gray scale value of the interference pixel with the median value calculated in step S500.
In this embodiment, by selecting a suitable second pixel region P2 including the selected interference pixel, the median of the original gray scale values of the respective pixels in the second pixel region P2 is calculated, and the median is used to replace the original gray scale value of the interference pixel. Therefore, noise generated by interference pixel points due to pulse interference can be effectively reduced.
Specifically, because the interference pixel point in the second pixel region P2 is compared with other pixel points in the second pixel region P2, especially the pixel points that are not interfered or are less interfered, the original gray value of the interference pixel point has a larger deviation. Therefore, when the original gray values of the pixels in the second pixel region P2 are sorted according to size, the original gray values of the interference pixels are not likely to appear at the middle position. The original gray value at the middle position belongs to a certain pixel point which is not interfered or is interfered less, and compared with the interference pixel point, the original gray value is more suitable for representing effective image information in an ultrasonic image.
In the present embodiment, after step S600 is completed, the image noise reduction method according to the present disclosure completes the noise reduction process on the ultrasound image.
In the present embodiment, the noise reduction processing on the ultrasound image is not limited to the above-described steps S100 to S600. In some examples, step S410 may also be included between step S400 and step S500. Step S410 may include obtaining the number of interference pixel points in the ultrasound image, and comparing the number of pixel points in the ultrasound image with the number of interference pixel points, and if the number of interference pixel points in the ultrasound image occupies a higher proportion in the number of pixel points in the ultrasound image, that is, the interference proportion is higher, discarding the ultrasound image of the frame.
In particular, in general, the interference pixels in the ultrasound image do not accurately represent the actual information in the blood vessel 2. In this case, if there are many interference pixels in the ultrasound image, the information indicated by the ultrasound image and the actual information of the blood vessel 2 come in and go out greatly, and a doctor or the like cannot make a diagnosis and treatment of the patient well based on the ultrasound image.
In addition, in some examples, if the interference proportion of the ultrasound image reaches 50% or more, the frame of ultrasound image is discarded. In other examples, the frame of ultrasound images is discarded if the interference proportion of the ultrasound images reaches 30% or more, taking into account the diagnostic rigor.
Additionally, in some examples, step S510 may also be included between step S500 and step S600. Step S510 includes selecting any interference pixel, and calculating the original gray value of the interference pixel, and the relative deviation between the median of the second pixel region P2 including the interference pixel and the average gray value of the first pixel region P1 including the interference pixel. If the relative deviation between the median of the second pixel region P2 including the interference pixel and the average gray value of the first pixel region P1 including the interference pixel is larger, the average gray value of the first pixel region P1 including the interference pixel is used to replace the original gray value of the interference pixel.
Specifically, when the relative deviation between the median value of the second pixel region P2 including the interference pixel point and the average gray scale value of the first pixel region P1 including the interference pixel point is larger, the median value is not suitable for representing valid image information in the ultrasound image.
In addition, in the present embodiment, in steps S100 to S600, when the noise reduction processing is performed on the ultrasound image using the image noise reduction method according to the present disclosure, only one frame of the ultrasound image is required to be a processing target. In this case, the noise reduction module 30 for noise reduction processing in the intravascular ultrasound system 1 can complete noise reduction processing on the ultrasound image without providing a buffer device. Thereby, the system structure of the intravascular ultrasound system 1 can be effectively optimized.
In addition, in the present embodiment, when the noise reduction processing is performed on the ultrasound image by using the image noise reduction method according to the present disclosure, only the interference pixel points in the ultrasound image are subjected to the noise reduction processing. This enables optimization of the noise reduction processing.
According to the method and the device, the interference pixel points in the ultrasonic image can be accurately identified, and the identified interference pixel points can be subjected to median filtering processing in a targeted manner to reduce noise caused by interference, so that noise reduction processing can be optimized and noise reduction can be effectively performed on the interference pixel points in the ultrasonic image.
While the present disclosure has been described in detail in connection with the drawings and examples, it should be understood that the above description is not intended to limit the disclosure in any way. Those skilled in the art can make modifications and variations to the present disclosure as needed without departing from the true spirit and scope of the disclosure, which fall within the scope of the disclosure.
Various examples of the present disclosure are described above in the detailed description. Although the description directly describes the above examples, it is to be understood that modifications and/or variations to the specific examples shown and described herein may occur to those skilled in the art. Any such modifications and/or variations that fall within the scope of the present description are also included therein. It is the intention of the inventors that the words and phrases in the specification and claims be given the ordinary and customary meaning to the skilled artisan, unless otherwise indicated.
Claims (10)
1. A noise reduction module for noise reduction processing of ultrasound images, is a noise reduction module included in an intravascular ultrasound system,
the ultrasonic probe of the intravascular ultrasonic system moves along the rotation direction and the length direction of the blood vessel under the condition of rotation to acquire an ultrasonic image from the blood vessel; the noise reduction module is used for carrying out noise reduction processing on the ultrasonic image;
the method for the noise reduction module to perform noise reduction processing on the ultrasonic image comprises the following steps: selecting any pixel point in the ultrasonic image, selecting a first pixel region comprising the pixel point, and calculating the average value of the original gray values of all the pixel points in the first pixel region to be used as the average gray value of the first pixel region; comparing the original gray value of each pixel point with the average gray value of a first pixel area comprising the pixel point to obtain a comparison result, and marking an interference pixel point according to the comparison result; traversing each interference pixel point in the ultrasonic image and acquiring the total number of the marked interference pixel points, calculating the ratio of the total number of the marked interference pixel points to the total number of the pixel points in the ultrasonic image, if the ratio is smaller than a preset value, selecting any interference pixel point in the ultrasonic image and selecting a second pixel region comprising the interference pixel point, respectively calculating the original gray value of the selected interference pixel point, the relative deviation between the median value of the original gray value of each pixel point in the second pixel region and the average gray value of the first pixel region comprising the selected interference pixel point, if the relative deviation between the median value and the average gray value is smaller, replacing the original gray value of the selected interference pixel point by the median value, if the relative deviation between the median value and the average gray value is larger, the average gray value is used to replace the original gray value of the selected interference pixel.
2. The noise reduction module of claim 1, wherein:
the ultrasound probe converts an electrical signal into an acoustic signal to emit the ultrasound beam within a blood vessel, and the ultrasound probe receives a reflected wave of the ultrasound beam within the blood vessel and converts the reflected wave into an electrical signal to acquire the ultrasound image from within the blood vessel.
3. The noise reduction module of claim 2, wherein:
the center frequency of the ultrasonic sound beam emitted by the ultrasonic probe is 20MHz to 60 MHz.
4. The noise reduction module of claim 1, wherein:
the second pixel region includes a prescribed pixel row along the rotation direction containing the selected interference pixel and a prescribed pixel column along the radial direction of the blood vessel containing the selected interference pixel.
5. The noise reduction module of claim 4, wherein:
the number of the pixels of the prescribed pixel row is 3 to 9.
6. The noise reduction module of claim 1, wherein:
and obtaining the comparison result of the original gray value of each pixel point and the average gray value of the first pixel area comprising the pixel point by calculating the ratio of the original gray value of each pixel point to the average gray value of the first pixel area comprising the pixel point.
7. The noise reduction module of claim 6, wherein:
if the ratio is larger than a first preset threshold or smaller than a second preset threshold, the pixel is marked as an interference pixel, wherein the first preset threshold is larger than the second preset threshold.
8. The noise reduction module of claim 7, wherein:
the first predetermined threshold is 1.1 or 1.3 and the second predetermined threshold is 0.7 or 0.9.
9. The noise reduction module of claim 1, wherein:
the first pixel region is the same as the second pixel region.
10. The noise reduction module of claim 1, wherein:
the preset value is 30% or 50%.
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