CN117557486B - Neural anesthesia puncture auxiliary positioning method based on ultrasonic image - Google Patents
Neural anesthesia puncture auxiliary positioning method based on ultrasonic image Download PDFInfo
- Publication number
- CN117557486B CN117557486B CN202410038245.7A CN202410038245A CN117557486B CN 117557486 B CN117557486 B CN 117557486B CN 202410038245 A CN202410038245 A CN 202410038245A CN 117557486 B CN117557486 B CN 117557486B
- Authority
- CN
- China
- Prior art keywords
- image
- image block
- gray level
- ultrasonic
- sequence
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 60
- 206010002091 Anaesthesia Diseases 0.000 title claims abstract description 30
- 230000037005 anaesthesia Effects 0.000 title claims abstract description 30
- 230000001537 neural effect Effects 0.000 title abstract description 8
- 238000001914 filtration Methods 0.000 claims abstract description 27
- 238000005070 sampling Methods 0.000 claims abstract description 12
- 230000003444 anaesthetic effect Effects 0.000 claims abstract description 7
- 230000000875 corresponding effect Effects 0.000 claims description 83
- 241001270131 Agaricus moelleri Species 0.000 claims description 15
- 210000005036 nerve Anatomy 0.000 claims description 11
- 238000010606 normalization Methods 0.000 claims description 11
- 238000002604 ultrasonography Methods 0.000 claims description 11
- 230000002596 correlated effect Effects 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 7
- 238000012163 sequencing technique Methods 0.000 claims description 6
- 238000003384 imaging method Methods 0.000 claims description 4
- 238000013507 mapping Methods 0.000 claims description 3
- 230000004807 localization Effects 0.000 claims 1
- 238000004364 calculation method Methods 0.000 abstract description 5
- 230000000694 effects Effects 0.000 description 5
- 206010044565 Tremor Diseases 0.000 description 4
- 210000004204 blood vessel Anatomy 0.000 description 3
- 238000000605 extraction Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000037081 physical activity Effects 0.000 description 1
- 230000001766 physiological effect Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/40—Image enhancement or restoration using histogram techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10132—Ultrasound image
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Ultra Sonic Daignosis Equipment (AREA)
Abstract
The invention relates to the technical field of image enhancement, in particular to a neural anesthesia puncture auxiliary positioning method based on an ultrasonic image. The method comprises the steps of respectively obtaining ultrasonic images in an unpuncture stage and a puncture stage, and obtaining the gray level dynamic change degree of each image block through the gray level fluctuation and the gray level distribution dynamic change of each image block of the ultrasonic image in the unpuncture stage; after each time of ultrasonic image acquisition in the time sequence calculation puncture stage, the gray level dynamic change degree of each image block is ordered under the whole ultrasonic image to obtain a time sequence change sequence; obtaining variation significance according to the variation trend of the gray level dynamic variation degree in the time sequence variation sequence, sampling non-local mean value filtering based on the difference condition between the variation significance, and obtaining the enhanced puncture ultrasonic image for auxiliary positioning. According to the invention, the similarity between tissues of the anesthetic region is analyzed from the angle of time sequence change, so that a high-quality enhanced image is obtained, and the reliability of accurate positioning of the subsequent puncture position is further improved.
Description
Technical Field
The invention relates to the technical field of image enhancement, in particular to a neural anesthesia puncture auxiliary positioning method based on an ultrasonic image.
Background
The ultrasonic imaging can dynamically display the tissue anatomical structures of the anesthesia region such as nerves and accompanying blood vessels of the target part in real time, effectively guide the direction and depth of the anesthesia puncture needle, realize accurate anesthesia and reduce the occurrence of anesthesia complications, so that the imaging quality of the ultrasonic imaging of the anesthesia region is an important factor related to the accurate positioning of the anesthesia puncture.
Non-local mean filtering is one of conventional techniques for implementing ultrasound image filtering enhancement, which can implement ultrasound image filtering enhancement effects according to similarity relationships between image blocks on an acquired ultrasound image. However, due to arterial blood vessels and regional nerve tremors during anesthesia puncture, the development of a puncture needle in an acquired real-time ultrasonic image can be changed, so that the puncture direction and depth position are difficult to accurately position, and due to the fact that different regional tremors in the ultrasonic image are different, the similarity weight of an image block is enhanced by only adopting mean square error as non-local mean value filtering, the filtered and enhanced image is difficult to effectively distinguish the position information of the puncture needle under different regional change characteristics, the obtained enhanced image is poor in quality, and the accurate positioning of the subsequent puncture position cannot be realized.
Disclosure of Invention
In order to solve the technical problems that the enhanced image quality obtained in the prior art is poor and the accurate positioning of the subsequent puncture position cannot be realized, the invention aims to provide an ultrasonic image-based neural anesthesia puncture auxiliary positioning method, which adopts the following technical scheme:
the invention provides a neural anesthesia puncture auxiliary positioning method based on ultrasonic images, which comprises the following steps:
Acquiring more than two ultrasonic images in an unpunctured stage and an ultrasonic image corresponding to each sampling time before the current time in a puncturing stage in an anesthetic region; dividing each ultrasonic image into more than two image blocks; taking all ultrasonic images in the unpunctured stage as an image group;
For all ultrasonic images in the image group, according to the fluctuation condition of gray distribution of each image block under each ultrasonic image and the deviation degree between the gray distribution condition of the corresponding image block in each ultrasonic image and the gray distribution condition in the whole ultrasonic image of the image group, obtaining the gray dynamic change degree of each image block corresponding to the image group;
Sequentially adding the ultrasonic images corresponding to each sampling time before the current time in the puncture stage into an image group according to a time sequence, and acquiring the gray level dynamic change degree of each image block corresponding to the image group once after adding one ultrasonic image; sequencing all gray level dynamic change degrees corresponding to each image block according to a time sequence order to obtain a time sequence change sequence corresponding to each image block at the current time; in the time sequence change sequence of each image block at the current time, according to the change trend condition of the gray level dynamic change degree, obtaining the change significance of each image block at the current time;
obtaining the similarity weight between two image blocks at the current time according to the difference condition of the change significance between the two image blocks at the current time; and acquiring an enhanced puncture ultrasonic image at the current time by adopting non-local mean filtering based on the similarity weight corresponding to the current time, and performing auxiliary positioning.
Further, the method for acquiring the gray level dynamic change degree comprises the following steps:
Taking each image block as a target image block in sequence, and acquiring a gray histogram of the target image block under any ultrasonic image in the image group; according to the distribution condition of each gray level on the gray level histogram, a gray level distribution sequence and a gray level fluctuation index of the target image block under the ultrasonic image are obtained;
According to the gray level distribution sequence of the target image block under each ultrasonic image in the image group, a gray level distribution mean value sequence of the target image block is obtained;
Calculating a DTW value between a gray level distribution sequence and a gray level distribution mean value sequence of the target image block under each ultrasonic image, and obtaining a change deviation index of the target image block under each ultrasonic image;
Calculating the product of the gray scale fluctuation index and the change deviation index of the target image block under each ultrasonic image to obtain the gray scale change index of the target image block under each ultrasonic image; and taking the average value of the gray level change indexes of the target image block under all ultrasonic images as the gray level dynamic change degree of the target image block.
Further, the step of obtaining the gray level distribution sequence and the gray level fluctuation index of the target image block under the ultrasonic image according to the distribution condition of each gray level on the gray level histogram comprises the following steps:
Acquiring the distribution frequency of all gray scales in a gray histogram of a target image block corresponding to the ultrasonic image; arranging the distribution frequencies of all the gray scales in the target image block according to the order from small gray scales to large gray scales to obtain a gray scale distribution sequence of the target image block;
taking the maximum value of the distribution frequency of the target image block in the gray level distribution sequence under the ultrasonic image as the maximum fluctuation value; taking the minimum value of non-zero distribution frequency in a gray level distribution sequence of a target image block under the ultrasonic image as a minimum fluctuation value; and taking the difference value of the maximum fluctuation value and the minimum fluctuation value as a gray scale fluctuation index of the target image block under the ultrasonic image.
Further, the obtaining the distribution frequency of all gray scales in the gray scale histogram of the target image block corresponding to the ultrasonic image includes:
When the gray level has the occurrence frequency in the gray level histogram of the target image block, marking the occurrence frequency as the distribution frequency of the corresponding gray level; when the gray level does not have the occurrence frequency in the gray histogram of the target image block, the distribution frequency of the corresponding gray level is recorded as zero.
Further, the method for acquiring the gray distribution mean value sequence comprises the following steps:
Acquiring a sequence number of each distribution frequency in a gray level distribution sequence of a target image block under each ultrasonic image; when non-zero distribution frequencies exist in the distribution frequencies corresponding to the serial numbers, taking the average value of all the non-zero distribution frequencies corresponding to the corresponding serial numbers as the average value of the distribution frequencies of the corresponding serial numbers; when the distribution frequency corresponding to the sequence number does not have non-zero distribution frequency, marking the average value of the distribution frequency corresponding to the sequence number as zero;
And sequencing all the distribution frequency mean values according to the sequence from small to large to obtain a gray level distribution mean value sequence of the target image block.
Further, the method for obtaining the change saliency comprises the following steps:
For any image block, calculating the difference between the dynamic change degrees of every two adjacent gray scales in the corresponding time sequence change sequence of the image block at the current time to obtain the change difference corresponding to the image block; taking the average value of all variation differences of the image block as the time sequence variation average value of the image block; taking the maximum value of the variation difference of the image block as a significant value of the image block;
Obtaining the change saliency of the image block at the current time according to the saliency value and the time sequence change mean value of the image block; the significant value and the variation significant value are positively correlated, the time sequence variation mean value and the variation significant value are negatively correlated, and the variation significant value is a value subjected to normalization processing.
Further, the method for obtaining the similarity weight comprises the following steps:
And carrying out negative correlation mapping and normalization processing on the difference of the change significance of any two image blocks at the current moment to obtain the similarity weight between the corresponding any two image blocks.
Further, the dividing each ultrasound image into more than two image blocks includes:
uniformly dividing each ultrasonic image into a preset number of image blocks; each ultrasonic image dividing method is the same and the preset number is more than or equal to 2.
Further, the preset number is set to 50.
Further, the total number of gray levels is 256.
The invention has the following beneficial effects:
according to the invention, the ultrasonic images in the unpunctured stage and the puncturing stage are respectively obtained, and the stable gray level dynamic change degree of each image block in the unpunctured stage is obtained through the gray level fluctuation and the gray level distribution dynamic change condition of each image block in the ultrasonic image in the unpunctured stage, so that the gray level dynamic change characteristics of different area images in the anesthesia area in the range of the image block are represented. Further, considering the influence of the puncture motion characteristic of the puncture needle head on the original change characteristic in the puncture stage, calculating the gray level dynamic change degree of each image block under the whole ultrasonic image after the ultrasonic image is acquired each time in the puncture stage on the time sequence, and further obtaining the time sequence change sequence of each image block consisting of the gray level dynamic change degrees at the current time. Because the gray level dynamic change degree of the image blocks can be changed to a certain extent in time sequence in the puncturing process, the change significance is obtained according to the change trend of the gray level dynamic change degree in the time sequence change sequence, the significance of the time sequence dynamic change influenced by the occurrence of the puncture needle is reflected, and then the similarity weight is obtained through the difference condition of the change significance between the image blocks, and can be used for optimizing non-local mean filtering. And finally, based on the similarity weight, adopting non-local mean filtering to realize the enhanced auxiliary puncture needle positioning of the ultrasonic image at the current moment. According to the invention, through the range of gray level dynamic change on time sequence, the similarity characteristics of the tissue in the anesthesia area are analyzed from the angle of the time sequence change characteristics, the robustness and the enhancement effect of filtering after the appearance of the puncture needle are improved, a high-quality enhanced ultrasonic image is obtained, and the reliability of the accurate positioning of the subsequent puncture position is further improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for assisting in positioning a nerve anesthesia puncture based on an ultrasonic image according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following description refers to the specific implementation, structure, characteristics and effects of the neural anesthesia puncture auxiliary positioning method based on the ultrasonic image according to the invention in combination with the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The invention provides a specific scheme of a neural anesthesia puncture auxiliary positioning method based on ultrasonic images, which is specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a method for assisting in positioning a nerve anesthesia puncture based on an ultrasonic image according to an embodiment of the invention is shown, the method comprises the following steps:
s1: acquiring more than two ultrasonic images in an unpunctured stage and an ultrasonic image corresponding to each sampling time before the current time in a puncturing stage in an anesthetic region; dividing each ultrasonic image into more than two image blocks; all ultrasound images in the unpunctured stage are taken as one image set.
Because the body tissue structure moves along with the physiological activities of the body, partial arterial blood vessels and tissues existing in the anesthetic region generate regional nerve tremors, so that the similarity between different tissue regions of a single ultrasonic image is difficult to analyze, and therefore, multiple ultrasonic images in time sequence are required to analyze the dynamic change characteristics of different regions. In the embodiment of the invention, the ultrasonic image of the anesthetic region is acquired by using the ultrasonic detection equipment, and the ultrasonic image is a gray image after being generated, wherein the ultrasonic image acquisition of the anesthetic region is divided into two stages, namely an unpuncture stage without puncture and a puncture stage in the puncture process.
The change characteristics of relatively stable change of each region can be obtained through the ultrasonic image in the non-puncture stage, and further the influence of movement change caused by movement of a puncture needle can be conveniently analyzed in the puncture process, so that more than two ultrasonic images in the non-puncture stage are obtained, the change characteristics are conveniently analyzed, the ultrasonic image corresponding to each sampling time before the current time in the puncture stage is obtained, the ultrasonic image in the real-time puncture process is enhanced, in the embodiment of the invention, the sampling time interval is set to be 0.5 seconds, a specific numerical value implementer can regulate and control by himself, and a plurality of ultrasonic images are obtained for analysis in the two stages based on the sampling time.
Because the variation conditions of different areas are inconsistent, in order to facilitate the analysis of different tissue areas respectively, each ultrasonic image is divided into more than two image blocks. Preferably, each ultrasonic image is uniformly divided into a preset number of image blocks, the method for dividing each ultrasonic image is the same, the preset number is greater than or equal to 2, accurate analysis of a plurality of ultrasonic images among different area positions is guaranteed, in the embodiment of the invention, the preset number is set to be 50, and a specific numerical value implementer can adjust according to specific implementation conditions. It should be noted that, the dividing method preferably selects uniform division, and equally divides according to the edge size of the image, when the average division cannot be performed, the greatest average division condition of the edge size of the image is taken, and the rest is used as an image block, for example, when the number of divided image blocks is 10 and the edge size of the image is 100, 3×3 image blocks with the edge length of 33 are preferably equally divided, and the rest area is used as an image block, and other dividing methods are not limited herein, and only the consistency of the dividing process method of each ultrasonic image needs to be ensured.
In order to facilitate analysis of the self-stable changes of different image blocks, all ultrasonic images in the unpunctured stage are taken as an image group, and the whole image group is analyzed, so that the motion change condition of each image block caused by physical activity under the condition that the motion interference of a puncture needle does not exist can be obtained.
S2: and for all the ultrasonic images in the image group, obtaining the gray level dynamic change degree of each image block corresponding to the image group according to the fluctuation condition of the gray level distribution of each image block under each ultrasonic image and the deviation degree of the gray level distribution condition of the corresponding image block in each ultrasonic image and the whole ultrasonic image in the image group.
When different areas have movements such as trembling, the gray information distribution of pixel points in the ultrasonic images also presents area variation, a stable area gray dynamic variation range is often presented on a plurality of ultrasonic images in an unpunctured stage according to the area variation characteristics, and the variation range tends to be stable along with the increase of analysis ultrasonic images, because all ultrasonic images are analyzed in the unpunctured stage, and the gray dynamic variation degree of each image block corresponding to the image group is obtained according to the fluctuation condition of gray distribution of each image block under each ultrasonic image and the deviation degree of the corresponding image block between the gray distribution condition of each ultrasonic image and the gray distribution condition of the whole ultrasonic image in the image group.
Preferably, each image block is sequentially taken as a target image block, the same analysis is carried out on each image block, the gray histogram of the target image block under the ultrasonic image is obtained for any ultrasonic image in the image group, and the gray distribution characteristics of the target image block under the ultrasonic image are reflected through the gray histogram. In the embodiment of the present invention, the abscissa of the gray histogram is the gray level, the ordinate is the occurrence frequency corresponding to the gray level, and the method for obtaining the gray histogram is a technical means well known to those skilled in the art, which is not described herein.
Further, according to the distribution condition of each gray level on the gray level histogram, a gray level distribution sequence and a gray level fluctuation index of the target image block under the ultrasonic image are obtained, and in the embodiment of the invention, the gray level is the size of a gray level value, the gray level range is between 0 and 255, and 256 gray levels are all obtained.
Preferably, the distribution frequency of all gray levels in the gray level histogram of the target image block corresponding to the ultrasonic image, that is, 256 gray levels, is obtained, and each gray level obtains a distribution frequency. In one embodiment of the present invention, when the gray level has an occurrence frequency in the gray level histogram of the target image block, the occurrence frequency is recorded as a distribution frequency of the corresponding gray level, and when the gray level does not have an occurrence frequency in the gray level histogram of the target image block, the distribution frequency of the corresponding gray level is recorded as zero, so as to ensure that each gray level obtains the distribution frequency.
Further, the distribution frequencies of all the gray scales in the target image block are arranged in the order from the small gray scales to the large gray scales, and a gray scale distribution sequence of the target image block is obtained, that is, the number of elements in all the gray scale distribution sequence is 256. And the maximum value of the distribution frequency of the target image block in the gray level distribution sequence under the ultrasonic image is taken as the maximum fluctuation value, the minimum value of the non-zero distribution frequency of the target image block in the gray level distribution sequence under the ultrasonic image is taken as the minimum fluctuation value, and the distribution frequency is zero, so that the corresponding gray level value does not appear in the target image block, the minimum value of the non-zero distribution frequency is taken as the minimum value of the gray level fluctuation condition, the difference value of the maximum fluctuation value and the minimum fluctuation value is taken as the gray level fluctuation index of the target image block under the ultrasonic image, and the gray level distribution variation range of the target image block in the ultrasonic image is reflected by the gray level fluctuation index.
Further, according to the gray distribution sequence of the target image block under each ultrasonic image in the image group, a gray distribution average value sequence of the target image block is obtained, preferably, a serial number of each distribution frequency in the gray distribution sequence of the target image block under each ultrasonic image is obtained, each serial number corresponds to each gray level, when a non-zero distribution frequency exists in the distribution frequency corresponding to the serial number, the change of gray distribution exists in the same gray level is indicated, and the average value of all the non-zero distribution frequencies corresponding to the corresponding serial number is taken as the distribution frequency average value of the corresponding serial number. When the distribution frequency corresponding to the serial number does not have non-zero distribution frequency, the fact that the corresponding gray level has no change of gray distribution all the time is indicated, and the average value of the distribution frequency corresponding to the serial number is recorded as zero. And sequencing all the distribution frequency mean values according to the sequence from the small sequence to the large sequence to obtain a gray distribution mean value sequence of the target image block, and reflecting the gray distribution condition of the target image block in the whole ultrasonic image of the image group through the gray distribution mean value sequence.
For example, when the gray level is 2, the number of the gray distribution sequence is 3, if the number of the corresponding distribution frequency of the number 3 under each ultrasonic image is 5,6,0,7,6, all the non-zero distribution frequencies are subjected to average calculation at this time, and the situation of 0 is excluded, so that the overall trend of gray information change is ensured to be calculated, that is, when the number of the distribution frequency is 3, the average value is calculated, the non-zero distribution frequency 5,6,7,6 is calculated, and the corresponding obtained distribution frequency average value is 6. When the gray level is 0, the number 1 in the gray level distribution sequence indicates that the gray level is 0 when the corresponding distribution frequency of the number 1 under all ultrasonic images is 0, so that the distribution frequency corresponding to the number 1 is marked as zero at the moment, and the situation that the whole gray information is not changed is represented.
Further, calculating a DTW value between a gray level distribution sequence and a gray level distribution mean value sequence of the target image block under each ultrasonic image to obtain a change deviation index of the target image block under each ultrasonic image, wherein the larger the change deviation index is, the more dissimilar the gray level distribution sequence and the gray level distribution mean value sequence under the corresponding ultrasonic image is, and the larger the deviation is. It should be noted that, the DTW value is calculated by using the dynamic time warping algorithm (DYNAMIC TIME WARPING, DTW), which reflects the similarity between the two sequences, and when the DTW value is smaller, the similarity between the two sequences is indicated, and the method for calculating the DTW value by using the dynamic time warping algorithm is a technical means known to those skilled in the art, which is not described herein.
Further, calculating the product of the gray level fluctuation index and the change deviation index of the target image block under each ultrasonic image to obtain the gray level change index of the target image block under each ultrasonic image, integrating the deviation condition and the distribution change range of gray level distribution, reflecting the change degree of the gray level distribution of the target image block under each ultrasonic image, and indicating that the change degree of the gray level distribution of the target image block under the corresponding ultrasonic image is larger when the gray level change index is larger.
And finally, taking the average value of the gray level change indexes of the target image block under all ultrasonic images as the gray level dynamic change degree of the target image block, and reflecting the dynamic change degree of the target image block in the image group at the moment through the gray level dynamic change degree. In the embodiment of the invention, the expression of the gray level dynamic change degree is as follows:
In the method, in the process of the invention, Expressed as/>The degree of gray level dynamic change of each image block,/>Expressed as the total number of ultrasound images in the image group,/>Expressed as/>The image block is at the/>Gray scale distribution sequence under ultrasonic image,/>Expressed as/>Gray level distribution average value sequence of individual image blocks,/>Expressed as/>The image block is at the/>Maximum fluctuation value in gray level distribution sequence under ultrasonic image/>Expressed as/>The image block is at the/>Minimum fluctuation value in gray level distribution sequence under ultrasonic image/>Represented as a DTW value calculation function.
Wherein,Expressed as/>The image block is at the/>Gray scale fluctuation index under ultrasonic image,/>Expressed as/>The image block is at the/>The variation deviation index under the individual ultrasound images,Expressed as/>The image block is at the/>Gray scale change index under each ultrasound image. When the gray scale variation amplitude of the image block in all the ultrasonic images is larger and the gray scale distribution range span is larger, the gray scale distribution dynamic variation degree is larger, and the gray scale dynamic variation degree is larger.
And (3) completing analysis of the gray level distribution information change conditions of different areas in the unpunctured stage through the acquisition of the gray level dynamic change degree.
S3: sequentially adding the ultrasonic images corresponding to each sampling time before the current time in the puncture stage into an image group according to a time sequence, and acquiring the gray level dynamic change degree of each image block corresponding to the image group once after adding one ultrasonic image; sequencing all gray level dynamic change degrees corresponding to each image block according to a time sequence order to obtain a time sequence change sequence corresponding to each image block at the current time; and in the time sequence change sequence of each image block at the current time, according to the change trend condition of the gray level dynamic change degree, obtaining the change significance of each image block at the current time.
When the puncture needle performs a puncture stage, as the puncture needle appears in the image, the area influenced by the puncture needle can greatly change the gray level dynamic change degree, so that the gray level dynamic change of the time sequence is more obvious, and therefore, the change condition of the gray level dynamic change degree of each image block on the time sequence is analyzed, the change significance of each image block can be found, the more obvious the change is, and the more complicated the movement condition in the corresponding area of the image block is indicated.
Therefore, in order to analyze the change condition of the gray level dynamic change degree on the time sequence in the puncture stage, the ultrasonic images corresponding to each sampling time before the current time in the puncture stage are sequentially added into the image group according to the time sequence, and the gray level dynamic change degree of each image block corresponding to the image group at the moment is obtained once after each ultrasonic image is added. Because the ultrasonic images corresponding to each sampling time are added one by one in time sequence, after each ultrasonic image is added, the ultrasonic images in the image group are updated, and according to the acquisition process of the gray level dynamic change degree in the step S2, the gray level dynamic change degree of the image block is calculated for each updated image group until the current time, and each image block corresponds to a plurality of gray level dynamic change degrees. For example, taking the image group of the unpunctured stage as the initial image group, the ultrasound images of the puncture stages sequenced according to the time sequence before the current moment are respectively: image 1, image 2, image 3, image 4 and image 5, the resulting image set is in order: { initial image group, image 1} { initial image group, image 1, image 2, image 3, image 4} and { initial image group, image 1, image 2, image 3, image 4, image 5} are five groups, each of which enables each image block to acquire a gray level dynamic change degree once.
Further, in order to analyze the change condition of gray level distribution on time sequence, all gray level dynamic change degrees corresponding to each image block are ordered according to the time sequence order, so that a time sequence change sequence corresponding to each image block at the current time is obtained, and the time sequence change sequence can completely reflect the change condition of the gray level dynamic change degree corresponding to each image block along with time, so that in the time sequence change sequence of each image block at the current time, the change significance of each image block at the current time is obtained according to the change trend condition of the gray level dynamic change degree.
Preferably, for any one image block, each image block performs the same analysis, calculates the difference between every two adjacent gray level dynamic change degrees in the corresponding time sequence change sequence of the image block at the current time, obtains the corresponding change difference of the image block, and reflects the influence degree of the change difference on the gray level dynamic change degree of the image block part after the puncture needle appears. Taking the average value of all the variation differences of the image block as the time sequence variation average value of the image block, reflecting the variation condition of the whole gray level dynamic variation degree through the time sequence variation average value, taking the maximum value of the variation differences of the image block as the significant value of the image block, wherein the significant value reflects the abrupt change degree of the gray level dynamic variation degree, and the greater the significant value is, the more likely the gray level dynamic variation degree of the image block is the puncture needle appearance part.
Further, according to the saliency value and the time sequence change mean value of the image block, the change saliency of the image block at the current time is obtained, and the saliency of the image block with larger gray level dynamic change is restrained through the overall change condition, so that the saliency of the puncture needle occurrence part with sudden change caused by the motion change can be more remarkably represented. The significant value and the change significant value are positively correlated, the time sequence change mean value and the change significant value are negatively correlated, the change significant value is a value subjected to normalization processing, and in the embodiment of the invention, the expression of the change significance is:
In the method, in the process of the invention, Expressed as/>Significance of change of individual image blocks,/>Expressed as/>First/>, of the image blockVariation difference,/>Expressed as/>Total number of variation differences for individual image blocks,/>Represented as a maximum value extraction function,It should be noted that, normalization is a technical means well known to those skilled in the art, and the normalization function may be selected by linear normalization or standard normalization, and the specific normalization method is not limited herein. /(I)Expressed as a preset adjustment parameter, in the embodiment of the present invention, the preset adjustment parameter is set to 0.001, which aims to prevent the situation that the formula is meaningless due to zero denominator.
Wherein,Expressed as/>Time sequence change mean value of each image block/>Expressed as/>Significant values of the individual image blocks. In other embodiments of the present invention, the significant value and the significant value of the change may be reflected by other basic mathematical operations, such as power operation, etc., and the average value of the time sequence change and the significant value of the change may be negatively correlated, such as subtraction, etc., without limitation.
When the change condition of the gray level dynamic change degree of the image block is more complex, namely, the larger the time sequence change mean value is, the complexity caused by the movement of the puncture needle cannot be better described, so that the change significance of the influence of the puncture needle is reflected to be lower, and the time sequence change mean value needs to be in negative correlation. Therefore, when the significant value is larger, the change condition of the gray dynamic change degree of the image block is smaller, which means that the influence of the puncture needle is more obvious, so the change significance is larger.
So far, the change analysis of the gray level dynamic change degree on the time sequence is completed, and the dynamic change remarkable degree reflecting the influence of the puncture needle is obtained.
S4: obtaining the similarity weight between two image blocks at the current time according to the difference condition of the change significance between the two image blocks at the current time; and acquiring an enhanced puncture ultrasonic image at the current time by adopting non-local mean filtering based on the similarity weight corresponding to the current time, and performing auxiliary positioning.
The change saliency can reflect the change saliency of the image block affected by puncture, namely, the part possibly appearing by the puncture needle is characterized, so that in the non-local mean value filtering process, the filtering specific gravity is considered to be higher as the influence of the puncture needle is lower in the filtering process between the areas with the more similar saliency. And the more significant difference between the regions indicates that the portion where the puncture needle appears, the more difference is needed to be reserved, namely the more difference is reserved for the portion where the puncture needle appears. Therefore, according to the difference condition of the change significance between the two image blocks at the current moment, the optimized non-local mean filtering corresponding to the current moment is obtained.
Preferably, the difference of the change significance of any two image blocks at the current moment is subjected to negative correlation mapping and normalization processing, so as to obtain a similarity weight corresponding to any two image blocks, and in the embodiment of the invention, the specific expression of the similarity weight is as follows:
In the method, in the process of the invention, Expressed as/>Image block and/>Similarity weight between image blocks,/>Expressed as/>Significance of change of individual image blocks,/>Expressed as/>Significance of change of individual image blocks,/>Represented as an absolute value extraction function,Represented as an exponential function with a base of natural constant.
Further, since the non-local mean filtering process needs to calculate the similarity between the two image blocks as the weight of the filtering process, the similarity weight between the two image blocks is used as the similarity weight parameter required in the non-local mean filtering process to filter, and the enhanced puncture ultrasonic image corresponding to the current moment is obtained. In other words, when calculating the similarity between the image blocks in the non-local mean filtering process, a similarity weight method is adopted to obtain a similarity relation between the image blocks for filtering, and it should be noted that the non-local mean filtering process is a technical means well known to those skilled in the art, and the similarity weight parameter is used as a necessary calculation parameter in the non-local mean filtering process, and is calculated in the prior art by using a mean square error, so that the optimization filtering is performed in a manner of replacing the calculation of the similarity weight, and the non-local mean filtering is not described herein.
The enhanced puncture ultrasonic image can clearly represent the image of the puncture needle, so that the tissue structure of the anesthesia area and the structure of the puncture needle part are more obvious, and the result of auxiliary positioning of the puncture needle is more accurate. The puncture needle positioning system is combined with the display to display the real-time puncture guiding image, so that a doctor can more intuitively observe the ultrasonic image and the puncture needle position, and the accuracy and the safety of puncture are ensured.
In summary, the invention respectively acquires the ultrasonic images in the unpunctured stage and the puncturing stage, and obtains the relatively stable gray level dynamic change degree of each image block in the unpunctured stage through the gray level fluctuation and the gray level distribution dynamic change condition of each image block in the ultrasonic image in the unpunctured stage, thereby representing the gray level dynamic change characteristics of different area images in the anesthesia area within the range of the image block. Further, considering the influence of the puncture motion characteristic of the puncture needle head on the original change characteristic in the puncture stage, calculating the gray level dynamic change degree of each image block under the whole ultrasonic image after the ultrasonic image is acquired each time in the puncture stage on the time sequence, and further obtaining the time sequence change sequence of each image block consisting of the gray level dynamic change degrees at the current time. Because the gray level dynamic change degree of the image blocks can be changed to a certain extent in time sequence in the puncturing process, the change significance is obtained according to the change trend of the gray level dynamic change degree in the time sequence change sequence, the significance of the time sequence dynamic change influenced by the occurrence of the puncture needle is reflected, and then the similarity weight is obtained through the difference condition of the change significance between the image blocks, and can be used for optimizing non-local mean filtering. And finally, based on the similarity weight, adopting non-local mean filtering to realize the enhanced auxiliary puncture needle positioning of the ultrasonic image at the current moment. According to the invention, through the range of gray level dynamic change on time sequence, the similarity characteristics of the tissue in the anesthesia area are analyzed from the angle of the time sequence change characteristics, the robustness and the enhancement effect of filtering after the appearance of the puncture needle are improved, a high-quality enhanced ultrasonic image is obtained, and the reliability of the accurate positioning of the subsequent puncture position is further improved.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (9)
1. An ultrasound image-based nerve anesthesia puncture auxiliary positioning method is characterized by comprising the following steps:
Acquiring more than two ultrasonic images in an unpunctured stage and an ultrasonic image corresponding to each sampling time before the current time in a puncturing stage in an anesthetic region; dividing each ultrasonic image into more than two image blocks; taking all ultrasonic images in the unpunctured stage as an image group;
For all ultrasonic images in the image group, according to the fluctuation condition of gray distribution of each image block under each ultrasonic image and the deviation degree between the gray distribution condition of the corresponding image block in each ultrasonic image and the gray distribution condition in the whole ultrasonic image of the image group, obtaining the gray dynamic change degree of each image block corresponding to the image group;
Sequentially adding the ultrasonic images corresponding to each sampling time before the current time in the puncture stage into an image group according to a time sequence, and acquiring the gray level dynamic change degree of each image block corresponding to the image group once after adding one ultrasonic image; sequencing all gray level dynamic change degrees corresponding to each image block according to a time sequence order to obtain a time sequence change sequence corresponding to each image block at the current time; in the time sequence change sequence of each image block at the current time, according to the change trend condition of the gray level dynamic change degree, obtaining the change significance of each image block at the current time;
Obtaining the similarity weight between two image blocks at the current time according to the difference condition of the change significance between the two image blocks at the current time; acquiring an enhanced puncture ultrasonic image at the current moment by adopting non-local mean filtering based on the similarity weight corresponding to the current moment, and performing auxiliary positioning;
the method for acquiring the change saliency comprises the following steps:
For any image block, calculating the difference between the dynamic change degrees of every two adjacent gray scales in the corresponding time sequence change sequence of the image block at the current time to obtain the change difference corresponding to the image block; taking the average value of all variation differences of the image block as the time sequence variation average value of the image block; taking the maximum value of the variation difference of the image block as a significant value of the image block;
Obtaining the change saliency of the image block at the current time according to the saliency value and the time sequence change mean value of the image block; the significant value and the change significance are positively correlated, the time sequence change mean value and the change significance are negatively correlated, and the change significance is a value subjected to normalization processing.
2. The method for assisting positioning of nerve anesthesia puncture based on ultrasonic image according to claim 1, wherein the method for obtaining the dynamic change degree of gray scale comprises the following steps:
Taking each image block as a target image block in sequence, and acquiring a gray histogram of the target image block under any ultrasonic image in the image group; according to the distribution condition of each gray level on the gray level histogram, a gray level distribution sequence and a gray level fluctuation index of the target image block under the ultrasonic image are obtained;
According to the gray level distribution sequence of the target image block under each ultrasonic image in the image group, a gray level distribution mean value sequence of the target image block is obtained;
Calculating a DTW value between a gray level distribution sequence and a gray level distribution mean value sequence of the target image block under each ultrasonic image, and obtaining a change deviation index of the target image block under each ultrasonic image;
Calculating the product of the gray scale fluctuation index and the change deviation index of the target image block under each ultrasonic image to obtain the gray scale change index of the target image block under each ultrasonic image; and taking the average value of the gray level change indexes of the target image block under all ultrasonic images as the gray level dynamic change degree of the target image block.
3. The method for assisting positioning of nerve anesthesia puncture based on ultrasonic image according to claim 2, wherein the step of obtaining the gray level distribution sequence and gray level fluctuation index of the target image block under the ultrasonic image according to the distribution condition of each gray level on the gray level histogram comprises the steps of:
Acquiring the distribution frequency of all gray scales in a gray histogram of a target image block corresponding to the ultrasonic image; arranging the distribution frequencies of all the gray scales in the target image block according to the order from small gray scales to large gray scales to obtain a gray scale distribution sequence of the target image block;
taking the maximum value of the distribution frequency of the target image block in the gray level distribution sequence under the ultrasonic image as the maximum fluctuation value; taking the minimum value of non-zero distribution frequency in a gray level distribution sequence of a target image block under the ultrasonic image as a minimum fluctuation value; and taking the difference value of the maximum fluctuation value and the minimum fluctuation value as a gray scale fluctuation index of the target image block under the ultrasonic image.
4. The method for assisting positioning of nerve anesthesia puncture based on ultrasonic image according to claim 3, wherein the step of obtaining the distribution frequency of all gray scales in the gray scale histogram of the target image block corresponding to the ultrasonic image comprises the steps of:
When the gray level has the occurrence frequency in the gray level histogram of the target image block, marking the occurrence frequency as the distribution frequency of the corresponding gray level; when the gray level does not have the occurrence frequency in the gray histogram of the target image block, the distribution frequency of the corresponding gray level is recorded as zero.
5. The method for assisting positioning of nerve anesthesia puncture based on ultrasonic image according to claim 3, wherein the method for acquiring the gray level distribution mean value sequence comprises the following steps:
Acquiring a sequence number of each distribution frequency in a gray level distribution sequence of a target image block under each ultrasonic image; when non-zero distribution frequencies exist in the distribution frequencies corresponding to the serial numbers, taking the average value of all the non-zero distribution frequencies corresponding to the corresponding serial numbers as the average value of the distribution frequencies of the corresponding serial numbers; when the distribution frequency corresponding to the sequence number does not have non-zero distribution frequency, marking the average value of the distribution frequency corresponding to the sequence number as zero;
And sequencing all the distribution frequency mean values according to the sequence from small to large to obtain a gray level distribution mean value sequence of the target image block.
6. The method for assisting in positioning the nerve anesthesia puncture based on the ultrasonic image according to claim 1, wherein the method for acquiring the similarity weight comprises the following steps:
And carrying out negative correlation mapping and normalization processing on the difference of the change significance of any two image blocks at the current moment to obtain the similarity weight between the corresponding any two image blocks.
7. The method for assisting positioning of a nerve anesthesia puncture based on ultrasonic imaging according to claim 1, wherein the dividing each ultrasonic image into more than two image blocks comprises:
uniformly dividing each ultrasonic image into a preset number of image blocks; each ultrasonic image dividing method is the same and the preset number is more than or equal to 2.
8. The method for assisting in positioning a nerve anesthesia puncture based on an ultrasonic image according to claim 7, wherein the preset number is set to 50.
9. The ultrasound image-based neuroanesthesia puncture assistance localization method of claim 2, wherein the total number of gray levels is 256.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410038245.7A CN117557486B (en) | 2024-01-11 | 2024-01-11 | Neural anesthesia puncture auxiliary positioning method based on ultrasonic image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410038245.7A CN117557486B (en) | 2024-01-11 | 2024-01-11 | Neural anesthesia puncture auxiliary positioning method based on ultrasonic image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117557486A CN117557486A (en) | 2024-02-13 |
| CN117557486B true CN117557486B (en) | 2024-04-19 |
Family
ID=89815043
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410038245.7A Active CN117557486B (en) | 2024-01-11 | 2024-01-11 | Neural anesthesia puncture auxiliary positioning method based on ultrasonic image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117557486B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117853570B (en) * | 2024-03-08 | 2024-05-10 | 科普云医疗软件(深圳)有限公司 | Anesthesia puncture auxiliary positioning method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106991661A (en) * | 2017-03-31 | 2017-07-28 | 重庆大学 | Merge KL conversion and the non-local mean denoising method of grey relational grade |
| WO2023193175A1 (en) * | 2022-04-07 | 2023-10-12 | 中国科学院深圳先进技术研究院 | Puncture needle real-time detection method and apparatus based on ultrasonic image |
| CN116883293A (en) * | 2023-09-08 | 2023-10-13 | 青岛云智霄凡科技有限公司 | Image enhancement processing method for nerve anesthesia puncture |
| CN117078671A (en) * | 2023-10-13 | 2023-11-17 | 陕西秒康医疗科技有限公司 | Thyroid ultrasonic image intelligent analysis system |
-
2024
- 2024-01-11 CN CN202410038245.7A patent/CN117557486B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106991661A (en) * | 2017-03-31 | 2017-07-28 | 重庆大学 | Merge KL conversion and the non-local mean denoising method of grey relational grade |
| WO2023193175A1 (en) * | 2022-04-07 | 2023-10-12 | 中国科学院深圳先进技术研究院 | Puncture needle real-time detection method and apparatus based on ultrasonic image |
| CN116883293A (en) * | 2023-09-08 | 2023-10-13 | 青岛云智霄凡科技有限公司 | Image enhancement processing method for nerve anesthesia puncture |
| CN117078671A (en) * | 2023-10-13 | 2023-11-17 | 陕西秒康医疗科技有限公司 | Thyroid ultrasonic image intelligent analysis system |
Non-Patent Citations (2)
| Title |
|---|
| Improved Weighted Non-Local Mean Filtering Algorithm for Laser Image Speckle Suppression;Jin Cheng et al.;Micromachines;20221230;第1-13页 * |
| 改进权重函数的非局部均值图像去噪算法;张娜 等;计算机工程;20161231;第42卷(第12期);第254-261页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117557486A (en) | 2024-02-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117557486B (en) | Neural anesthesia puncture auxiliary positioning method based on ultrasonic image | |
| CN110652317B (en) | Automatic positioning method for standard tangent plane in prenatal fetal ultrasound volume image | |
| JP7462898B2 (en) | Diagnostic Support Program | |
| CN111161241B (en) | Liver image identification method, electronic equipment and storage medium | |
| CN111667467B (en) | Clustering algorithm-based lower limb vascular calcification index multi-parameter accumulation calculation method | |
| JP2016531709A (en) | Image analysis technology for diagnosing disease | |
| CN108618749B (en) | Retina blood vessel three-dimensional reconstruction method based on portable digital fundus camera | |
| CN116580033B (en) | Multi-mode medical image registration method based on image block similarity matching | |
| CN111640126B (en) | Artificial intelligent diagnosis auxiliary method based on medical image | |
| CN111640127B (en) | Accurate clinical diagnosis navigation method for orthopedics department | |
| CN111084621B (en) | QRS wave group form identification method and device based on depth self-encoder | |
| TWI828661B (en) | Diagnosis support system | |
| CN116889467B (en) | Intelligent self-nailing method, device, equipment and medium for vertebral column | |
| US20220398720A1 (en) | Diagnostic support program | |
| CN114343666B (en) | Paroxysmal atrial fibrillation scanning method and system for long-range electrocardiographic monitoring, storage medium and electronic equipment | |
| CN116959742B (en) | Blood glucose data processing method and system based on spherical coordinate kernel principal component analysis | |
| Dunaeva et al. | Video analysis methods for remote measurement of respiration characteristics and heart rate variability | |
| US20230351554A1 (en) | Real-time cardiac magnetic resonance (mr) by respiratory phase | |
| CN113658152A (en) | Apparatus, method, computer device and storage medium for predicting stroke risk | |
| CN116327155A (en) | Remote heart rate measurement method, device and equipment based on adaptive ROI selection | |
| CN117281490A (en) | Method and system for telemetering vital signs of non-anesthetized animals | |
| CN116392077A (en) | Razor back detection system based on three-dimensional depth image and control method | |
| CN117838309A (en) | Method and system for compensating advancing offset of ultrasonic guided needle knife | |
| CN114722874A (en) | Heart beat clustering method based on focus region registration | |
| CN116746936A (en) | Operation assisting method, operation assisting system, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |