CN113077393A - Space smoothing method and system for color Doppler blood flow imaging - Google Patents
Space smoothing method and system for color Doppler blood flow imaging Download PDFInfo
- Publication number
- CN113077393A CN113077393A CN202110295333.1A CN202110295333A CN113077393A CN 113077393 A CN113077393 A CN 113077393A CN 202110295333 A CN202110295333 A CN 202110295333A CN 113077393 A CN113077393 A CN 113077393A
- Authority
- CN
- China
- Prior art keywords
- neighborhood
- pixel
- template
- smoothing
- blood flow
- 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.)
- Granted
Links
- 238000009499 grossing Methods 0.000 title claims abstract description 54
- 230000017531 blood circulation Effects 0.000 title claims abstract description 50
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000003384 imaging method Methods 0.000 title claims abstract description 23
- 238000004458 analytical method Methods 0.000 claims abstract description 15
- 238000010276 construction Methods 0.000 claims description 4
- 230000000694 effects Effects 0.000 abstract description 5
- 230000009286 beneficial effect Effects 0.000 description 3
- 210000004204 blood vessel Anatomy 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000000877 morphologic effect Effects 0.000 description 2
- 208000024172 Cardiovascular disease Diseases 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 210000001685 thyroid gland Anatomy 0.000 description 1
- 238000012285 ultrasound imaging Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- 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
- G06T2207/10024—Color image
-
- 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
-
- 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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30101—Blood vessel; Artery; Vein; Vascular
- G06T2207/30104—Vascular flow; Blood flow; Perfusion
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Ultra Sonic Daignosis Equipment (AREA)
Abstract
The invention belongs to the technical field of ultrasonic imaging, and particularly relates to a space smoothing method and a space smoothing system for color Doppler blood flow imaging, wherein the related method comprises the following steps: s1, constructing a neighborhood template; s2, performing neighborhood matching analysis on the blood flow image according to the neighborhood template to obtain a matching template; s3, carrying out anisotropic smoothing processing on the blood flow image according to the matching template; the method solves the problem of difficult parameter debugging, has stronger practicability, achieves different smoothing effects by designing different neighborhood templates, has better flexibility, and effectively improves the boundary sawtooth of the blood flow image.
Description
Technical Field
The invention belongs to the technical field of ultrasonic imaging, and particularly relates to a space smoothing method and system for color Doppler blood flow imaging.
Background
Color Doppler Flow Imaging (CDFI) is a basic ultrasound Imaging mode, and CDFI can display the spatial distribution of blood Flow velocity in real time, and has important clinical value in the diagnosis of cardiovascular diseases. The fullness, continuity and smoothness of the blood flow are important evaluation indexes of CDFI, and the ideal blood flow does not have obvious deficiency inside and can not overflow to the outer side of the blood vessel wall, namely, the blood flow boundary is highly overlapped with the blood vessel wall. However, in the actual imaging process, it is often found that black holes and defects occur inside blood flow, and jaggies occur at the boundary, which eventually reduces the quality of blood flow imaging.
The prior art comprises morphological expansion, corrosion, opening operation, closing operation, top-cap conversion, bottom-cap conversion and the like, but the improvement effect of the method on blood flow highly depends on the arrangement of structural elements, particularly on the types and sizes of the structural elements, which brings difficulty to actual parameter debugging, and is difficult to find a set of parameters suitable for all imaging parts. For example, thyroid blood flow is much finer than carotid blood flow and therefore smaller structural element sizes are required. Based on the analysis, it is understood that designing a more flexible processing method for solving the above problems has great clinical significance.
Disclosure of Invention
Based on the above-mentioned shortcomings and drawbacks of the prior art, it is an object of the present invention to at least solve one or more of the above-mentioned problems of the prior art, in other words, to provide a method and system for spatial smoothing of color doppler flow imaging that meets one or more of the above-mentioned needs.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method of spatial smoothing for color doppler flow imaging, comprising the steps of:
s1, constructing a neighborhood template;
s2, performing neighborhood matching analysis on the blood flow image according to the neighborhood template to obtain a matching template;
and S3, performing anisotropic smoothing processing on the blood flow image according to the matching template.
Preferably, the step S1 specifically includes: setting a neighborhood size N, and constructing a neighborhood template T according to the neighborhood size; wherein N is an odd number not greater than 7, and the total number of neighboring pixels is N2-1。
Preferably, the step S2 specifically includes:
s21, judging whether the neighborhood of the pixel to be smoothed has a pixel value; if yes, the corresponding position value of the neighborhood characteristic R is 1; if not, R is 0;
s22, judging whether the neighborhood characteristic R of the pixel to be smoothed is matched with the neighborhood template T or not;
s23, sequentially traversing each pixel of the blood flow image until the analysis of the entire image is completed.
Preferably, the size of the domain feature R is the same as the size of the domain template T.
Preferably, the step S22 specifically includes: judging whether the domain feature R is equal to any one of the neighborhood templates T or not, if so, setting flag (x, y) to be 1; if not, setting flag (x, y) to be 0; wherein, (x, y) is the coordinate of the pixel to be smoothed, and flag is the matching template.
Preferably, the step S3 specifically includes:
s31, judging whether the flag of the current pixel is 1, if so, executing a step S32; if not, starting to process the next pixel;
s32, judging the direction of the neighborhood pixels, if the direction is positive, smoothing the positive neighborhood pixels, and assigning the pixel value to the central pixel; and if the pixel value is negative, smoothing the negative neighborhood pixels, and assigning the pixel value to the central pixel.
S33, sequentially traversing each pixel of the blood flow image until the smoothing of the entire image is completed.
The invention also provides a spatial smoothing system for color doppler flow imaging, comprising:
the construction module is used for constructing a neighborhood template;
the matching module is used for performing neighborhood matching analysis on the blood flow image according to the neighborhood template to obtain a matching template;
and the smoothing module is used for carrying out anisotropic smoothing processing on the blood flow image according to the matching template.
Preferably, the matching module includes:
the first matching module is used for judging whether the neighborhood of the pixel to be smoothed has a pixel value or not; if yes, the corresponding position value of the neighborhood characteristic R is 1; if not, R is 0;
the second matching module is used for judging whether the neighborhood characteristic R of the pixel to be smoothed is matched with the neighborhood template T or not; each pixel of the blood flow image is traversed in turn until the analysis of the entire image is completed.
Preferably, the smoothing module includes:
the first processing module is used for judging whether the flag of the current pixel is 1 or not; if yes, executing a second processing module; if not, starting to process the next pixel;
the second processing module is used for judging the direction of the neighborhood pixels, smoothing the forward neighborhood pixels if the direction of the neighborhood pixels is positive, and assigning the pixel value to the central pixel; if the pixel value is negative, smoothing the negative neighborhood pixels, and assigning the pixel value to a central pixel; and sequentially traversing each pixel of the blood flow image until smoothing of the full image is completed.
Compared with the prior art, the invention has the beneficial effects that:
the space smoothing method for color Doppler blood flow imaging provided by the invention can solve the problem of difficulty in parameter debugging and has stronger practicability.
The space smoothing method for color Doppler blood flow imaging provided by the invention achieves different smoothing effects by designing different neighborhood templates, and has better flexibility.
The space smoothing method for color Doppler blood flow imaging provided by the invention can effectively improve the boundary sawtooth of the blood flow image.
Drawings
FIG. 1 is a flow chart of a spatial smoothing method for color Doppler blood flow imaging according to an embodiment of the present invention;
fig. 2 is a class 1 neighborhood template when N is 3 according to the first embodiment of the present invention;
fig. 3 is a class 2 neighborhood template when N is 3 according to embodiment one of the present invention;
fig. 4 is a class 3 neighborhood template when N is 3 according to embodiment one of the present invention;
fig. 5 is a structural diagram of a spatial smoothing system for color doppler flow imaging according to a second embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention, the following description will explain the embodiments of the present invention with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.
The first embodiment is as follows:
as shown in fig. 1, the present embodiment provides a spatial smoothing method for color doppler flow imaging, including the following steps:
s1, constructing a neighborhood template;
s2, performing neighborhood matching analysis on the blood flow image according to the neighborhood template to obtain a matching template;
and S3, performing anisotropic smoothing processing on the blood flow image according to the matching template.
The construction of the neighborhood template in step S1 specifically includes:
setting a neighborhood size and marking as N; where N is an odd number not greater than 7, and in this embodiment, N is 3.
As shown in fig. 2 to 4, when N is 3, the neighborhood template T is constructed from N, and the total number of neighborhood pixels is N2-1; when N is 3, the neighborhood pixels are 8, and for the zigzag smoothing, the combination of the neighborhood pixels needs to have corners, but cannot form a semi-closed curve, so the neighborhood template is any one of fig. 2 to 4.
In step S2, performing neighborhood matching analysis on the blood flow image according to the neighborhood template to obtain a matching template, specifically:
s21, judging whether the neighborhood of the pixel to be smoothed has a pixel value; if yes, the corresponding position value of the neighborhood characteristic R is 1; if not, R is 0; wherein, the size of the domain characteristic R is the same as that of the domain template T;
s22, judging whether the neighborhood characteristic R of the pixel to be smoothed is matched with the neighborhood template T or not;
s23, according to the steps S21-S22, each pixel of the blood flow image is traversed in sequence until the analysis of the whole image is completed.
Wherein, step S22 specifically includes: judging whether the neighborhood characteristic R of the current pixel is equal to any one neighborhood template or not; if yes, setting flag (x, y) to be 1; otherwise, setting flag (x, y) to be 0; wherein, (x, y) is the coordinate of the current pixel, and flag is the matching template.
In step S3, performing anisotropic smoothing on the blood flow image according to the matching template, specifically including:
s31, judging whether the flag of the current pixel is 1, if so, executing a step S32; if not, starting to process the next pixel;
s32, judging the direction of the neighborhood pixels, and dividing the neighborhood pixels into a positive direction and a negative direction; if the pixel value is positive, smoothing the positive neighborhood pixels, and assigning the pixel value to a central pixel; and if the pixel value is negative, smoothing the negative neighborhood pixels, and assigning the pixel value to the central pixel.
S33, according to the steps S31-S32, each pixel of the blood flow image is traversed in sequence until the smoothing of the whole image is completed.
Compared with the prior art, the embodiment has the following beneficial effects:
the embodiment avoids the difficulty of parameter debugging and has stronger practicability; different smooth effects are achieved by designing different neighborhood templates, and the method has better flexibility; the boundary sawtooth of the blood flow image can be effectively improved.
Example two:
as shown in fig. 5, the present embodiment provides a spatial smoothing system for color doppler flow imaging, including:
the construction module 11 is used for constructing a neighborhood template;
the matching module 12 is used for performing neighborhood matching analysis on the blood flow image according to the neighborhood template to obtain a matching template;
and the smoothing module 13 is configured to perform anisotropic smoothing on the blood flow image according to the matching template.
Further, the matching module 12 includes:
the first matching module is used for judging whether the neighborhood of the pixel to be smoothed has a pixel value or not; if yes, the corresponding position value of the neighborhood characteristic R is 1; if not, R is 0;
the second matching module is used for judging whether the neighborhood characteristic R of the pixel to be smoothed is matched with the neighborhood template T or not; each pixel of the blood flow image is traversed in turn until the analysis of the entire image is completed.
Further, the smoothing module 13 includes:
the first processing module is used for judging whether the flag of the current pixel is 1 or not; if yes, executing a second processing module; if not, starting to process the next pixel;
the second processing module is used for judging the direction of the neighborhood pixels, smoothing the forward neighborhood pixels if the direction of the neighborhood pixels is positive, and assigning the pixel value to the central pixel; if the pixel value is negative, smoothing the negative neighborhood pixels, and assigning the pixel value to a central pixel; and sequentially traversing each pixel of the blood flow image until smoothing of the entire image is completed.
The spatial smoothing system for color doppler blood flow imaging in this embodiment corresponds to the method in the first embodiment, and will not be described herein again.
Compared with the prior art, the invention has the following beneficial effects:
1. compared with the traditional morphological processing, the method effectively solves the problem of difficulty in parameter debugging and has stronger practicability.
2. The method achieves different smoothing effects by designing different neighborhood templates, and has better flexibility.
3. The invention can effectively improve the boundary sawtooth of the blood flow image.
The foregoing has outlined rather broadly the preferred embodiments and principles of the present invention and it will be appreciated that those skilled in the art may devise variations of the present invention that are within the spirit and scope of the appended claims.
Claims (9)
1. A method for spatial smoothing of color Doppler flow imaging, comprising the steps of:
s1, constructing a neighborhood template;
s2, performing neighborhood matching analysis on the blood flow image according to the neighborhood template to obtain a matching template;
and S3, performing anisotropic smoothing processing on the blood flow image according to the matching template.
2. The method according to claim 1, wherein the step S1 is specifically as follows: setting a neighborhood size N, and constructing a neighborhood template T according to the neighborhood size; wherein N is an odd number not greater than 7, and the total number of neighboring pixels is N2-1。
3. The method according to claim 2, wherein the step S2 specifically comprises:
s21, judging whether the neighborhood of the pixel to be smoothed has a pixel value; if yes, the corresponding position value of the neighborhood characteristic R is 1; if not, R is 0;
s22, judging whether the neighborhood characteristic R of the pixel to be smoothed is matched with the neighborhood template T or not;
s23, sequentially traversing each pixel of the blood flow image until the analysis of the entire image is completed.
4. The method of claim 3, wherein the size of the region feature R is the same as the size of the region template T.
5. The method according to claim 3, wherein the step S22 is specifically as follows: judging whether the domain feature R is equal to any one of the neighborhood templates T or not, if so, setting flag (x, y) to be 1; if not, setting flag (x, y) to be 0; wherein, (x, y) is the coordinate of the pixel to be smoothed, and flag is the matching template.
6. The method according to claim 5, wherein the step S3 specifically comprises:
s31, judging whether the flag of the current pixel is 1, if so, executing a step S32; if not, starting to process the next pixel;
s32, judging the direction of the neighborhood pixels, if the direction is positive, smoothing the positive neighborhood pixels, and assigning the pixel value to the central pixel; and if the pixel value is negative, smoothing the negative neighborhood pixels, and assigning the pixel value to the central pixel.
S33, sequentially traversing each pixel of the blood flow image until the smoothing of the entire image is completed.
7. A spatial smoothing system for color doppler flow imaging, comprising:
the construction module is used for constructing a neighborhood template;
the matching module is used for performing neighborhood matching analysis on the blood flow image according to the neighborhood template to obtain a matching template;
and the smoothing module is used for carrying out anisotropic smoothing processing on the blood flow image according to the matching template.
8. The system of claim 7, wherein the matching module comprises:
the first matching module is used for judging whether the neighborhood of the pixel to be smoothed has a pixel value or not; if yes, the corresponding position value of the neighborhood characteristic R is 1; if not, R is 0;
the second matching module is used for judging whether the neighborhood characteristic R of the pixel to be smoothed is matched with the neighborhood template T or not; each pixel of the blood flow image is traversed in turn until the analysis of the entire image is completed.
9. The system of claim 7, wherein the smoothing module comprises:
the first processing module is used for judging whether the flag of the current pixel is 1 or not; if yes, executing a second processing module; if not, starting to process the next pixel;
the second processing module is used for judging the direction of the neighborhood pixels, smoothing the forward neighborhood pixels if the direction of the neighborhood pixels is positive, and assigning the pixel value to the central pixel; if the pixel value is negative, smoothing the negative neighborhood pixels, and assigning the pixel value to a central pixel; and sequentially traversing each pixel of the blood flow image until smoothing of the full image is completed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110295333.1A CN113077393B (en) | 2021-03-19 | 2021-03-19 | Space smoothing method and system for color Doppler blood flow imaging |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110295333.1A CN113077393B (en) | 2021-03-19 | 2021-03-19 | Space smoothing method and system for color Doppler blood flow imaging |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113077393A true CN113077393A (en) | 2021-07-06 |
| CN113077393B CN113077393B (en) | 2022-10-25 |
Family
ID=76613461
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110295333.1A Active CN113077393B (en) | 2021-03-19 | 2021-03-19 | Space smoothing method and system for color Doppler blood flow imaging |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113077393B (en) |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030083850A1 (en) * | 2001-10-26 | 2003-05-01 | Schmidt Darren R. | Locating regions in a target image using color matching, luminance pattern matching and hue plane pattern matching |
| US20030181814A1 (en) * | 2002-03-19 | 2003-09-25 | Ting-Lan Ji | System and method for post-processing ultrasound color doppler imaging |
| CN101464998A (en) * | 2009-01-15 | 2009-06-24 | 浙江大学 | Non-gauss veins noise smooth filtering method for textile industry |
| CN101847252A (en) * | 2010-04-28 | 2010-09-29 | 清华大学 | Image amplification method for maintaining image smoothness |
| CN102523373A (en) * | 2011-12-07 | 2012-06-27 | 天津天地伟业物联网技术有限公司 | Filtering noise reduction system and filtering noise reduction method of video data |
| CN102930538A (en) * | 2012-10-25 | 2013-02-13 | 河南师范大学 | Adaptive image segmentation method for combining Gaussian potential histogram and spatial histogram |
| CN103839281A (en) * | 2014-03-27 | 2014-06-04 | 武汉大学 | Method for measuring blood flow velocity by utilization of radiography microbubbles |
| CN104123547A (en) * | 2014-07-25 | 2014-10-29 | 黑龙江大学 | Improved directional filter and flexible matching based recognition method |
| CN106127707A (en) * | 2016-06-23 | 2016-11-16 | 南京大学金陵学院 | A kind of processing method of supersonic blood image |
| CN109493365A (en) * | 2018-10-11 | 2019-03-19 | 中国科学院上海技术物理研究所 | A kind of tracking of Weak target |
| CN110427926A (en) * | 2019-09-11 | 2019-11-08 | 中国计量大学 | A kind of improved OPTA finger vena thinning algorithm |
| CN112102209A (en) * | 2020-11-17 | 2020-12-18 | 四川圣点世纪科技有限公司 | Abnormal vein image restoration method and device |
-
2021
- 2021-03-19 CN CN202110295333.1A patent/CN113077393B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030083850A1 (en) * | 2001-10-26 | 2003-05-01 | Schmidt Darren R. | Locating regions in a target image using color matching, luminance pattern matching and hue plane pattern matching |
| US20030181814A1 (en) * | 2002-03-19 | 2003-09-25 | Ting-Lan Ji | System and method for post-processing ultrasound color doppler imaging |
| CN101464998A (en) * | 2009-01-15 | 2009-06-24 | 浙江大学 | Non-gauss veins noise smooth filtering method for textile industry |
| CN101847252A (en) * | 2010-04-28 | 2010-09-29 | 清华大学 | Image amplification method for maintaining image smoothness |
| CN102523373A (en) * | 2011-12-07 | 2012-06-27 | 天津天地伟业物联网技术有限公司 | Filtering noise reduction system and filtering noise reduction method of video data |
| CN102930538A (en) * | 2012-10-25 | 2013-02-13 | 河南师范大学 | Adaptive image segmentation method for combining Gaussian potential histogram and spatial histogram |
| CN103839281A (en) * | 2014-03-27 | 2014-06-04 | 武汉大学 | Method for measuring blood flow velocity by utilization of radiography microbubbles |
| CN104123547A (en) * | 2014-07-25 | 2014-10-29 | 黑龙江大学 | Improved directional filter and flexible matching based recognition method |
| CN106127707A (en) * | 2016-06-23 | 2016-11-16 | 南京大学金陵学院 | A kind of processing method of supersonic blood image |
| CN109493365A (en) * | 2018-10-11 | 2019-03-19 | 中国科学院上海技术物理研究所 | A kind of tracking of Weak target |
| CN110427926A (en) * | 2019-09-11 | 2019-11-08 | 中国计量大学 | A kind of improved OPTA finger vena thinning algorithm |
| CN112102209A (en) * | 2020-11-17 | 2020-12-18 | 四川圣点世纪科技有限公司 | Abnormal vein image restoration method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113077393B (en) | 2022-10-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Kim et al. | Example-based learning for single-image super-resolution | |
| CN103226822B (en) | Medical imaging joining method | |
| CN105608671A (en) | Image connection method based on SURF algorithm | |
| DE102010046259A1 (en) | Image frequency conversion using motion estimation and compensation | |
| US8175417B2 (en) | Apparatus, method, and computer-readable recording medium for pixel interpolation | |
| CN102722875B (en) | Visual-attention-based variable quality ultra-resolution image reconstruction method | |
| EP3067862A2 (en) | Image processing method and apparatus | |
| CN104881843A (en) | Image interpolation method and image interpolation apparatus | |
| CN111369550A (en) | Image registration and defect detection method, model, training method, device and equipment | |
| US11836961B2 (en) | Information processing apparatus, information processing method, and storage medium for determining whether a captured image of a subject is suitable for recognition processing | |
| CN103065292B (en) | Face super resolution rebuilding method based on principal component sparse expression | |
| CN106204550A (en) | The method for registering of a kind of non-rigid multi modal medical image and system | |
| WO2017096814A1 (en) | Image processing method and apparatus | |
| JP2019204193A (en) | Image processing device, image processing method, and program | |
| Sengupta et al. | A new rank constraint on multi-view fundamental matrices, and its application to camera location recovery | |
| CN107945151B (en) | Repositioning image quality evaluation method based on similarity transformation | |
| CN103456031A (en) | Novel method for area image interpolation | |
| CN113077393B (en) | Space smoothing method and system for color Doppler blood flow imaging | |
| Li et al. | Dmhomo: Learning homography with diffusion models | |
| JP2006068373A (en) | Mammilla detector and program thereof | |
| JP2006338191A (en) | Image processor and domain division program | |
| JP5737072B2 (en) | Motion compensation frame generation apparatus and method | |
| CN105069764B (en) | A kind of image de-noising method and system based on Edge track | |
| CN110555385A (en) | welding seam characteristic point solving method based on variable step length curvature filtering | |
| CN110427944A (en) | Acquisition methods, device, equipment and the storage medium of car plate detection data |
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 | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: A Spatial Smoothing Method and System for Color Doppler Blood Flow Imaging Effective date of registration: 20230413 Granted publication date: 20221025 Pledgee: Zhejiang Lin'an Rural Commercial Bank Co.,Ltd. Qingshan Branch Pledgor: JURONG MEDICAL TECHNOLOGY (HANGZHOU) Co.,Ltd. Registration number: Y2023330000747 |