CN114881924B - Quantitative analysis device based on ultrasonic image elastic signals - Google Patents
Quantitative analysis device based on ultrasonic image elastic signals Download PDFInfo
- Publication number
- CN114881924B CN114881924B CN202210323622.2A CN202210323622A CN114881924B CN 114881924 B CN114881924 B CN 114881924B CN 202210323622 A CN202210323622 A CN 202210323622A CN 114881924 B CN114881924 B CN 114881924B
- Authority
- CN
- China
- Prior art keywords
- elastic
- signal
- texture
- elastic signal
- color saturation
- 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
- 238000004445 quantitative analysis Methods 0.000 title claims abstract description 14
- 238000002604 ultrasonography Methods 0.000 claims abstract description 33
- 238000004458 analytical method Methods 0.000 claims abstract description 20
- 238000000034 method Methods 0.000 claims abstract description 13
- 206010028980 Neoplasm Diseases 0.000 description 6
- 238000000605 extraction Methods 0.000 description 6
- 238000003745 diagnosis Methods 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 206010006187 Breast cancer Diseases 0.000 description 3
- 208000026310 Breast neoplasm Diseases 0.000 description 3
- 201000011510 cancer Diseases 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000003384 imaging method Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 210000000481 breast Anatomy 0.000 description 2
- 239000003086 colorant Substances 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000001747 exhibiting effect Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000003759 clinical diagnosis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000036210 malignancy Effects 0.000 description 1
- 230000003211 malignant effect Effects 0.000 description 1
- 230000001575 pathological effect Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000002062 proliferating effect Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 208000024891 symptom Diseases 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- 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
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
-
- 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
- G06T2207/30068—Mammography; Breast
-
- 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
- G06T2207/30096—Tumor; Lesion
Landscapes
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (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)
- Ultra Sonic Daignosis Equipment (AREA)
Abstract
The invention relates to a quantitative analysis device based on an ultrasonic image elastic signal, which comprises: an image acquisition module: for acquiring ultrasound images with nodules; elastic signal acquisition module: the method comprises the steps of obtaining an elastic signal according to the ultrasonic image; signal purity analysis module: for calculating the color saturation of the elastic signal by color saturation analysis. The invention can effectively carry out quantitative analysis on the elastic information in the elastic ultrasonic image.
Description
Technical Field
The invention relates to the technical field of medical image processing, in particular to a quantitative analysis device based on an ultrasonic image elastic signal.
Background
Today, with increasing demand for rapid and accurate diagnosis, and shortage of clinical staff, computer analysis methods have been increasingly applied to assist conventional clinical diagnosis and show good effects. Taking breast cancer as an example, as the second leading fatal cancer in women worldwide, the mortality rate of breast cancer is as high as 15%. Timely diagnosis of breast cancer is critical to improving the life expectancy of females. The ultrasonic imaging is used as a noninvasive, non-radiative and low-cost tumor diagnosis technology, and can well assist clinicians in judging benign and malignant cancer symptoms. However, due to the low quality of ultrasound images, analysis of tumor nodules in ultrasound images is a challenging task and is highly susceptible to physician subjective factors, resulting in misdiagnosis. As one of potential solutions, the corresponding computer analysis method can well conduct quantitative and accurate analysis on the nodules of different pathological positions, so as to assist clinicians in making more accurate judgment and reducing misdiagnosis.
As an important basis for diagnosing various cancers, analysis of the signs of texture in ultrasound images plays a vital role in clinical examinations. Hard-textured nodules are more prone to attack surrounding normal tissue, exhibiting stronger malignancy characteristics; in contrast, soft-textured nodules are less prone to attack on surrounding normal tissue, exhibiting more benign features. Unfortunately, conventional ultrasound approaches are difficult to analyze the texture of nodules due to limitations in the principles of self imaging and the effects of surrounding proliferating tissue and various types of noise, which are highly prone to misdiagnosis. Elastic ultrasound is used as a novel ultrasonic imaging technology, and the softness of a scanned part can be expressed on an ultrasonic image in a color form by processing an acquired original ultrasonic signal, so that the defect that the traditional conventional ultrasound has poor tissue texture display effect is overcome. However, the conventional examination method is limited by the prior art, and only subjective or semi-subjective analysis can be performed on the texture information in the elastic ultrasonic image, so that the elastic ultrasonic technology is limited to be widely applied.
Disclosure of Invention
The invention aims to solve the technical problem of providing a quantitative analysis device based on an elastic signal of an ultrasonic image, which can effectively perform quantitative analysis on elastic information in the elastic ultrasonic image.
The technical scheme adopted for solving the technical problems is as follows: provided is a quantitative analysis device based on an ultrasound image elastic signal, comprising:
an image acquisition module: for acquiring ultrasound images with nodules;
elastic signal acquisition module: the method comprises the steps of obtaining an elastic signal according to the ultrasonic image;
Signal purity analysis module: for calculating the color saturation of the elastic signal by color saturation analysis.
The signal purity analysis module is used for analyzing the signal purity of the signalCalculating the color saturation of the elastic signal, wherein s e (x, y) represents the color saturation of the elastic signal and s e epsilon [0,1], max () represents the maximum value, min () represents the minimum value, R x,y represents the luminance value of the pixel (x, y) in the red channel, G x,y represents the luminance value of the pixel (x, y) in the green channel, and B x,y represents the luminance value of the pixel (x, y) in the blue channel.
The method further comprises a first judging module: when the color saturation s e of the elastic signal is larger than a preset saturation threshold, the elastic signal is indicated to be bright; and when the color saturation s e of the elastic signal is smaller than a preset saturation threshold, the blurring of the elastic signal is indicated.
Further comprises:
Elastic signal texture classification module: for classifying the texture of the elastic signals, the texture of the elastic signals comprising three types of soft, hard and moderate;
Elastic signal measuring module: for calculating an elasticity information measure based on the texture classification of the elasticity signal and the color saturation s e (x, y) of the elasticity signal.
The elastic signal measuring module passes throughCalculating an elasticity information measure value, wherein S e (x, y) is the elasticity information measure value, S e (x, y) represents the color saturation of the elasticity signal,Is an elastic weighting constant Is an elastic weighting coefficient andOmega e is the texture classification of elastic signals, soft means soft, medium means medium, hard; λ is the positive and negative operational coefficient and λ= +1 or-1.
The method further comprises a second judging module: when the elasticity information measure S e (x, y) is greater than the first texture threshold, then it is indicated that the texture of the ultrasound image at the pixel (x, y) is hard; the elasticity information measure S e (x, y) is less than the second texture threshold, indicating that the texture of the ultrasound image at the pixel (x, y) is soft; the elasticity information measure S e (x, y) lying between the first texture threshold and the second texture threshold indicates that the texture of the ultrasound image at pixel (x, y) is moderate.
Advantageous effects
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects: the invention can effectively quantify the elastic information in the elastic ultrasonic image, makes up for the short plate which has insufficient accuracy of naked eye analysis and lacks consistency, greatly improves the film reading efficiency of the ultrasonic doctor, increases the diagnosis accuracy and greatly assists the daily work of the ultrasonic doctor.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a diagram showing the result of extracting color saturation of an elastic signal according to an embodiment of the present invention;
Fig. 3 is a schematic diagram of the extraction result of the elasticity information measure value in the embodiment of the present invention.
Detailed Description
The application will be further illustrated with reference to specific examples. It is to be understood that these examples are illustrative of the present application and are not intended to limit the scope of the present application. Furthermore, it should be understood that various changes and modifications can be made by one skilled in the art after reading the teachings of the present application, and such equivalents are intended to fall within the scope of the application as defined in the appended claims.
An embodiment of the present invention relates to a quantitative analysis device based on an elastic signal of an ultrasound image, referring to fig. 1, including:
an image acquisition module: for acquiring ultrasound images with nodules;
elastic signal acquisition module: the method comprises the steps of obtaining an elastic signal according to the ultrasonic image;
Signal purity analysis module: the color saturation of the elastic signal is calculated through a color saturation analysis method;
Elastic signal texture classification module: for classifying the texture of the elastic signals, the texture of the elastic signals comprising three types of soft, hard and moderate;
elastic signal measuring module: for calculating an elasticity information measure based on the texture classification of the elasticity signal and the color saturation of the elasticity signal.
The present embodiment will be described in detail below:
the sonographer can classify the scanned site roughly or shallowly by reading the elastic ultrasound image. However, when the elastic ultrasonic image is disordered and unobtrusive, accurate interpretation of the elastic information is often difficult to achieve based on naked eye judgment, so that more accurate measurement of conventional elastic information is required.
Further, the elastic signal acquisition module includes:
And a detection unit: the method comprises the steps of extracting a color region I c in an ultrasonic image through a threshold value judging method based on RGB channels, searching color pixels in the color region I c by taking eight neighborhood as a radius to obtain a plurality of independent connected domains, selecting the connected domain with the largest area in all the connected domains, and fitting the smallest boundary frame of the connected domain with the largest area to obtain an elastic signal region I e;
Extraction unit: for extracting the elastic signal in the elastic signal region I e by RGB color channel luminance comparison.
Further, the formula of the threshold judgment method in the detection unit is as follows:
Wherein Coloured (x, y) is the identification result of ultrasound at the pixel (x, y), true represents the identification result is color, false represents the identification result is black and white, G (x,y) is the brightness value of the pixel (x, y) on the green channel of the ultrasound image, R (x,y) is the brightness value of the pixel (x, y) on the red channel of the ultrasound image, B (x,y) is the brightness value of the pixel (x, y) on the blue channel of the ultrasound image, thr is a preset threshold.
Further, the extraction unit is provided with a plurality of extraction unitsExtracting elastic signals in the elastic signal region I e, wherein CheckInfo (x, y) is an elastic signal extraction result of the elastic ultrasonic image at the pixel (x, y), the pixel (x, y) is located in the elastic signal region I e, D e represents a channel decision equation, C (x,y) represents a luminance value of the elastic signal region I e at the pixel (x, y) in the channel C, (c\c) (x,y) represents a set of luminance values of the elastic signal region I e at the pixel (x, y) in two other channels except the channel C, and L c is an elastic conversion coefficient; when using blue to indicate soft property, red to indicate hard property, { L R＝250,LG＝150,LB =50 }; when a red color is used to indicate a soft property, a blue color indicates a hard property, { L R＝50,LG＝150,LB =250 }.
The embodiment can obtain the extracted elastic information classification image, and the image can well mark the elastic information in the nodule. In addition, the present embodiment also measures the ratio of various elastic signals in the nodule area, and three types are classified into hard, soft and medium. If ω e e { soft, medium, hard } is used to represent the required category, the calculation formula can be specifically expressed as:
Wherein X e represents the set of all pixels marked in the elastic signal region I e, X ROI represents the set of all pixels in the nodule region, 1 * In (a) is an indication function, when the return value of the elastic signal extraction equation CheckInfo (x, y) belongs toReturning to 1 if the time is short, otherwise returning to 0; Is an indication function, returns 1 when pixel (X, y) is within the region indicated by X ROI, otherwise returns 0, Is a lookup value for the sought elastic class and is defined as { t soft＝50,tmedium＝150,thard =250 } in terms of the elastic transformation coefficient L c. The higher the measured hard floor ratio using this metering method, the more hard components in the nodule region are indicated, whereas the higher the measured soft floor ratio, the more soft components in the nodule region are indicated.
In examinations using elastic ultrasound, the sonographer often uses the color change as a viewing basis to determine the texture of the site being scanned. However, due to the deficiency and difference of the sensitivity of human eyes to various colors, accurate and consistent interpretation of various colors and changes thereof is difficult, so that the misdiagnosis rate is improved. Therefore, the elastic ultrasonic image is analyzed based on the principle of color saturation analysis, so that the purity of various signals in the elastic ultrasonic image is accurately estimated, and further, the quantitative analysis of the elastic information is realized. In the signal purity analysis module, the characteristics of each pixel in the RGB color space are specifically utilized for comparison, and if R x,y、Gx,y and B x,y are used to respectively refer to the brightness values in the red, green and blue channels at the (x, y) position of the target pixel, the calculation process of the color saturation s e of the elastic signal can be specifically expressed as follows:
Where max () represents the maximum value and min () represents the minimum value, s e e [0,1]. The present embodiment further includes a first determination module: when the color saturation s e of the elastic signal is greater than a preset saturation threshold, the elastic signal is indicated to be bright (pure); when the color saturation s e of the elastic signal is smaller than the preset saturation threshold, the blurring (not pure) of the elastic signal is indicated.
Referring to fig. 2, (a) shows a breast ultrasound image with elastic signals, and white boxes are elastic signal areas; (b) As a result of the calculation of the saturation of the elastic color in the elastic signal region, it is easy to find that the higher the saturation of the color, the purer the color of the corresponding pixel.
Further, in the elastic signal measurement module, a specific calculation formula of the elastic information measurement value is:
Wherein S e (x, y) is an elasticity information measure, As elastic weighting constants, the classification according to different elasticity is defined as:
as the elastic weighting coefficients, it is defined by different elastic classifications:
Wherein soft indicates soft texture, medium indicates hard texture, λ is positive and negative coefficient of operation, and λ= +1 if the elastic signal reading of hard texture is greater than the elastic signal reading of soft texture at (x, y) when the texture classification ω e of the elastic signal is medium (medium); if the elastic signal reading for hard property is smaller than the elastic signal reading for soft property at (x, y), λ= -1. It is understood here that there are three channel readings for each pixel (x, y), and that when the texture ω e is medium, the readings for the corresponding channels of soft and hard need to be compared again to determine the assigned value of λ.
The present embodiment further includes a second judging module: the elasticity information measure S e (x, y) is between 0 and 1, and when the elasticity information measure S e (x, y) is greater than the first texture threshold, it indicates that the texture of the ultrasound image at the pixel (x, y) is hard; the elasticity information measure S e (x, y) is less than the second texture threshold, indicating that the texture of the ultrasound image at the pixel (x, y) is soft; the elasticity information measure S e (x, y) lying between the first texture threshold and the second texture threshold indicates that the texture of the ultrasound image at pixel (x, y) is moderate.
Referring to fig. 3, (a) a breast ultrasound image with elastic signals, white boxes are elastic signal areas; (b) For the extracted elastic information measure value, it is not difficult to find that the higher the elastic information measure value is, the brighter the corresponding pixel is.
Therefore, the invention can effectively quantify the elastic information in the elastic ultrasonic image, make up for the short plates which have insufficient accuracy of naked eye analysis and lack of consistency, greatly improve the film reading efficiency of the ultrasonic doctor and increase the diagnosis accuracy, and greatly assist the daily work of the ultrasonic doctor.
The foregoing descriptions of specific exemplary embodiments of the present invention are presented for purposes of illustration and description. It is not intended to limit the invention to the precise form disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiments were chosen and described in order to explain the specific principles of the invention and its practical application to thereby enable one skilled in the art to make and utilize the invention in various exemplary embodiments and with various modifications as are suited to the particular use contemplated. It is intended that the scope of the invention be defined by the claims and their equivalents.
Claims (4)
1. A quantitative analysis device based on an ultrasound image elastic signal, comprising:
an image acquisition module: for acquiring ultrasound images with nodules;
elastic signal acquisition module: the method comprises the steps of obtaining an elastic signal according to the ultrasonic image;
Signal purity analysis module: the color saturation of the elastic signal is calculated through a color saturation analysis method;
Elastic signal texture classification module: for classifying the texture of the elastic signals, the texture of the elastic signals comprising three types of soft, hard and moderate;
elastic signal measuring module: the method comprises the steps of calculating an elasticity information measure value according to the texture classification of an elasticity signal and the color saturation of the elasticity signal; wherein the elastic signal measuring module passes through Calculating an elasticity information measure value, wherein S e (x, y) is the elasticity information measure value, S e (x, y) represents the color saturation of the elasticity signal,Is an elastic weighting constant Is an elastic weighting coefficient andOmega e is the texture classification of elastic signals, soft means soft, medium means medium, hard; λ is the positive and negative operational coefficients, λ= +1 when the elastic signal reading of the hard property is greater than the elastic signal reading of the soft property at (x, y), and λ= -1 when the elastic signal reading of the hard property is less than the elastic signal reading of the soft property at (x, y).
2. The quantitative analysis device based on ultrasound image elastic signals according to claim 1, wherein the signal purity analysis module is configured to analyze the elastic signals byCalculating the color saturation of the elastic signal, wherein s e (x, y) represents the color saturation of the elastic signal and s e epsilon [0,1], max () represents the maximum value, min () represents the minimum value, R x,y represents the luminance value of the pixel (x, y) in the red channel, G x,y represents the luminance value of the pixel (x, y) in the green channel, and B x,y represents the luminance value of the pixel (x, y) in the blue channel.
3. The quantitative analysis device based on ultrasound image elastic signals according to claim 1, further comprising a first judgment module: when the color saturation s e of the elastic signal is larger than a preset saturation threshold, the elastic signal is indicated to be bright; and when the color saturation s e of the elastic signal is smaller than a preset saturation threshold, the blurring of the elastic signal is indicated.
4. The quantitative analysis device based on an ultrasound image elastic signal according to claim 1, further comprising a second judgment module: when the elasticity information measure S e (x, y) is greater than the first texture threshold, then it is indicated that the texture of the ultrasound image at the pixel (x, y) is hard; the elasticity information measure S e (x, y) is less than the second texture threshold, indicating that the texture of the ultrasound image at the pixel (x, y) is soft; the elasticity information measure S e (x, y) lying between the first texture threshold and the second texture threshold indicates that the texture of the ultrasound image at pixel (x, y) is moderate.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210323622.2A CN114881924B (en) | 2022-03-30 | 2022-03-30 | Quantitative analysis device based on ultrasonic image elastic signals |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210323622.2A CN114881924B (en) | 2022-03-30 | 2022-03-30 | Quantitative analysis device based on ultrasonic image elastic signals |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114881924A CN114881924A (en) | 2022-08-09 |
| CN114881924B true CN114881924B (en) | 2024-07-23 |
Family
ID=82669390
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210323622.2A Active CN114881924B (en) | 2022-03-30 | 2022-03-30 | Quantitative analysis device based on ultrasonic image elastic signals |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114881924B (en) |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112998748A (en) * | 2019-12-19 | 2021-06-22 | 通用电气精准医疗有限责任公司 | Method and system for strain automatic measurement and strain ratio calculation for ultrasonic elastography |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8055036B2 (en) * | 2005-05-27 | 2011-11-08 | Hitachi Medical Corporation | Ultrasonic diagnostic apparatus and ultrasonic image display method |
| CN103845081B (en) * | 2012-11-28 | 2018-04-10 | 深圳迈瑞生物医疗电子股份有限公司 | Ultrasonic elastograph imaging system and method, real-time dynamic interframe processing method |
| KR101654674B1 (en) * | 2013-11-28 | 2016-09-06 | 삼성전자주식회사 | Method and ultrasound apparatus for providing ultrasound elastography |
| GB2528283B (en) * | 2014-07-16 | 2020-08-05 | Barco Nv | Image colour calibration with multiple colour scales |
| CN105654490A (en) * | 2015-12-31 | 2016-06-08 | 中国科学院深圳先进技术研究院 | Lesion region extraction method and device based on ultrasonic elastic image |
| WO2018116892A1 (en) * | 2016-12-19 | 2018-06-28 | オリンパス株式会社 | Ultrasonic observation device, method for operating ultrasonic observation device, and program for operating ultrasonic observation device |
| JP6825432B2 (en) * | 2017-03-13 | 2021-02-03 | 富士通株式会社 | Analysis program, analysis method and analysis equipment |
-
2022
- 2022-03-30 CN CN202210323622.2A patent/CN114881924B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112998748A (en) * | 2019-12-19 | 2021-06-22 | 通用电气精准医疗有限责任公司 | Method and system for strain automatic measurement and strain ratio calculation for ultrasonic elastography |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114881924A (en) | 2022-08-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8131054B2 (en) | Computerized image analysis for acetic acid induced cervical intraepithelial neoplasia | |
| CN113855079B (en) | Real-time detection and auxiliary analysis method of breast diseases based on breast ultrasound images | |
| US8503747B2 (en) | Image analysis for cervical neoplasia detection and diagnosis | |
| CN108648825B (en) | A method for assessing vitiligo and leukoplakia based on image recognition | |
| CN102175625A (en) | Method for identifying cancer cells | |
| CN113450327B (en) | Blood vessel distribution information recognition device of thyroid nodule | |
| CN113450326B (en) | Blood vessel information extraction element of thyroid gland tubercle | |
| EP3139339A1 (en) | Health degree assessing device and health degree assessing system | |
| CN117974588A (en) | A deep learning model, construction method and system based on multimodal ultrasound images | |
| CN113080862A (en) | Tongue picture polarized light imaging technology-based traditional Chinese medicine diagnosis system and method | |
| CN111428066B (en) | Method for classifying and segmenting lesion image based on convolutional neural network | |
| CN114881924B (en) | Quantitative analysis device based on ultrasonic image elastic signals | |
| US20240065540A1 (en) | Apparatus and method for detecting cervical cancer | |
| CN114926389B (en) | Elastic signal extraction device for ultrasonic image | |
| CN114881922B (en) | Elastic signal calcification analysis device based on ultrasonic image | |
| CN112734749A (en) | Vocal leukoplakia auxiliary diagnosis system based on convolutional neural network model | |
| CN114881925B (en) | Elastic ultrasonic image-based benign and malignant nodule judgment device | |
| CN114881923B (en) | A nodule echo analysis device based on elastic signals of ultrasound images | |
| CN115018957B (en) | A virtual staining method for hyperspectral images of unstained sections | |
| CN113255781B (en) | Representative picture selecting method and device for CP-EBUS and diagnosis system | |
| CN114022429A (en) | Detection device is levied to mammary gland node branch leaf | |
| CN114022431A (en) | Mammary nodule roughness measuring device | |
| CN114155204A (en) | Breast nodule boundary sharpness judgment device | |
| CN119313725A (en) | Real-time positioning method, fusion display method and device for endoscopic image area | |
| Li et al. | Computerized image analysis for acetic acid induced intraepithelial lesions |
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 |