CN113808100A - Device for identifying rough calcification of breast nodules - Google Patents

Device for identifying rough calcification of breast nodules Download PDF

Info

Publication number
CN113808100A
CN113808100A CN202111085334.XA CN202111085334A CN113808100A CN 113808100 A CN113808100 A CN 113808100A CN 202111085334 A CN202111085334 A CN 202111085334A CN 113808100 A CN113808100 A CN 113808100A
Authority
CN
China
Prior art keywords
calcification
image
cals
grow
nodule
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
Application number
CN202111085334.XA
Other languages
Chinese (zh)
Other versions
CN113808100B (en
Inventor
韩东旭
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shiwei Xinzhi Medical Technology Shanghai Co ltd
Original Assignee
Shiwei Xinzhi Medical Technology Shanghai Co ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Shiwei Xinzhi Medical Technology Shanghai Co ltd filed Critical Shiwei Xinzhi Medical Technology Shanghai Co ltd
Priority to CN202111085334.XA priority Critical patent/CN113808100B/en
Publication of CN113808100A publication Critical patent/CN113808100A/en
Application granted granted Critical
Publication of CN113808100B publication Critical patent/CN113808100B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings
    • A61B8/0825—Detecting organic movements or changes, e.g. tumours, cysts, swellings for diagnosis of the breast, e.g. mammography
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/5207—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of raw data to produce diagnostic data, e.g. for generating an image
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/5215—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/5215—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data
    • A61B8/5223—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data for extracting a diagnostic or physiological parameter from medical diagnostic data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/12—Edge-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • 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
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • 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
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • G06T2207/20132—Image cropping
    • 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)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Surgery (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Veterinary Medicine (AREA)
  • General Physics & Mathematics (AREA)
  • Biophysics (AREA)
  • Pathology (AREA)
  • Biomedical Technology (AREA)
  • Theoretical Computer Science (AREA)
  • Molecular Biology (AREA)
  • Public Health (AREA)
  • Animal Behavior & Ethology (AREA)
  • Quality & Reliability (AREA)
  • Geometry (AREA)
  • Physiology (AREA)
  • Ultra Sonic Daignosis Equipment (AREA)

Abstract

The invention relates to a device for identifying rough calcification of breast nodules, which comprises: an image acquisition module: for acquiring an ultrasound image with a breast nodule; the interesting nodule image extraction module: the method comprises the steps that a breast nodule boundary of the ultrasonic image is intercepted in a mode of selecting a plurality of interested coordinate points, and an interested nodule image is obtained; a calcification identification module: for passing Bayesian ExcelleIdentifying calcifications in the interesting nodule image by a chemical algorithm to obtain an image Icals_grow(ii) a Calcification geometric descriptor construction module: for according to image Icals_growTo construct a calcification geometric descriptor; coarse calcification identification module: for identifying the image I by the calcification geometry descriptorcals_growWhether the medium calcification is coarse calcification. The method and the device can effectively identify the coarse calcification in the ultrasonic image.

Description

Device for identifying rough calcification of breast nodules
Technical Field
The invention relates to the technical field of auxiliary medical diagnosis, in particular to a device for identifying rough calcification of breast nodules.
Background
Nowadays, with the increasing demand for rapid and accurate diagnosis and the shortage of clinical staff, computer analysis methods have been increasingly applied to support routine clinical diagnosis and show good results.
It is expected that breast cancer will become the second leading cancer of life in women in recent years with a mortality rate of 15%. These statistics indicate that diagnosis of breast cancer is critical to improving life expectancy, especially in women. As a common clinical tool, ultrasound imaging is a non-invasive, non-radiative, low-cost cancer diagnostic technique. However, due to the low image quality, identifying breast lesions and detecting signs of cancer from ultrasound is a challenging task.
The growth and progression of malignant tumors can be reflected by their direction, appearance, texture, composition, and many other factors. As a well-used tool, grayscale Ultrasound (US) images can visualize many of these factors, helping physicians to better view and understand breast nodules. However, in current clinical practice, the features observed in ultrasound breast images can only be assessed subjectively or semi-subjectively, which limits the widespread use of ultrasound images. Therefore, automated accurate breast nodule quantitative analysis criteria are crucial for accurate cancer diagnosis.
The breast imaging report and data system (BI-RADS) is a guideline for scientific measurement and reporting of breast nodules. Unfortunately, there is currently no study of quantifying the BI-RADS features to improve the diagnostic performance of breast cancer classification.
Disclosure of Invention
The invention aims to provide a device for identifying rough calcification of a breast nodule, which can effectively identify the rough calcification in an ultrasonic image.
The technical scheme adopted by the invention for solving the technical problems is as follows: provided is a device for identifying rough calcification of a breast nodule, including:
an image acquisition module: for acquiring an ultrasound image with a breast nodule;
the interesting nodule image extraction module: the method comprises the steps that a breast nodule boundary of the ultrasonic image is intercepted in a mode of selecting a plurality of interested coordinate points, and an interested nodule image is obtained;
a calcification identification module: the method is used for identifying calcifications in the interested nodule image through a convolutional neural network to obtain an image Icals_grow；
Calcification geometric descriptor construction module: for according to image Icals_growTo construct a calcification geometric descriptor;
coarse calcification identification module: for identifying the image I by the calcification geometry descriptorcals_growWhether the medium calcification is coarse calcification.
A construction calcification geometric descriptor in the calcification geometric descriptor construction module, comprising:
calculating the image Icals_growThe area of each calcification in (a);
and constructing a calcification geometric descriptor according to the calculated area of each calcification point.
Computing the image I in the calcification geometry descriptor construction modulecals_growThe area of each calcification point in (a), the formula is:
Figure BDA0003265343200000021
wherein S isCCIndicates the area of calcifications, XCCSet of pixel coordinates representing a calcification, (x, y) representing image Icals_growThe coordinates of the pixel points in (1).
The calcification geometric descriptor constructing module constructs a calcification geometric descriptor according to the calculated area of each calcification point, and the formula is as follows:
Figure BDA0003265343200000022
wherein, CirCCIndicating the roundness of the calcifications, SCCIndicates the area of calcifications, XCCSet of pixel coordinates, X ', representing calcification points'CCSet of pixel coordinates, P, representing erosion by a butterfly-shaped structuring element of 1-pixel width within a calcificationCCShowing the perimeter of the calcified area (x, y)Display image Icals_growThe coordinates of the pixel points in (1).
Identifying the image I by the calcification geometry descriptor in the coarse calcification identification modulecals_growWhether the medium calcification is coarse calcification or not is specifically as follows: if the area S of the calcified pointCC＞tSAnd the roundness Cir of the calcified pointCC＞tCirThen, it indicates that the calcified point is coarse calcified point, tSDenotes the area threshold, tCirIndicating the roundness threshold.
Advantageous effects
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects: according to the invention, a calcification geometric descriptor is constructed according to calcification points detected in an ultrasonic image, and effective identification of coarse calcification is realized through the calcification geometric descriptor; the invention can facilitate doctors to accurately judge the pathological part and provide effective data support for doctors to accurately judge the pathological part better, faster and more accurately.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a schematic diagram of superpixel segmentation according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a coarse calcification identification result according to the embodiment of the present invention.
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
The embodiment of the present invention relates to a device for identifying rough calcification of breast nodules, referring to fig. 1, including:
an image acquisition module: for acquiring an ultrasound image with a breast nodule;
the interesting nodule image extraction module: the method comprises the steps that a breast nodule boundary of the ultrasonic image is intercepted in a mode of selecting a plurality of interested coordinate points, and an interested nodule image is obtained;
a calcification identification module: identifying calcification points in the nodule image of interest through a Bayesian optimization algorithm to obtain an image Icals_grow；
Calcification geometric descriptor construction module: for according to image Icals_growTo construct a calcification geometric descriptor;
coarse calcification identification module: for identifying the image I by the calcification geometry descriptorcals_growWhether the medium calcification is coarse calcification.
The invention is described in detail below by means of a specific embodiment:
in this embodiment, the calcification identification module further includes a calcification candidate detection module before the calcification identification module, and the calcification candidate detection module is configured to remove a boundary mask from the nodule image of interest to obtain an image I'erodeDividing the image into a plurality of superpixels, detecting calcification candidate points from each superpixel according to the contrast and the brightness to obtain an image Icals_raw。
The calcification candidate point detection module specifically comprises: the image I 'is divided by an image segmentation method of a k-means clustering method'erodeIs divided into 200 super pixels to obtain an image I'SP200And from said image I'SP200The super-pixel 100 before the average brightness is selected.
Will be from picture I'SP200The selected superpixel of which the average luminance is 100 th before is divided into 300 superpixels to obtain an image I'SP300And from said image I'SP300The super pixel 150 before the average brightness is selected.
Will be from picture I'SP300The selected superpixel 150 before the average brightness is divided into 750 superpixels to obtain an image I'SP750And from said image I'SP750The superpixel of contrast variance 375 ahead is selected and finally image I 'is further eroded by 6-pixel or 10-pixel (depending on nodule size) wide dish-shaped structuring elements'SP750Is limited byA mask for removing the potential false calcification candidate points detected at the edge to obtain an image I with the calcification candidate pointscals_raw。
As for the super-pixel segmentation, fig. 2 may be referred to, where (a) in fig. 2 is a nodule image of interest, and (b) in fig. 2 shows an image after the super-pixel segmentation.
For image Icals_rawAnd (3) cutting each connected domain (namely, the pixel point set of the region occupied by the calcifications) independently. Further, for image Icals_rawAnd (3) extracting the features of each connected domain which is cut separately, extracting two features which are respectively a histogram feature expressing the brightness feature expression of the candidate region and a gray level co-occurrence matrix feature expressing the texture feature expression of the candidate region, and combining the histogram feature and the gray level co-occurrence matrix feature to express that the total number is 6+14 to 20. The reasonable interval for extracting the characteristic value is determined by the statistical analysis of the characteristic expression difference between the true calcification and the false calcification and the Bayesian optimization algorithm. Carrying out feature extraction on each candidate calcification region by a Bayesian optimization algorithm, checking the feature extraction with the determined reasonable interval, and if the feature value is out of the defined interval, carrying out verification on the image Icals_rawDeleting the non-calcified area to obtain an image Icals_fine. This embodiment also applies to image I by a region growing methodcals_fineThe calcified points in the image are subjected to regional growth, the growing regions of the calcified points are limited through the energy range calibrated by the SURF descriptor, and finally a calcified image I is generatedcals_grow。
Based on clinical experience, coarse calcifications generally suggest a lower malignancy potential of the nodule than micro calcifications, and therefore identification of coarse and micro calcifications is required.
The coarse calcifications often appear in the ultrasonic image in a strip-shaped pattern, and based on this, in the calcific geometric descriptor constructing module, the size and roundness information of the calcifications are identified by constructing the calcific geometric descriptor, which specifically includes:
(one) calculating the image Icals_growThe area of each calcification point in (a), the formula is:
Figure BDA0003265343200000041
wherein S isCCIndicates the area of calcifications, XCCSet of pixel coordinates representing a calcification, (x, y) representing image Icals_growThe coordinates of the pixel points in (1).
(II) constructing a calcification geometric descriptor according to the calculated area of each calcification point, wherein the formula is as follows:
Figure BDA0003265343200000051
wherein, CirCCIndicating the roundness of the calcifications, SCCIndicates the area of calcifications, XCCSet of pixel coordinates, X ', representing calcification points'CCSet of pixel coordinates, P, representing erosion by a butterfly-shaped structuring element of 1-pixel width within a calcificationCCThe perimeter of the calcified region, (x, y) the image Icals_growThe coordinates of the pixel points in (1).
Due to CirCC∝SCCTherefore, it should be noted that the circularity Cir of the connected domain with smaller size is larger than the circularity Cir of the connected domain with larger size (i.e. the set of pixels in the region occupied by the calcifications)CCAnd is smaller. Therefore, in applying roundness CirCCWhen making measurements, it is necessary to set a minimum size limit, since smaller connected domains are less likely to be coarse calcifications. That is, in the coarse calcification identification module, if the area S of the calcification pointCC＞tSAnd the roundness Cir of the calcified pointCC＞tCirThen, it indicates that the calcified point is coarse calcified point, tSDenotes the area threshold, tCirIndicating the roundness threshold. Through experiments, the present embodiment sets the area threshold to be: t is tS95; the roundness threshold is set to: t is tCir＝0.78。
Fig. 3 (a) shows calcifications detected from the ultrasound image, which are three punctiform calcifications and one bar-shaped calcifications, respectively, and the bright white calcifications in fig. 3 (b) are linear calcifications, i.e., coarse calcifications.
Therefore, the invention constructs the calcification geometric descriptor according to the calcification points detected in the ultrasonic image, and realizes effective identification of coarse calcification through the calcification geometric descriptor.

Claims (5)

1. A device for identifying rough calcification of breast nodules, comprising:
an image acquisition module: for acquiring an ultrasound image with a breast nodule;
the interesting nodule image extraction module: the method comprises the steps that a breast nodule boundary of the ultrasonic image is intercepted in a mode of selecting a plurality of interested coordinate points, and an interested nodule image is obtained;
a calcification identification module: identifying calcification points in the nodule image of interest through a Bayesian optimization algorithm to obtain an image Icals_grow；
Calcification geometric descriptor construction module: for according to image Icals_growTo construct a calcification geometric descriptor;
coarse calcification identification module: for identifying the image I by the calcification geometry descriptorcals_growWhether the medium calcification is coarse calcification.
2. The apparatus for identifying rough calcification of breast nodules according to claim 1, wherein the constructing a calcification geometric descriptor in the calcification geometric descriptor constructing module comprises:
calculating the image Icals_growThe area of each calcification in (a);
and constructing a calcification geometric descriptor according to the calculated area of each calcification point.
3. The apparatus for identifying rough calcification of breast nodules according to claim 2, wherein said calculating said image I in said calcification geometric descriptor constructing modulecals_growThe area of each calcification point in (a), the formula is:
Figure FDA0003265343190000011
wherein S isCCIndicates the area of calcifications, XCCSet of pixel coordinates representing a calcification, (x, y) representing image Icals_growThe coordinates of the pixel points in (1).
4. The apparatus for identifying rough calcification of breast nodules according to claim 2, wherein the calcification geometric descriptor constructing module constructs a calcification geometric descriptor according to the calculated area of each calcification, and the formula is as follows:
Figure FDA0003265343190000012
wherein, CirCCIndicating the roundness of the calcifications, SCCIndicates the area of calcifications, XCCSet of pixel coordinates, X ', representing calcification points'CCSet of pixel coordinates, P, representing erosion by a butterfly-shaped structuring element of 1-pixel width within a calcificationCCThe perimeter of the calcified region, (x, y) the image Icals_growThe coordinates of the pixel points in (1).
5. The apparatus according to claim 1, wherein the coarse calcification identification module identifies the image I by the calcification geometric descriptorcals_growWhether the medium calcification is coarse calcification or not is specifically as follows: if the area S of the calcified pointCC＞tSAnd the roundness Cir of the calcified pointCC＞tCirThen, it indicates that the calcified point is coarse calcified point, tSDenotes the area threshold, tCirIndicating the roundness threshold.
CN202111085334.XA 2021-09-16 2021-09-16 Breast nodule coarse calcification recognition device Active CN113808100B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111085334.XA CN113808100B (en) 2021-09-16 2021-09-16 Breast nodule coarse calcification recognition device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111085334.XA CN113808100B (en) 2021-09-16 2021-09-16 Breast nodule coarse calcification recognition device

Publications (2)

Publication Number Publication Date
CN113808100A true CN113808100A (en) 2021-12-17
CN113808100B CN113808100B (en) 2024-03-19

Family

ID=78895502

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111085334.XA Active CN113808100B (en) 2021-09-16 2021-09-16 Breast nodule coarse calcification recognition device

Country Status (1)

Country Link
CN (1) CN113808100B (en)

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020196967A1 (en) * 2001-06-13 2002-12-26 Fuji Photo Film Co., Ltd. Prospective abnormal shadow detecting system
US20080152220A1 (en) * 2006-12-21 2008-06-26 Fujifilm Corporation Mass segmentation using mirror image of region of interest
CN101727537A (en) * 2009-11-16 2010-06-09 杭州电子科技大学 Computer determining method of mammary gland CR image based on double visual angle information fusion
US20120121159A1 (en) * 2010-11-10 2012-05-17 Hernandez-Cisneros Rolando-Rafael Method for the detection and classification of microcalcification clusters in digital mammograms
CN104637044A (en) * 2013-11-07 2015-05-20 中国科学院深圳先进技术研究院 Ultrasonic image extracting system for calcified plaque and sound shadow thereof
CN110264461A (en) * 2019-06-25 2019-09-20 南京工程学院 Microcalciffcation point automatic testing method based on ultrasonic tumor of breast image
CN111832563A (en) * 2020-07-17 2020-10-27 江苏大学附属医院 Intelligent breast tumor identification method based on ultrasonic image
CN112001895A (en) * 2020-08-03 2020-11-27 什维新智医疗科技(上海)有限公司 Thyroid calcification detection device
CN113379739A (en) * 2021-07-23 2021-09-10 平安科技（深圳）有限公司 Ultrasonic image identification method, device, equipment and storage medium

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020196967A1 (en) * 2001-06-13 2002-12-26 Fuji Photo Film Co., Ltd. Prospective abnormal shadow detecting system
US20080152220A1 (en) * 2006-12-21 2008-06-26 Fujifilm Corporation Mass segmentation using mirror image of region of interest
CN101727537A (en) * 2009-11-16 2010-06-09 杭州电子科技大学 Computer determining method of mammary gland CR image based on double visual angle information fusion
US20120121159A1 (en) * 2010-11-10 2012-05-17 Hernandez-Cisneros Rolando-Rafael Method for the detection and classification of microcalcification clusters in digital mammograms
CN104637044A (en) * 2013-11-07 2015-05-20 中国科学院深圳先进技术研究院 Ultrasonic image extracting system for calcified plaque and sound shadow thereof
CN110264461A (en) * 2019-06-25 2019-09-20 南京工程学院 Microcalciffcation point automatic testing method based on ultrasonic tumor of breast image
CN111832563A (en) * 2020-07-17 2020-10-27 江苏大学附属医院 Intelligent breast tumor identification method based on ultrasonic image
CN112001895A (en) * 2020-08-03 2020-11-27 什维新智医疗科技(上海)有限公司 Thyroid calcification detection device
CN113379739A (en) * 2021-07-23 2021-09-10 平安科技（深圳）有限公司 Ultrasonic image identification method, device, equipment and storage medium

Also Published As

Publication number Publication date
CN113808100B (en) 2024-03-19

Similar Documents

Publication Publication Date Title
Maitra et al. Technique for preprocessing of digital mammogram
US7876947B2 (en) System and method for detecting tagged material using alpha matting
George et al. Automated cell nuclei segmentation for breast fine needle aspiration cytology
WO2022063198A1 (en) Lung image processing method, apparatus and device
CN107798679B (en) Breast region segmentation and calcification detection method for mammary gland molybdenum target image
Taha et al. Automatic polyp detection in endoscopy videos: A survey
US7480401B2 (en) Method for local surface smoothing with application to chest wall nodule segmentation in lung CT data
Rajkumar et al. Automated mammogram segmentation using seed point identification and modified region growing algorithm
Duarte et al. Segmenting mammographic microcalcifications using a semi-automatic procedure based on Otsu's method and morphological filters
KR101396308B1 (en) A method for extracting morphological features for grading pancreatic ductal adenocarcinomas
CN113450326B (en) Blood vessel information extraction element of thyroid gland tubercle
CN113808100B (en) Breast nodule coarse calcification recognition device
CN113393452B (en) Thyroid nodule blood vessel number detection device
CN113808101B (en) Breast nodule calcification analysis device
KR101527604B1 (en) A Method for Grading Pancreatic Ductal Adenocarcinomas Based On Morphological Features
CN113940704A (en) Thyroid-based muscle and fascia detection device
Siddique et al. Effective Segmentation of Liver CT images using Marker Controlled Watershed Algorithm
CN113808102B (en) Breast nodule calcification shadow recognition device
Adam et al. Epithelial layer estimation using curvatures and textural features for dysplastic tissue detection
CN113822864A (en) Mammary nodule calcification detection device
CN114155204A (en) Breast nodule boundary sharpness judgment device
CN114022429A (en) Detection device is levied to mammary gland node branch leaf
CN114155205A (en) Mammary nodule boundary definition judging device
CN114155206A (en) Breast nodule boundary information extraction element
CN113450325B (en) Thyroid nodule benign and malignant recognition device

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
CB02 Change of applicant information
CB02 Change of applicant information

Address after: 201114 room 101-8, building 3, No. 2388, Chenhang Road, Minhang District, Shanghai

Applicant after: Shiwei Xinzhi medical technology (Shanghai) Co.,Ltd.

Address before: 1628 suzhao Road, Minhang District, Shanghai 201112

Applicant before: Shiwei Xinzhi medical technology (Shanghai) Co.,Ltd.

GR01 Patent grant
GR01 Patent grant