CN107016660A - 旋转式乳房影像的病变检测方法及病变检测装置 - Google Patents
旋转式乳房影像的病变检测方法及病变检测装置 Download PDFInfo
- Publication number
- CN107016660A CN107016660A CN201610283378.6A CN201610283378A CN107016660A CN 107016660 A CN107016660 A CN 107016660A CN 201610283378 A CN201610283378 A CN 201610283378A CN 107016660 A CN107016660 A CN 107016660A
- Authority
- CN
- China
- Prior art keywords
- image
- breast
- breast image
- lesion
- rotary
- 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
- 210000000481 breast Anatomy 0.000 title claims abstract description 152
- 238000001514 detection method Methods 0.000 title claims abstract description 51
- 230000036285 pathological change Effects 0.000 title 2
- 231100000915 pathological change Toxicity 0.000 title 2
- 230000003902 lesion Effects 0.000 claims abstract description 103
- 238000000034 method Methods 0.000 claims abstract description 14
- 238000012545 processing Methods 0.000 claims description 25
- 230000011218 segmentation Effects 0.000 claims description 18
- 238000012217 deletion Methods 0.000 claims description 9
- 230000037430 deletion Effects 0.000 claims description 9
- 201000010099 disease Diseases 0.000 claims description 4
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 11
- 230000006798 recombination Effects 0.000 description 11
- 238000005215 recombination Methods 0.000 description 11
- 206010028980 Neoplasm Diseases 0.000 description 9
- 238000012216 screening Methods 0.000 description 5
- 206010006187 Breast cancer Diseases 0.000 description 4
- 208000026310 Breast neoplasm Diseases 0.000 description 3
- 230000002308 calcification Effects 0.000 description 3
- 238000002595 magnetic resonance imaging Methods 0.000 description 3
- 239000000523 sample Substances 0.000 description 3
- 238000002604 ultrasonography Methods 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 201000011510 cancer Diseases 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000009607 mammography Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000005192 partition Methods 0.000 description 2
- 238000003908 quality control method Methods 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 208000024891 symptom Diseases 0.000 description 2
- 101000911390 Homo sapiens Coagulation factor VIII Proteins 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000001574 biopsy Methods 0.000 description 1
- 201000008275 breast carcinoma Diseases 0.000 description 1
- 230000003139 buffering effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000002059 diagnostic imaging Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 239000012634 fragment Substances 0.000 description 1
- 102000057593 human F8 Human genes 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 239000002674 ointment Substances 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 229940047431 recombinate Drugs 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000028327 secretion Effects 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 238000003325 tomography Methods 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
- G06T5/00—Image enhancement or restoration
- G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/43—Detecting, measuring or recording for evaluating the reproductive systems
- A61B5/4306—Detecting, measuring or recording for evaluating the reproductive systems for evaluating the female reproductive systems, e.g. gynaecological evaluations
- A61B5/4312—Breast evaluation or disorder diagnosis
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
- A61B6/025—Tomosynthesis
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
- A61B6/502—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of breast, i.e. mammography
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
- A61B6/5205—Devices using data or image processing specially adapted for radiation diagnosis involving processing of raw data to produce diagnostic data
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
- A61B6/5211—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/003—Reconstruction from projections, e.g. tomography
- G06T11/006—Inverse problem, transformation from projection-space into object-space, e.g. transform methods, back-projection, algebraic methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/003—Reconstruction from projections, e.g. tomography
- G06T11/008—Specific post-processing after tomographic reconstruction, e.g. voxelisation, metal artifact correction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
- G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
- G06T7/337—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods involving reference images or patches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
- G06T7/41—Analysis of texture based on statistical description of texture
- G06T7/45—Analysis of texture based on statistical description of texture using co-occurrence matrix computation
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2576/00—Medical imaging apparatus involving image processing or analysis
- A61B2576/02—Medical imaging apparatus involving image processing or analysis specially adapted for a particular organ or body part
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
- A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
- A61B6/5258—Devices using data or image processing specially adapted for radiation diagnosis involving detection or reduction of artifacts or noise
-
- 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
-
- 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
- G06T2207/10016—Video; Image sequence
-
- 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
- G06T2207/10072—Tomographic images
-
- 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
- G06T2207/10072—Tomographic images
- G06T2207/10088—Magnetic resonance imaging [MRI]
-
- 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
- G06T2207/20076—Probabilistic image processing
-
- 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
- G06T2207/20152—Watershed segmentation
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/03—Recognition of patterns in medical or anatomical images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/03—Recognition of patterns in medical or anatomical images
- G06V2201/032—Recognition of patterns in medical or anatomical images of protuberances, polyps nodules, etc.
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- Medical Informatics (AREA)
- Theoretical Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Biomedical Technology (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Pathology (AREA)
- Heart & Thoracic Surgery (AREA)
- Biophysics (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- Molecular Biology (AREA)
- Veterinary Medicine (AREA)
- Radiology & Medical Imaging (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Optics & Photonics (AREA)
- High Energy & Nuclear Physics (AREA)
- Reproductive Health (AREA)
- Mathematical Physics (AREA)
- Gynecology & Obstetrics (AREA)
- Data Mining & Analysis (AREA)
- Quality & Reliability (AREA)
- Dentistry (AREA)
- Computing Systems (AREA)
- Artificial Intelligence (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Probability & Statistics with Applications (AREA)
- Algebra (AREA)
- Mathematical Analysis (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Physiology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Primary Health Care (AREA)
- Databases & Information Systems (AREA)
Abstract
本发明提出一种旋转式乳房影像的病变检测方法及病变检测装置。此方法包括下列步骤。取得一组旋转式乳房影像。此组旋转式乳房影像包括多张子乳房影像。对这些子乳房影像进行重组,以产生重组乳房影像。比对重组乳房影像以及未重组的旋转式乳房影像以确认至少一个病变位置。本发明可方便人员检视三维乳房影像且降低病变检测的伪阳性。
Description
技术领域
本发明涉及一种医疗图像处理技术,尤其涉及一种旋转式乳房影像的病变(lesion)检测方法及病变检测装置。
背景技术
乳腺癌(mammary carcinoma)是女性常见的恶性肿瘤之一,其主要症状包括乳房肿瘤(tumor)、异常分泌物或形状变异等。提早筛检出乳房的异常症状,将有助于尽早针对肿瘤进行治疗,以降低癌细胞恶化或扩散等问题。诸如临床或自我乳房检测、活体组织检查、乳房摄影术(mammography)、超音波(ultrasound)或磁共振(magnetic resonance)显像等筛检方式已广泛在临床上使用或成为学术研究的重要议题。
据研究指出,与低密度乳房相比,拥有高密度乳房的女性具有相对高的风险罹患乳癌。因此,乳房及乳腺组织的密度分析亦是乳癌评估的重要因素之一。另一方面,虽然现今临床上已使用计算机辅助检测(Computer AidedDetection;CADe)系统来自动辨识肿瘤、肿块或钙化点,但是都存在高伪阳性的风险。
发明内容
本发明提供一种旋转式乳房影像的病变检测方法及病变检测装置,其可有效降低计算机辅助检测系统的伪阳性。
本发明提出一种旋转式乳房影像的病变检测方法,且至少包括(但不仅限于)下列步骤。取得一组旋转式乳房影像。此组旋转式乳房影像包括多张子乳房影像。对这些子乳房影像进行重组,以产生重组乳房影像。比对重组乳房影像以及未重组的组旋转式乳房影像以确认至少一个病变位置。
另一观点而言,本发明提出一种病变检测装置,其至少包括(但不仅限于)储存单元及处理单元。而处理单元耦接储存单元,且取得一组旋转式乳房影像。而此组旋转式乳房影像包括多张子乳房影像。处理单元对这些子乳房影像进行重组,以产生重组乳房影像。处理单元比对重组乳房影像以及未重组的旋转式乳房影像以确认至少一个病变位置。
基于上述,本发明实施例所提出的旋转式乳房影像的病变检测方法及病变检测装置,其将旋转式乳房影像重组,并将重组乳房影像及旋转式乳房影像进行比对,以确认病变(例如,肿瘤、肿块或钙化点等)位置。据此,本发明实施例便能助于减少计算机辅助检测系统的伪阳性。
为让本发明的上述特征和优点能更明显易懂,下文特举实施例,并配合附图作详细说明如下。
附图说明
图1是依据本发明一实施例说明病变检测装置的方框图;
图2是依据本发明一实施例说明一种旋转式乳房影像的病变检测方法流程图;
图3是旋转式扫描的示意图范例;
图4为旋转式乳房影像于立体空间的示意图;
图5A~图5C是依据本发明一实施例说明填补作业的示意图;
图6是依据本发明一实施例说明自动化病变检测方法的流程图;
图7A是未重组的旋转式乳房影像的局部影像;
图7B是区域分割影像;
图7C所示为经图7B的区域分割影像所判断出的可疑病变区域;
图7D所示为图7C经伪阳性减少作业筛选的病变位置;
图8是一范例说明决定厚度区域的示意图;
图9A是一范例说明投影影像;
图9B是一范例说明分割后的投影影像。
附图标记:
100:病变检测装置
110:储存单元
111:影像输入模块
113:影像重组模块
115:病变判断模块
117:影像质量模块
150:处理单元
S210~S250、S610~S670:步骤
301:乳房
320:乳房取像容器
340:扫描仪
410、810:圆柱体
431、432:子乳房影像
501:区隔位置
503:缺失位置
520:矩形
530:影像集合
710:旋转式乳房影像
711:肿瘤
730:区域分割影像
731:可疑病变区域
733:病变位置
811:厚度区域
910、920:投影影像
911:目标区块
921:皮肤组织类别区块
923:拍摄失误类别区块
r:半径
θ:旋转角度
TH:厚度值
H:子乳房影像的高
W:子乳房影像的宽
RD1:旋转方向
具体实施方式
图1是依据本发明一实施例说明病变检测装置的方框图。请参照图1,病变检测装置100至少包括(但不仅限于)储存单元110及处理单元150。病变检测装置100可以是服务器、客户端、桌面计算机、笔记本电脑、网络计算机、工作站、个人数字助理(personal digital assistant;PDA)、平板个人计算机(personal computer;PC)、计算机辅助检测(CADe)系统等电子装置,且不以此为限。
储存单元110可以是任何型态的固定或可移动随机存取内存(randomaccess memory,RAM)、只读存储器(read-only memory,ROM)、闪存(flashmemory)或类似元件或上述元件的组合。在本实施例中,储存单元110用以储存旋转式乳房影像、子乳房影像、重组乳房影像、扫描参数、程序代码、装置组态、缓冲的或永久的数据,并记录影像输入模块111、影像重组模块113、病变判断模块115及影像质量模块117等软件程序。前述模块的详细运作内容待稍后实施例详细说明。本实施例中所述的储存单元110并未限制是单一内存元件,上述的各软件模块亦可以分开储存在两个或两个以上相同或不同型态的内存元件中。
处理单元150的功能可藉由使用诸如中央处理单元(central processingunit;CPU)、微处理器、微控制器、数字信号处理(digital signal processing;DSP)芯片、场可程序化逻辑门阵列(Field Programmable Gate Array;FPGA)等可程序化单元来实施。处理单元150的功能亦可用独立电子装置或集成电路(integrated circuit;IC)实施,且处理单元150亦可用硬件或软件实施,存取储存单元110中的各模块、软件程序、程序代码等并执行对应操作。
为了方便理解本发明实施例的操作流程,以下将举诸多实施例详细说明本发明实施例中病变检测装置100进行乳房图像处理及病变检测的流程。图2是依据本发明一实施例说明一种旋转式乳房影像的病变检测方法流程图。请参照图2,本实施例的方法适用于图1中的病变检测装置100。下文中,将搭配病变检测装置100中的各项元件及模块说明本发明实施例所述的方法。本方法的各个流程可依照实施情形而随之调整,且并不仅限于此。
在步骤S210中,处理单元150通过影像输入模块111取得一组旋转式乳房影像。而此组旋转式乳房影像包括多张(或片段(slice))子乳房影像。在本发明实施例中,这些子乳房影像是通过扫描仪绕着乳房取像容器下方进行旋转扫描一圈的过程中所分别取得。此扫描仪例如是具有基于自动乳房超音波(automated breast ultrasound;ABUS)、数字乳房断层层析(digital breasttomosynthesis;DBT)、磁共振显影(magnetic resonance imaging:MRI)等医疗影像扫描技术的探头(probe)。针对超音波扫描,此乳房取像容器中可装载液体或水溶性软膏等来作为超音波传导媒介。
举例而言,图3是旋转式扫描的示意图范例。请参照图3,使用者以俯卧方式,使其乳房301可完全或部分地置于圆柱型的乳房取像容器320中。具有探头的可移动式扫描仪340置于(固定式)乳房取像容器320的圆柱型底部下方,且可通过机械装置(未显示)来驱动其以旋转方向RD1(即,顺时针)或旋转方向RD1的相反方向(即,逆时针)旋转至少一圈(360°),以使扫描范围能涵盖乳房301的全部或部分投影面积。在扫描的过程中,扫描仪340会随着每旋转一旋转角度(例如,3°、5°、8°等)对乳房301扫描以取得一张子乳房影像。例如,旋转角度为3°,则扫描仪340旋转一圈(360°)后便可取得120张子乳房影像。
需说明的是,在进行旋转扫描之前,可默认或手动调整扫描参数。此扫描参数至少包括(但不仅限于)影像扫描起始位置、旋转方向(顺时针或是逆时针)、旋转角度等,且可记录于储存单元110中以备后续使用。
影像输入模块111可自储存单元110、通过无线或有线通信单元(例如,Wi-Fi、以太网(Ethernet))、直接通过图3所述医学影像扫描仪(例如,ABUS扫描仪器、MRI扫描仪器等)或储存装置(例如,DVD、随身碟、硬盘等)取得旋转式乳房影像。
在步骤S230中,处理单元150通过影像重组模块113对这些子乳房影像进行重组,以产生重组乳房影像。为了让通过旋转方式取得的旋转乳房影像能够依照不同方向的视角(view)来观看,本发明实施例依据旋转式扫描的旋转特性来进行重组。
在一实施例中,影像重组模块113将依据扫描仪的扫描起始位置、旋转角度及旋转方向将这些子乳房影像转换成影像集合,判断各相邻的子乳房影像之间的间隔位于此影像集合中的缺失位置,且通过内插法补足此缺失位置。
具体而言,请先参照图4所示为旋转式乳房影像于立体空间的示意图。为了方便说明,图4中仅以两张子乳房影像431、子乳房影像432进行说明,然不以此为限。每张子乳房影像的宽W即为旋转半径(例如,图3中扫描仪340单次可扫描的长度),而其宽H为扫描仪可扫描的最大深度。影像重组模块113将旋转扫描一圈的每张子乳房影像(例如,子乳房影像431、子乳房影像432)依据先前记录或默认的扫描参数(例如,影像扫描起始位置、顺时针或是逆时针旋转、旋转角度等),以各自对应的扫描位置依序排列,以形成圆柱体410。此时,圆柱体410中例如是两张子乳房影像431、子乳房影像432之间存在间隔(即,未存在像素或扫描影像)。
接着,影像重组模块113对这些间隔进行填补作业。图5A~图5C是依据本发明一实施例说明填补作业的示意图。请先参照图5A,影像重组模块113将图4中数张子乳房影像所形成的圆柱体410建立二维直角坐标系统。例如,圆柱体410的圆形顶所属平面的二维直角坐标系统。
影像重组模块113可将此二维直角坐标系统转换成以旋转角度及半径长度的坐标系统。具体而言,首先,定义相邻两子乳房影像中区隔位置501在直角坐标系统的坐标为(x1,y2)(以圆柱体410中圆形顶的中心点为原点(0,0)且影像扫描起始位置至圆形顶的中心点的沿线为x轴(或称水平轴))、圆柱体410中圆形顶的直径宽Width(即,2*W)及高Height(即,H)。接着,影像重组模块113将坐标(x1,y1)以下列公式(1)(勾股定理)及(2)进行转换,以取得坐标(r1,θ1):
r1代表此位置相距圆柱体410中圆形顶边缘的距离,而θ1代表自扫描起始位置所旋转的角度。
藉由前述转换,影像重组模块113将部分或全部的子乳房影像映射成影像集合。请参照图5B,此坐标系统的水平轴为(自扫描起始位置起始所旋转的)旋转角度θ,而垂直轴为半径r。多张子乳房影像依旋转方向在此r-θ坐标系统上依序排列,并重组成影像集合530。例如,图4中旋转方向RD1为顺时针,则在影像集合中子乳房影像431将位于子乳房影像432左侧。而图5A中的区隔位置501亦可映射至影像集合530中缺失位置503。
请接着参照图5C为影像集合530的局部放大示意图。影像重组模块113可通过诸如双线性内插法(bilinear interpolation)、双立方内插法(bicubicinterpolation)、最邻近点(nearest)内插法等来补足此缺失位置503。而在补足所有相邻子乳房影像间的间隔之后,便可形成完整的(例如,无缺失位置)影像集合。影像重组模块113可以此影像集合再将自r-θ坐标系统转换回如图5A的呈现方式,以形成三维的重组乳房影像。而此重组乳房影像并不会存在如图4所示相邻两子乳房影像之间的间隔。
而处理单元150可进一步通过显示单元(未显示,例如,液晶显示器(LCD)、电浆显示器面板(PDP)、有机发光二极管(OLED)等)显示此重组乳房影像,并可通过输入单元(未显示,例如,触控装置、键盘、鼠标等)接收用户的输入操作来以不同方向(例如,图5A中矩形520是用以显示的方向)的视角检视重组乳房影像。
在步骤S250中,处理单元150通过病变判断模块115比对重组乳房影像以及未重组的旋转式乳房影像以确认至少一个病变位置。在本实施例中,使用基于区域(region based)的病变(例如,肿瘤、肿块或钙化点等)检测方法,对旋转式乳房影像进行自动检测。通过所提出的区域筛选方式,可对可疑病变区域进行条件筛选以找出病变位置。
图6是依据本发明一实施例说明自动化病变检测方法的流程图。请参照图6,在步骤S610中,病变判断模块115对未重组的旋转式乳房影像进行区域分割,以产生区域分割影像。病变判断模块115可利用原始影像(即,未重组的旋转式乳房影像中的一片段子乳房影像)进行基于像素或纹路差异的区域分割(例如,分水岭(watershed)切割、马可夫随机场(Markov RandomField;MRF))。
例如,图7A是未重组的旋转式乳房影像的局部影像。假设旋转式乳房影像710中存在肿瘤711。病变判断模块115对旋转式乳房影像710进行区域分割后,形成如图7B所示的区域分割影像730。区域分割影像730中具有相同或相似纹路特征的像素受划分为相同区域(以相同颜色表示)。需说明的是,区域分割算法中的各项参数可依据需求自行调整,本发明实施例不对此限制。
在步骤S630中,处理单元150通过病变判断模块115判断区域分割影像中的至少一个可疑病变区域。具体而言,病变判断模块115将旋转式乳房影像切割后,区域分割影像仍存在许多不必要的区块,且目标病变区块亦包含于其中。病变判断模块115可利用各区块的像素值特征(例如,平均值、最大值、最小值、中位数、变异数等),依据所欲检测的目标特性(例如,较暗区域、近似椭圆形、长短轴比例等)来进行初步筛选,以决定可疑病变区域,此步骤中所使用的影像各个区块的特征以及特性将可依照需求进行调整。例如,图7C所示为经图7B的区域分割影像730所判断出的可疑病变区域731。
在步骤S650中,病变判断模块115对可疑病变区域进行伪阳性减少作业,以判断病变位置。具体而言,经初步的筛选后,病变判断模块115利用已经筛选过后的可疑病变区块再次对所剩区块进行伪阳性减少(false-positivereduction)。伪阳性减少作业所使用特征可至少包括(但不仅限于)三个部分:形状(例如,面积、长短轴比率)、像素强度(例如,平均值、标准偏差)以及纹理(例如,灰阶共生矩阵(Gray-Level Co-occurrence Matrix;GLCM)、马可夫随机场(Markov Random Field;MRF)、贾柏滤波器(Gabor Filter)。换句而言,病变判断模块115可基于默认或经由手动选择的特征,对所剩区块进行筛选,以比对出目标病变位置。例如,图7D所示为图7C经伪阳性减少作业筛选的病变位置733。
在步骤S670中,病变判断模块115依据病变位置寻找重组乳房影像中是否存在相连的病变区域,以确认此病变位置。具体而言,病变判断模块115将决定的病变位置(例如,图7D中的病变位置733)与三维的重组乳房影像比对。若在此重组乳房影像中具有相连的病变区域(即,与病变位置相对应的区域),则代表实际存在此病变(或是存在的机率高于80%、90%等)。而若在此重组乳房影像中未具有相连的病变区域,则代表未存在病变(或是存在的机率低于10%、15%等)。
在一些实施例中,处理单元150可进一步通过显示单元呈现诸如病变位置、可疑病变区域、病变区域、发现病变的提示信息(例如,“发现肿瘤!”)等其中的一个或其组合,以协助医疗人员清楚得知检测状况。
此外,为了维持影像扫描的质量控制(quality control),在一些实施例中,处理单元150通过影像质量模块117可进一步判断旋转式乳房影像是否完整或拍摄失误过高(例如,失误比例大于70%、80%等)。影像质量模块117可对旋转式乳房影像中的部分厚度区域的影像进行垂直投影,以产生投影影像,并依据投影影像中的拍摄失误类别与皮肤组织类别的比值,判断旋转式乳房影像的影像质量。
具体而言,在步骤S210或步骤S230之后或是步骤S250之前,处理单元150可依据影像质量模块117所判断影像质量的结果,来决定是否继续进行后续的病变检测。影像质量模块117可自三维的重组乳房影像或例如图4形成圆柱体410的子乳房影像集合中决定厚度值(例如,2公分、5公分等,不同使用者可能有所不同)的厚度区域。
例如,图8是一范例说明决定厚度区域的示意图。影像质量模块117自形成圆柱体810的子乳房影像集合或是重组乳房影像上方决定厚度值TH,从而决定厚度区域811。
影像质量模块117接着对已决定的厚度区域(例如,图8的厚度区域811)的影像进行垂直投影,其可选择各位置中以垂直方向的所有像素中具有最低像素值,来分别作为各位置上的值。
影像质量模块117可进一步去除皮肤组织外围不必要的区域。例如,图9A是一范例说明投影影像。请参照图9A,投影影像910中的目标区块911以外的影像视为不必要的区域。
影像质量模块117可依据目标区域内的像素特征,利用利用区域分割方法(例如,分水岭分割、马可夫随机场等),将投影影像区分为皮肤组织类别区块与拍摄失误类别区块。例如,图9B是一范例说明分割后的投影影像。请参照图9B,经分割后的投影影像920包括皮肤组织类别区块921与拍摄失误类别区块923。影像质量模块117可利用两个类别的面积来产生比值(例如,拍摄失误类别区块923面积总和/皮肤组织类别区块921面积总和)用于决定此影像扫描质量的优劣程度。例如,若此比值大于质量门槛值(例如,30%、20%等),则影像质量模块117判断质量不佳,进而重新进行扫描作业。例如,通过显示单元显示“建议重新扫描!”的提示信息。反之,若比值小于质量门槛值(例如,15%、30%等),则影像质量模块117判断质量良好,且可进行例如步骤S250的病变检测作业。
综上所述,本发明实施例所提出的旋转式乳房影像的病变检测方法及病变检测装置,其是将旋转式乳房影像进一步重组,以方便医疗人员能以不同方向的视角来检视。利用基于区域的病变检测方法能协助筛选病变位置,且可进一步与三维的重组乳房影像进行比对,以降低伪阳性。此外,本发明实施例更能通过影像质量判断,从而维持影像扫描质量。
虽然本发明已以实施例揭示如上,然其并非用以限定本发明,任何所属技术领域中普通技术人员,在不脱离本发明的精神和范围内,当可作些许的改动与润饰,故本发明的保护范围当视所附权利要求界定范围为准。
Claims (10)
1.一种旋转式乳房影像的病变检测方法,其特征在于,包括:
取得旋转式乳房影像,其中所述旋转式乳房影像包括多张子乳房影像;
对所述多个子乳房影像进行重组,以产生重组乳房影像;以及
比对所述重组乳房影像以及未重组的该组旋转式乳房影像以确认至少一病变位置。
2.根据权利要求1所述的病变检测方法,其特征在于,取得所述组旋转式乳房影像的步骤之前,还包括:
通过扫描仪绕着乳房取像容器下方进行旋转扫描一圈的过程中,分别取得所述多个子乳房影像。
3.根据权利要求2所述的病变检测方法,其特征在于,对所述多个子乳房影像进行重组的步骤包括:
依据该扫描仪的扫描起始位置、旋转角度及旋转方向,将所述多个子乳房影像转换成影像集合;
判断各相邻的所述多个子乳房影像之间的间隔位于所述影像集合中的缺失位置;以及
通过内插法补足所述缺失位置。
4.根据权利要求1所述的病变检测方法,其特征在于,比对所述重组乳房影像以及未重组的所述组旋转式乳房影像以确认该至少一病变位置的步骤包括:
对未重组的所述组旋转式乳房影像进行区域分割,以产生区域分割影像;
判断所述区域分割影像中的至少一可疑病变区域;
对所述至少一可疑病变区域进行伪阳性减少作业,以判断所述至少一病变位置;以及。
依据所述至少一病变位置寻找所述重组乳房影像中是否存在相连的病变区域,以确认所述至少一病变位置。
5.根据权利要求1所述的病变检测方法,其特征在于,取得所述组旋转式乳房影像的步骤之后,还包括:
对所述组旋转式乳房影像中的部分厚度区域的影像进行垂直投影,以产生投影影像;以及
依据该投影影像中的拍摄失误类别与皮肤组织类别的比值,判断所述组旋转式乳房影像的影像质量。
6.一种病变检测装置,其特征在于,包括:
储存单元;以及
处理单元,耦接所述储存单元,取得旋转式乳房影像,其中所述旋转式乳房影像包括多张子乳房影像,对所述多个子乳房影像进行重组,以产生重组乳房影像,且比对所述重组乳房影像以及未重组的所述组旋转式乳房影像以确认至少一病变位置。
7.根据权利要求6所述的病变检测装置,其特征在于,所述多个子乳房影像是通过扫描仪绕着乳房取像容器下方进行旋转扫描一圈的过程中所分别取得。
8.根据权利要求7所述的病变检测装置,其特征在于,所述处理单元依据所述扫描仪的扫描起始位置、旋转角度及旋转方向将所述多个子乳房影像转换成影像集合,判断各相邻的所述多个子乳房影像之间的间隔位于该影像集合中的缺失位置,且通过内插法补足该缺失位置。
9.根据权利要求6所述的病变检测装置,其特征在于,所述处理单元对未重组的该组旋转式乳房影像进行区域分割,以产生区域分割影像,判断所述区域分割影像中的至少一可疑病变区域,对所述至少一可疑病变区域进行伪阳性减少作业,以判断所述至少一病变位置,依据所述至少一病变位置寻找所述重组乳房影像中是否存在相连的病变区域,以确认所述至少一病变位置。
10.根据权利要求6所述的病变检测装置,其特征在于,所述处理单元对所述组旋转式乳房影像中的部分厚度区域的影像进行垂直投影,以产生投影影像,并依据所述投影影像中的拍摄失误类别与皮肤组织类别的比值,判断所述组旋转式乳房影像的影像质量。
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
TW105102651A TWI577342B (zh) | 2016-01-28 | 2016-01-28 | 旋轉式乳房影像之病變偵測方法及病變偵測裝置 |
TW105102651 | 2016-01-28 |
Publications (2)
Publication Number | Publication Date |
---|---|
CN107016660A true CN107016660A (zh) | 2017-08-04 |
CN107016660B CN107016660B (zh) | 2020-10-16 |
Family
ID=59241033
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201610283378.6A Active CN107016660B (zh) | 2016-01-28 | 2016-04-29 | 旋转式乳房影像的病变检测装置运作方法及病变检测装置 |
Country Status (3)
Country | Link |
---|---|
US (1) | US9886757B2 (zh) |
CN (1) | CN107016660B (zh) |
TW (1) | TWI577342B (zh) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112432902A (zh) * | 2020-12-03 | 2021-03-02 | 中国人民解放军陆军军医大学第二附属医院 | 一种外周血细胞形态学判别细胞数目的自动检测系统及方法 |
Families Citing this family (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE102016202240A1 (de) * | 2016-02-15 | 2017-08-17 | Siemens Healthcare Gmbh | Magnetresonanz-bildgebung |
US10991092B2 (en) * | 2018-08-13 | 2021-04-27 | Siemens Healthcare Gmbh | Magnetic resonance imaging quality classification based on deep machine-learning to account for less training data |
CN110400303A (zh) * | 2019-07-25 | 2019-11-01 | 杭州依图医疗技术有限公司 | 一种确定、显示乳房图像中病灶的方法及装置 |
TWI839758B (zh) * | 2022-06-20 | 2024-04-21 | 緯創資通股份有限公司 | 醫療影像的處理方法及處理醫療影像的運算裝置 |
Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1411356A (zh) * | 1999-11-18 | 2003-04-16 | 罗切斯特大学 | 用于锥形线束体积计算机x线断层摄影乳房造影法的装置和方法 |
CN101208042A (zh) * | 2005-06-28 | 2008-06-25 | 柯尼卡美能达医疗印刷器材株式会社 | 异常阴影候选检测方法、异常阴影候选检测装置 |
US20090171244A1 (en) * | 2007-12-21 | 2009-07-02 | Koning Corporation | Methods and apparatus of cone beam ct imaging and image-guided procedures |
CN101606853A (zh) * | 2008-06-18 | 2009-12-23 | 株式会社东芝 | 超声波诊断装置及超声波图像取得方法 |
TW201347737A (zh) * | 2012-05-18 | 2013-12-01 | Univ Nat Taiwan | 乳房超音波影像掃描及診斷輔助系統 |
US20140018681A1 (en) * | 2012-07-10 | 2014-01-16 | National Taiwan University | Ultrasound imaging breast tumor detection and diagnostic system and method |
US8824762B2 (en) * | 2010-10-22 | 2014-09-02 | The Johns Hopkins University | Method and system for processing ultrasound data |
US20150228092A1 (en) * | 2014-02-12 | 2015-08-13 | General Electric Company | Digital breast tomosynthesis reconstruction using adaptive voxel grid |
CN105188552A (zh) * | 2013-02-27 | 2015-12-23 | 王士平 | 乳房超声扫描装置 |
Family Cites Families (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4015836A (en) * | 1975-07-31 | 1977-04-05 | General Electric Company | Mammography table |
WO2004030523A2 (en) | 2002-10-01 | 2004-04-15 | U-Systems, Inc. | Apparatus and method for full-field breast ultrasound scanning |
US7708691B2 (en) | 2005-03-03 | 2010-05-04 | Sonowise, Inc. | Apparatus and method for real time 3D body object scanning without touching or applying pressure to the body object |
US7876938B2 (en) * | 2005-10-06 | 2011-01-25 | Siemens Medical Solutions Usa, Inc. | System and method for whole body landmark detection, segmentation and change quantification in digital images |
US20080292164A1 (en) * | 2006-08-29 | 2008-11-27 | Siemens Corporate Research, Inc. | System and method for coregistration and analysis of non-concurrent diffuse optical and magnetic resonance breast images |
US7732774B2 (en) * | 2008-09-19 | 2010-06-08 | Jefferson Science Associates, Llc | High resolution PET breast imager with improved detection efficiency |
DE102011003137A1 (de) * | 2011-01-25 | 2012-07-26 | Siemens Aktiengesellschaft | Bildgebungsverfahren mit einer verbesserten Darstellung eines Gewebebereichs |
US20130109963A1 (en) * | 2011-10-31 | 2013-05-02 | The University Of Connecticut | Method and apparatus for medical imaging using combined near-infrared optical tomography, fluorescent tomography and ultrasound |
JP5848590B2 (ja) * | 2011-12-02 | 2016-01-27 | 浜松ホトニクス株式会社 | 乳房撮像装置 |
TWM448263U (zh) * | 2012-10-12 | 2013-03-11 | Ye X Ray Mfg Corp | X光檢測設備之移位裝置 |
JP6431342B2 (ja) * | 2014-01-16 | 2018-11-28 | キヤノン株式会社 | 画像処理装置、画像処理方法およびプログラム |
JP6489801B2 (ja) * | 2014-01-16 | 2019-03-27 | キヤノン株式会社 | 画像処理装置、画像診断システム、画像処理方法およびプログラム |
TWM521447U (zh) * | 2016-01-28 | 2016-05-11 | 太豪生醫股份有限公司 | 旋轉式乳房影像之病變偵測裝置 |
-
2016
- 2016-01-28 TW TW105102651A patent/TWI577342B/zh active
- 2016-04-15 US US15/099,620 patent/US9886757B2/en active Active
- 2016-04-29 CN CN201610283378.6A patent/CN107016660B/zh active Active
Patent Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1411356A (zh) * | 1999-11-18 | 2003-04-16 | 罗切斯特大学 | 用于锥形线束体积计算机x线断层摄影乳房造影法的装置和方法 |
CN101208042A (zh) * | 2005-06-28 | 2008-06-25 | 柯尼卡美能达医疗印刷器材株式会社 | 异常阴影候选检测方法、异常阴影候选检测装置 |
US20090171244A1 (en) * | 2007-12-21 | 2009-07-02 | Koning Corporation | Methods and apparatus of cone beam ct imaging and image-guided procedures |
CN101606853A (zh) * | 2008-06-18 | 2009-12-23 | 株式会社东芝 | 超声波诊断装置及超声波图像取得方法 |
US8824762B2 (en) * | 2010-10-22 | 2014-09-02 | The Johns Hopkins University | Method and system for processing ultrasound data |
TW201347737A (zh) * | 2012-05-18 | 2013-12-01 | Univ Nat Taiwan | 乳房超音波影像掃描及診斷輔助系統 |
US20140018681A1 (en) * | 2012-07-10 | 2014-01-16 | National Taiwan University | Ultrasound imaging breast tumor detection and diagnostic system and method |
CN105188552A (zh) * | 2013-02-27 | 2015-12-23 | 王士平 | 乳房超声扫描装置 |
US20150228092A1 (en) * | 2014-02-12 | 2015-08-13 | General Electric Company | Digital breast tomosynthesis reconstruction using adaptive voxel grid |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112432902A (zh) * | 2020-12-03 | 2021-03-02 | 中国人民解放军陆军军医大学第二附属医院 | 一种外周血细胞形态学判别细胞数目的自动检测系统及方法 |
Also Published As
Publication number | Publication date |
---|---|
TW201726062A (zh) | 2017-08-01 |
CN107016660B (zh) | 2020-10-16 |
TWI577342B (zh) | 2017-04-11 |
US20170221199A1 (en) | 2017-08-03 |
US9886757B2 (en) | 2018-02-06 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN107016660A (zh) | 旋转式乳房影像的病变检测方法及病变检测装置 | |
Dey et al. | Soft computing based medical image analysis | |
US10861151B2 (en) | Methods, systems, and media for simultaneously monitoring colonoscopic video quality and detecting polyps in colonoscopy | |
Li et al. | An effective computer aided diagnosis model for pancreas cancer on PET/CT images | |
Byra et al. | Early prediction of response to neoadjuvant chemotherapy in breast cancer sonography using Siamese convolutional neural networks | |
US20190108632A1 (en) | Advanced computer-aided diagnosis of lung nodules | |
Varghese et al. | Reliability of CT‐based texture features: Phantom study | |
KR102058348B1 (ko) | 컴퓨터 단층 촬영 영상에서 딥러닝 특징과 형상 특징을 이용한 혈관근지방종과 투명세포 신세포암 분류 장치 및 방법 | |
Ding et al. | Smart electronic gastroscope system using a cloud–edge collaborative framework | |
WO2006126384A1 (ja) | 異常陰影候補の表示方法及び医用画像処理システム | |
US11276490B2 (en) | Method and apparatus for classification of lesion based on learning data applying one or more augmentation methods in lesion information augmented patch of medical image | |
Daoud et al. | A Fusion‐Based Approach for Breast Ultrasound Image Classification Using Multiple‐ROI Texture and Morphological Analyses | |
WO2008157843A1 (en) | System and method for the detection, characterization, visualization and classification of objects in image data | |
JP5048233B2 (ja) | Cadシステムにおける解剖学的形状の検出のための方法及びシステム | |
Zhao et al. | TriZ-a rotation-tolerant image feature and its application in endoscope-based disease diagnosis | |
Almakady et al. | Rotation invariant features based on three dimensional Gaussian Markov random fields for volumetric texture classification | |
TW201726064A (zh) | 醫療影像處理裝置及其乳房影像處理方法 | |
Xiao et al. | A cascade and heterogeneous neural network for CT pulmonary nodule detection and its evaluation on both phantom and patient data | |
US8121376B2 (en) | Diagnostic imaging support processing apparatus and diagnostic imaging support processing program product | |
Nguyen et al. | Digital image analysis is a useful adjunct to endoscopic ultrasonographic diagnosis of subepithelial lesions of the gastrointestinal tract | |
JP2006325640A (ja) | 異常陰影候補の表示方法及び医用画像処理システム | |
Kaçmaz et al. | Effect of interpolation on specular reflections in texture‐based automatic colonic polyp detection | |
CN116228709A (zh) | 一种胰腺实性占位病灶的交互式超声内镜图像识别方法 | |
Selvaraj et al. | Design and development of artificial intelligence‐based application programming interface for early detection and diagnosis of colorectal cancer from wireless capsule endoscopy images | |
Kil et al. | Deep learning in thyroid ultrasonography to predict tumor recurrence in thyroid cancers |
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 |