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公开(公告)号：KR101028365B1
公开(公告)日：2011-04-11
申请号：KR1020090009063
申请日：2009-02-04
Applicant: 서울여자대학교 산학협력단 , 서울대학교산학협력단
IPC: A61B6/02
Abstract: 본 발명은 연속적으로 촬영된 컴퓨터 단층촬영(Computed Tomography, CT) 영상의 각 슬라이스를 정합함으로써 연속 CT 영상 내의 폐결절 (Pulmonary Nodule)을 추적 관찰하는 방법 및 그 장치를 제공할 수 있다. 본 발명은 폐를 포함하는 최적경계볼륨의 중심 좌표를 기반으로 기준 영상에 대한 추적 영상의 위치를 보정하는 단계, 기준 영상 및 추적 영상 간의 관상(coronal) 최대강도 투사영상 간의 비교 결과 및 시상(sagittal) 최대강도 투사영상 간의 비교 결과에 기반하여 상기 기준 영상 및 상기 추적 영상을 강체 정합하는 단계, 및 상기 기준 영상 및 상기 추적 영상 내의 폐결절의 형태 정보 및 주변 지역 정보에 기반하여 상기 기준 영상 및 상기 추적 영상을 템플릿 정합하는 단계를 포함할 수 있다.
컴퓨터 단층촬영 영상, 폐결절, 관상 최대강도 투사, 시상 최대강도 투사, 강체 정합, 템플릿 매칭-
公开(公告)号：KR1020100089704A
公开(公告)日：2010-08-12
申请号：KR1020090009063
申请日：2009-02-04
Applicant: 서울여자대학교 산학협력단 , 서울대학교산학협력단
IPC: A61B6/02
CPC classification number: A61B6/032 , A61B6/025 , A61B6/50 , A61B6/52 , G06T2207/10081
Abstract: PURPOSE: A pulmonary nodule multistage matching method of a serial computer tomography scan and a method thereof are provided to reduce time for matching computed tomography images by using a coronal maximum intensity projection image and a sagittal maximum intensity projection image. CONSTITUTION: A tracing image location according to a reference image is compensated based on the center coordinate of an optimum boundary volume(S110). A coronal maximum intensity projection image of the reference image and a coronal maximum intensity projection image of the tracing image are compared. A sagittal maximum intensity projection image of the reference image and a sagittal maximum intensity projection image of the tracing image are compared(S120). The reference image and the tracing image are matched(S130). The reference image and the tracing image are matched to a template(S140).
Abstract translation: 目的：提供一种串联计算机断层扫描的肺结节多级匹配方法及其方法，以通过使用冠状面最大强度投影图像和矢状面最大强度投影图像来减少用于匹配计算机断层摄影图像的时间。 构成：根据参考图像的跟踪图像位置基于最佳边界体积的中心坐标进行补偿（S110）。 比较参考图像的冠状最大强度投影图像和跟踪图像的冠状面最大强度投影图像。 比较参考图像的矢状最大强度投影图像和跟踪图像的矢状面最大强度投影图像（S120）。 参考图像和跟踪图像匹配（S130）。 参考图像和跟踪图像与模板匹配（S140）。
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Patent Agency Ranking
公开(公告)号：KR1020090111160A
公开(公告)日：2009-10-26
申请号：KR1020080036783
申请日：2008-04-21
Applicant: 서울여자대학교 산학협력단
Abstract: PURPOSE: A method and an apparatus of diagnosing prostate cancer using a histopathology image and a magnetic resonance image are provided to improve recognition of a tumor area of a T2-weighted magnetic resonance image by dense correction. CONSTITUTION: A histopathology image correcting unit(11) generates and corrects the entire histopathology image. The histopathology image correcting unit includes a section engaging unit(112) and a resolution lowering unit(114). A magnetic resonance image correcting unit(13) corrects a T2-weighted magnetic resonance image. The magnetic resonance image correcting unit includes a bleeding area extracting unit(132) and a bleeding area substitution unit(134). A matching processing unit(15) includes the first matching unit(152), the second matching unit(154) and the third matching unit(156).
Abstract translation: 目的：提供使用组织病理学图像和磁共振图像诊断前列腺癌的方法和装置，以通过密集校正来改善T2加权磁共振图像的肿瘤区域的识别。 构成：组织病理学图像校正单元（11）产生和校正整个组织病理学图像。 组织病理学图像校正单元包括部分接合单元（112）和分辨率降低单元（114）。 磁共振图像校正单元（13）校正T2加权磁共振图像。 磁共振图像校正单元包括出血区域提取单元（132）和出血区域替换单元（134）。 匹配处理单元（15）包括第一匹配单元（152），第二匹配单元（154）和第三匹配单元（156）。
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4.发明授权동적조영증강 유방 ＭＲ 영상에서 조직별 밝기값 보정 및 종양 강성 제약을 적용한 비강체 정합 방법 및 시스템 有权非激素注射方法和系统，每个组织的密度校正和动态对比增强乳腺图像中肿瘤的刚度约束
公开(公告)号：KR101258814B1
公开(公告)日：2013-04-26
申请号：KR1020110116563
申请日：2011-11-09
Applicant: 서울여자대학교 산학협력단
Abstract: PURPOSE: A nonrigid registration method and system applying the tumor rigidity constraint and the density correction of each tissue in a dynamic contract enhanced breast MR image are provided to accurately classify a tissue by regularizing a contrast density. CONSTITUTION: A breast region is automatically divided by removing a breast skin and a pectoral(S110). A fuzzy C-means grouping method is used in the divided breast region. A breast tissue is classified into fat, the mammary gland, a tumor, and a blood vessel(S120). The tumor is divided by using a density and a morphological operation(S130). The density is corrected in the fat and the mammary gland(S140). [Reference numerals] (AA) Start; (BB) End; (S110) Breast division based on slope and direction information; (S120) Tissue classification using a fuzzy C-means grouping method; (S130) Tumor division based on brightness value and shape information; (S140) Rigid body matching based on normalized mutual information and brightness value correction; (S150) Non-rigid body matching applying tumor rigid constraints;
Abstract translation: 目的：提供一种应用肿瘤刚度约束和动态收缩增强乳房MR图像中每个组织的密度校正的非刚性配准方法和系统，以通过正则化对比度密度来精确地分类组织。 构成：通过去除乳房皮肤和胸部，自动分割乳房区域（S110）。 在分割的乳房区域中使用模糊C均值分组方法。 乳腺组织分为脂肪，乳腺，肿瘤和血管（S120）。 通过使用密度和形态学手术分割肿瘤（S130）。 在脂肪和乳腺中校正密度（S140）。 （附图标记）（AA）开始; （BB）结束; （S110）基于斜率和方向信息的乳房分割; （S120）使用模糊C均值分组法进行组织分类; （S130）基于亮度值和形状信息的肿瘤分割; （S140）基于归一化互信息和亮度值校正的刚体匹配; （S150）应用肿瘤刚性约束的非刚体匹配;
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公开(公告)号：KR100933569B1
公开(公告)日：2009-12-29
申请号：KR1020080036783
申请日：2008-04-21
Applicant: 서울여자대학교 산학협력단
Abstract: PURPOSE: A method and an apparatus of diagnosing prostate cancer using a histopathology image and a magnetic resonance image are provided to improve recognition of a tumor area of a T2-weighted magnetic resonance image by dense correction. CONSTITUTION: A histopathology image correcting unit(11) generates and corrects the entire histopathology image. The histopathology image correcting unit includes a section engaging unit(112) and a resolution lowering unit(114). A magnetic resonance image correcting unit(13) corrects a T2-weighted magnetic resonance image. The magnetic resonance image correcting unit includes a bleeding area extracting unit(132) and a bleeding area substitution unit(134). A matching processing unit(15) includes the first matching unit(152), the second matching unit(154) and the third matching unit(156).
Abstract translation: 目的：提供一种使用组织病理学图像和磁共振图像诊断前列腺癌的方法和装置，以通过密集校正来改善对T2加权磁共振图像的肿瘤区域的识别。 构成：组织病理学图像校正单元（11）生成并校正整个组织病理学图像。 组织病理学图像校正单元包括区段接合单元（112）和分辨率降低单元（114）。 磁共振图像校正单元（13）校正T2加权磁共振图像。 磁共振图像校正单元包括出血区域提取单元（132）和出血区域置换单元（134）。 匹配处理单元（15）包括第一匹配单元（152），第二匹配单元（154）和第三匹配单元（156）。
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公开(公告)号：KR101460908B1
公开(公告)日：2014-11-17
申请号：KR1020130094757
申请日：2013-08-09
Applicant: 서울여자대학교 산학협력단
CPC classification number: G06T7/37 , G06T2207/10076 , G06T2207/30096
Abstract: 본 발명은 방사선치료계획을 위한 4D CT 영상에서 호흡이 다른 CT 영상 간 비강체 정합을 통한 자동 폐종양 위치 추적 방법에 관한 것으로, 본 발명에 따른 4차원 컴퓨터 단층촬영 영상의 폐종양 위치 추적 시스템은 4D CT의 각 위상영상에서 폐 분할 후, 최대흡기 및 최대호기 위상영상 간 폐 경계 정보를 이용하여, 어파인 정합을 수행하고, 호흡량에 따라 어파인 변환벡터를 산출하는 변환벡터 예측부 및 상기 변환벡터 예측부에서 산출된 어파인 변환벡터와 위상영상 간 비강체 정합으로 산출된 변형벡터로 폐종양의 위치를 자동 추적하는 폐종양 위치 추적부를 포함하는 것을 기술적 특징으로 한다.
Abstract translation: 本发明涉及通过用于放射治疗计划的4D CT图像中具有不同呼吸的4D计算机断层摄影（CT）图像之间的非刚性配准来自动跟踪肺肿瘤的位置的方法。 根据本发明，用于跟踪来自4d ct图像的肺肿瘤位置的系统包括：转换矢量预测单元，其从每个4D CT相位图像分割肺，使用肝脏和肺之间的最大值的边界信息进行仿射登记 吸气相图像和最大呼气相图像，并根据呼吸量计算仿射变换矢量; 以及肺肿瘤位置跟踪单元，其利用由转换矢量预测单元计算的仿射变换向量和通过相位图像之间的非刚性配准计算的变形矢量自动跟踪肺肿瘤的位置。
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公开(公告)号：KR101251822B1
公开(公告)日：2013-04-09
申请号：KR1020110120578
申请日：2011-11-18
Applicant: 서울여자대학교 산학협력단
CPC classification number: A61B6/507
Abstract: PURPOSE: A system for analyzing perfusion in a dynamic enhanced focus lung computerized tomography image and a method thereof are provided to accurately and efficiently separate solitary pulmonary nodule by applying proper curvature and shape information based algorithm according to a type of solitary pulmonary nodule. CONSTITUTION: A solitary pulmonary nodule candidate area is extracted from a type of solitary pulmonary nodule determined from a lung computerized tomography image(210). The solitary pulmonary nodule is divided by separating surrounding tissues from the solitary pulmonary nodule candidate area(212). An image before dynamic enhanced focus and an image after dynamic enhanced focus are initially matched with each other by using a central point of the solitary pulmonary nodule(214). The initial matching is corrected through rigid matching based on mutual information(216). The solitary pulmonary nodule divided from the image before dynamic enhanced focus is regionalized(218). Perfusion analysis is performed by using an average brightness value for each region(220). [Reference numerals] (210) Extracting a solitary pulmonary nodule candidate area; (212) Dividing a solitary pulmonary nodule by separating surrounding tissues; (214) Initially matching enhanced images before and after dynamic enhanced focus with each other; (216) Correcting the initial matching; (218) Regionalizing the divided solitary pulmonary nodule; (220) Performing perfusion analysis using an average brightness value for each region; (AA,BB) Start;
Abstract translation: 目的：提供一种用于分析动态增强型聚焦肺计算机断层摄影图像中的灌注的系统及其方法，以通过根据孤立性肺结节的类型应用适当的曲率和形状信息的算法来精确和有效地分离孤立性肺结节。 构成：从肺计算机断层摄影图像确定的一种孤立性肺结节中提取孤立性肺结核候选区域（210）。 孤立性肺结节通过从孤立性肺结节候选区域分离周围组织而被分割（212）。 动态增强焦点之前的图像和动态增强焦点之后的图像最初通过使用孤立肺结节（214）的中心点相互匹配。 初始匹配通过基于互信息的刚性匹配进行修正（216）。 动态增强焦点之前分离的孤立性肺结节区分（218）。 通过使用每个区域的平均亮度值来执行灌注分析（220）。 （210）提取孤立性肺结核候选区域; （212）通过分离周围组织分割孤立性肺结节; （214）最初将动态增强焦点之前和之后的增强图像彼此匹配; （216）纠正初始匹配; （218）区分分离的孤立性肺结节; （220）使用每个区域的平均亮度值进行灌注分析; （AA，BB）开始;
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