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    • 21. 发明授权
    • Angiography method and apparatus
    • 血管造影方法和装置
    • US06842638B1
    • 2005-01-11
    • US10010773
    • 2001-11-13
    • Jasjit S. SuriKecheng LiuDee H. Wu
    • Jasjit S. SuriKecheng LiuDee H. Wu
    • A61B6/00G06T5/00G06T7/00G06T7/60A61B5/05
    • G06T7/0012A61B6/481A61B6/504G06T7/12G06T7/149G06T7/64G06T2207/10088G06T2207/20064G06T2207/20161G06T2207/30101G06T2207/30172
    • A two-dimensional slice formed of pixels (376) is extracted from the angiographic image (76) after enhancing the vessel edges by second order spatial differentiation (78). Imaged vascular structures in the slice are located (388) and flood-filled (384). The edges of the filled regions are iteratively eroded to identify vessel centers (402). The extracting, locating, flood-filling, and eroding is repeated (408) for a plurality of slices to generate a plurality of vessel centers (84) that are representative of the vascular system. A vessel center (88) is selected, and a corresponding vessel direction (92) and orthogonal plane (94) are found. The vessel boundaries (710) in the orthogonal plane (94) are identified by iteratively propagating (704) a closed geometric contour arranged about the vessel center (88). The selecting, finding, and estimating are repeated for the plurality of vessel centers (84). The estimated vessel boundaries (710) are interpolated (770) to form a vascular tree (780).
    • 在通过二阶空间微分增强容器边缘之后,从血管造影图像(76)中提取由像素(376)形成的二维切片(78)。 切片中的成像血管结构位于(388)和充满灌注(384)。 填充区域的边缘被迭代地腐蚀以识别血管中心(402)。 对于多个切片重复提取,定位,灌注和侵蚀(408),以产生代表血管系统的多个血管中心(84)。 选择容器中心(88),并找到相应的容器方向(92)和正交平面(94)。 在正交平面(94)中的血管边界(710)通过迭代地传播(704)围绕血管中心(88)布置的封闭几何轮廓来识别。 对于多个血管中心(84)重复选择,发现和估计。 估计的血管边界(710)被内插(770)以形成血管树(780)。
    • 22. 发明授权
    • Method and apparatus for medical image display for surgical tool planning and navigation in clinical environments
    • 用于临床环境中外科手术工具计划和导航的医学图像显示的方法和装置
    • US06614453B1
    • 2003-09-02
    • US09565253
    • 2000-05-05
    • Jasjit S. SuriRuhul QuddusYansun Xu
    • Jasjit S. SuriRuhul QuddusYansun Xu
    • G06F300
    • G06F19/321A61B34/20A61B34/25G06F19/00G16H50/50Y10S715/961
    • A medical imaging display system includes a memory (40) for storing first image data representative of a region of interest. The memory (40) stores image data generated by medical imaging devices such as magnetic resonance devices (20), computed tomography devices (22), nuclear imaging devices (26,28,30), and ultrasound devices. Typically, image data from these devices is obtained some time prior to a surgical event and users may access this data in planning for the surgical event. A processor (42), in data communication with the memory, is organized under a component object modeling architecture. The processor (42) is connected to a user interface (10) for providing user requests to the processor. Thus, in response to user action via the user interface (10), the processor (42) determines an object (54) adapted to act on the request, selects a handle (60) for the determined object and, employs the object via the handle to act on the request. Additionally, the image guided surgical system also includes a source (48) of substantially real time image data generated in the surgical theater, such as spectroscopy devices, which can also be manipulated via software objects to display desired portions of the region of interest. Any of these images can be viewed on a display (46) in a planning environment, in the surgical suite, or even by a consultant, geographically remote from the surgical site.
    • 医疗成像显示系统包括用于存储表示感兴趣区域的第一图像数据的存储器(40)。 存储器(40)存储由诸如磁共振装置(20),计算机断层摄影装置(22),核成像装置(26,28,30)和超声装置之类的医疗成像装置产生的图像数据。 通常,来自这些设备的图像数据在手术事件之前的一段时间获得,并且用户可以在规划外科事件时访问该数据。 与存储器进行数据通信的处理器(42)被组织在组件对象建模架构下。 处理器(42)连接到用户界面(10),用于向处理器提供用户请求。 因此,响应于通过用户界面(10)的用户动作,处理器(42)确定适于作用于该请求的对象(54),为所确定的对象选择句柄(60),并且通过该对象 根据要求处理。 此外,图像引导手术系统还包括在手术室中生成的基本上实时的图像数据的源(48),例如光谱设备,其还可以经由软件对象来操纵以显示感兴趣区域的期望部分。 这些图像中的任何一个都可以在规划环境,外科手术套件中的显示器(46)上,或者甚至可以在地理上远离手术部位的顾问进行观看。
    • 24. 发明授权
    • Mobile architecture using cloud for data mining application
    • 移动架构使用云进行数据挖掘应用
    • US08639008B2
    • 2014-01-28
    • US13449518
    • 2012-04-18
    • Jasjit S. Suri
    • Jasjit S. Suri
    • G06K9/00
    • A61B5/0013A61B5/0022A61B5/02007A61B5/055A61B6/5217A61B8/5223G06F19/00G06F19/321G16H10/60G16H50/20G16H50/30G16H50/70
    • Three tier architecture for image-based diagnosis and monitoring application using Cloud is described. The presentation layer is run on the tablet (mobile device), while the business and persistence layer runs on a single cloud or distributed on different Clouds in a multi-tenancy and multi-user application. Such architecture is used for automated data mining application for computing (a) cardiovascular risk, stroke risk using IMT measurement, plaque characterization, (b) computing diagnostic index for benign vs. malignant tissue for ovarian cancer classification (c) benign vs. malignant tissue characterization for prostate cancer and (d) classification of fatty liver disease vs. normal cases. The Architecture is for data mining application.
    • 描述了使用云的基于图像的诊断和监控应用的三层架构。 表现层在平板电脑(移动设备)上运行,而业务和持久层在多租户和多用户应用程序中运行在单个云上或分布在不同的云上。 这种架构用于计算自动化数据挖掘应用(a)使用IMT测量,斑块表征的心血管风险,卒中风险,(b)用于卵巢癌分类的良性与恶性组织的计算诊断指标(c)良性与恶性组织 表征前列腺癌和(d)脂肪肝疾病与正常情况的分类。 架构用于数据挖掘应用。
    • 26. 发明申请
    • Non-Invasive Imaging-Based Prostate Cancer Prediction
    • 基于非侵入性成像的前列腺癌预测
    • US20120163693A1
    • 2012-06-28
    • US13412118
    • 2012-03-05
    • Jasjit S. Suri
    • Jasjit S. Suri
    • G06K9/00
    • G06T7/0012G06T7/41G06T2207/10072G06T2207/10132G06T2207/20081G06T2207/30081
    • A system (UroImage™) is an imaging based system for predicting if the prostate is cancerous or not using non-invasive ultrasound. The method is an on-line system where region of interest processor computes the capsule region in the Urological image. The feature extraction processor finds the significant features such as non-linear higher order spectra and high pass filter discrete wavelet based features, and combines them. The on-line classifier processor uses along with the training-based parameters to estimate and predicate if the patient's prostate is cancerous or not. The UroImage™ also introduces the applicability of this system for MR, CT or fusion of these modalities with ultrasound for predicting cancer.
    • 系统(UroImage™)是一种基于成像的系统,用于使用非侵入性超声预测前列腺癌是否癌变。 该方法是在线系统,其中感兴趣区域处理器计算泌尿图像中的胶囊区域。 特征提取处理器发现诸如非线性高阶谱和基于离散小波的高通滤波特征的重要特征,并将其组合。 在线分类器处理器与基于训练的参数一起使用,以估计和判断患者的前列腺是否癌变。 UroImage™还介绍了该系统对MR,CT或这些模式与超声波预测癌症融合的适用性。
    • 27. 发明申请
    • Imaging Based Symptomatic Classification Using a Combination of Trace Transform, Fuzzy Technique and Multitude of Features
    • 使用跟踪变换,模糊技术和多种特征组合的基于成像的症状分类
    • US20120078099A1
    • 2012-03-29
    • US13253952
    • 2011-10-05
    • Jasjit S. Suri
    • Jasjit S. Suri
    • A61B8/00
    • A61B8/483A61B8/0891A61B8/12G01R33/5608
    • A statistical (a) Computer Aided Diagnostic (CAD) technique is described for symptomatic versus asymptomatic plaque automated classification of carotid ultrasound images and (b) presents a cardiovascular risk score computation. We demonstrate this for longitudinal Ultrasound, CT, MR modalities and extendable to 3D carotid Ultrasound. The on-line system consists of Atherosclerotic Wall Region estimation using AtheroEdge™ for longitudinal Ultrasound or Athero-CTView™ for CT or Athero-MRView from MR. This greyscale Wall Region is then fed to a feature extraction processor which uses the combination: (a) Higher Order Spectra; (b) Discrete Wavelet Transform (DWT); (c) Texture and (d) Wall Variability. Another combination uses: (a) Local Binary Pattern; (b) Law's Mask Energy and (c) Wall Variability. Another combination uses: (a) Trace Transform; (b) Fuzzy Grayscale Level Co-occurrence Matrix and (c) Wall Variability. The output of the Feature Processor is fed to the Classifier which is trained off-line.
    • 描述了用于颈动脉超声图像的症状与无症状斑块自动分类的统计学(a)计算机辅助诊断(CAD)技术,以及(b)呈现心血管风险评分计算。 我们展示了纵向超声,CT,MR方式,并可扩展到三维颈动脉超声。 在线系统由动脉粥样硬化壁区域估计,使用AtheroEdge™进行纵向超声波或用于CT的Athero-CTView™或来自MR的Athero-MRView。 然后将这个灰度级的墙区域馈送到使用以下组合的特征提取处理器:(a)高阶光谱; (b)离散小波变换(DWT); (c)纹理和(d)壁变化。 另一种组合使用:(a)本地二进制模式; (b)法律面具能量和(c)墙面变异性。 另一种组合使用:(a)轨迹变换; (b)模糊灰度等级共生矩阵和(c)墙壁变异性。 特征处理器的输出被馈送到离线训练的分类器。
    • 29. 发明申请
    • Method for breast screening in fused mammography
    • 融合乳房X线照相术中乳房筛查的方法
    • US20100111379A1
    • 2010-05-06
    • US11632078
    • 2005-07-07
    • Jasjit S. SuriYajie Sun
    • Jasjit S. SuriYajie Sun
    • G06K9/00
    • A61B6/5247A61B6/032A61B6/4417A61B6/502A61B6/52A61B6/5217A61B8/0825A61B8/4416A61B8/5238G06T7/0012G06T7/0081G06T7/0091G06T7/11G06T7/12G06T7/155G06T7/168G06T11/003G06T2207/10112G06T2207/10116G06T2207/10136G06T2207/20016G06T2207/20076G06T2207/20168G06T2207/30068G06T2207/30096
    • A method for use in medical imaging of a patient including, with the patient immobilized with respect to an imaging reference frame, acquiring first digital imaging information including a first region of interest using a first imaging modality; processing the first digital imaging information to identify a feature for analysis; and using a second imaging modality to acquire targeted second imaging information for a second region of interest, the second region of interest corresponding to a subset of the first region of interest, wherein the second region of interest includes the feature for analysis. An apparatus for use in medical imaging comprising structure for immobilizing a patient with respect to an imaging reference frame; a first imaging system for acquiring first digital imaging information including a first region of interest using a first imaging modality; a processor processing the first digital imaging information using a diagnostic tool to identify a feature of interest; and a second imaging system for acquiring second imaging information using a second imaging modality, the second imaging information corresponding to a second region of interest including the feature for analysis.
    • 一种用于患者的医学成像的方法,包括:相对于成像参考系被固定的患者,使用第一成像模态获取包括第一感兴趣区域的第一数字成像信息; 处理第一数字成像信息以识别用于分析的特征; 以及使用第二成像模式来获取第二感兴趣区域的目标第二成像信息,所述第二感兴趣区域对应于所述第一感兴趣区域的子集,其中所述第二感兴趣区域包括用于分析的特征。 一种用于医学成像的装置,包括用于使患者相对于成像参考框架固定的结构; 第一成像系统,用于使用第一成像模态获取包括第一感兴趣区域的第一数字成像信息; 处理器使用诊断工具处理所述第一数字成像信息,以识别感兴趣的特征; 以及第二成像系统,用于使用第二成像模式获取第二成像信息,所述第二成像信息对应于包括用于分析的特征的第二感兴趣区域。