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    • 1. 发明授权
    • Computer assisted detection of lesions in volumetric medical images
    • 计算机辅助检测体积医学图像中的病变
    • US07596256B1
    • 2009-09-29
    • US10246070
    • 2002-09-16
    • Kaufman ArieDongqing ChenJerome LiangMark R. Wax
    • Kaufman ArieDongqing ChenJerome LiangMark R. Wax
    • G06K9/00
    • G06T7/0012G06T7/12G06T2207/10072G06T2207/30032
    • A computer-assisted detection method is provided for detecting suspicious locations of lesions in the volumetric medical images. The method includes steps of features extraction and fusion. The first step is computing gradient feature for extraction of the layer of Partial Volume Effect (LPVE) between different tissues that related to specific organs. The LPVE will combine with the result of voxel classification to fulfill the task of tissue classification. After tissue classification, the contour of tissue boundary is determined. The gradient feature is also used to determine the direction that intensity changes. This direction that intensity changes most dramatically serves as the normal vector for voxel on the contour of the tissue boundary. The second step is to determine a local surface patch on the contour for each voxel on the contour. A local landmark system is then created on that patch and the so-called Euclidean Distance Transform Vector (EDTV) is computed based on those landmarks. The EDTV is the basic shape feature for lesion detection whose development and invasion results abnormal shape change on the tissue boundary. A vector classification algorithm for pattern recognition based on EDTVs is also provided. The voxel on the contour of tissue boundary can be grouped into areas based on similar pattern to form lesion patch and local lesion volume. That area will further be analyzed for estimation of the likelihood of lesion.
    • 提供了一种用于检测体积医学图像中病变的可疑位置的计算机辅助检测方法。 该方法包括特征提取和融合的步骤。 第一步是在与特定器官相关的不同组织之间提取部分体积效应(LPVE)层的计算梯度特征。 LPVE将结合体素分类的结果来完成组织分类的任务。 组织分类后,确定组织边界的轮廓。 梯度特征也用于确定强度变化的方向。 强度变化最大的方向是组织边界轮廓上的体素的法向量。 第二步是为轮廓上的每个体素确定轮廓上的局部表面贴片。 然后在该补丁上创建一个局部地标系统,并根据这些地标计算所谓的欧几里德距离变换向量(EDTV)。 EDTV是病变检测的基本形状特征,其发育和侵袭在组织边界上形成异常形状变化。 还提供了基于EDTV的模式识别的矢量分类算法。 组织边界轮廓上的体素可以基于相似的模式分组成区域,形成病变斑块和局部病变体积。 该区域将进一步分析以估计病变的可能性。
    • 3. 发明申请
    • Sampling medical images for virtual histology
    • 抽取医学图像进行虚拟组织学
    • US20090226065A1
    • 2009-09-10
    • US11664833
    • 2005-10-07
    • Dongqing Chen
    • Dongqing Chen
    • G06K9/00A61B5/05G06T17/00G06F17/00
    • G06F19/325G06F16/5862G06F19/321G06T7/0012G06T7/11G06T2207/10072G06T2207/20101G06T2207/30032
    • A system (300, 400, 800) and method (100, 200) are provided for building a digital sample library of lesions or cancers from medical images, the system (300) including an image scanner (310), image visualization or reviewing equipment (320) in signal communication with the image scanner, a digital sample library database (332), and a network for data communication connected between the library, the reviewing equipment, and the at least one scanner; and the method (100) including acquiring patient medical images (112), detecting target lesions in the acquired patient medical images (114, 116, 118), extracting digital samples (120) of the detected target lesions, collecting pathological and histological results (124, 126) of the detected target lesions, collecting diagnostic results of the detected target lesions (128), performing model selection and feature extraction (122) for each digital sample of a lesion, and storing (130) each extracted digital sample for library evolution.
    • 提供一种系统(300,400,800)和方法(100,200),用于从医学图像建立病变或癌症的数字样本库,所述系统(300)包括图像扫描仪(310),图像可视化或复查设备 (320),与所述图像扫描器进行信号通信,数字样本库数据库(332)和连接在所述库,所述检查设备和所述至少一个扫描仪之间的用于数据通信的网络; 并且所述方法(100)包括获取患者医学图像(112),检测获取的患者医学图像(114,116,118)中的目标病变,提取检测到的目标病变的数字样本(120),收集病理学和组织学结果 收集检测到的目标病变的诊断结果(128),为病变的每个数字样本执行模型选择和特征提取(122),并存储(130)每个提取的数字样本库 演化。
    • 7. 发明申请
    • Systems and Methods for Automated Segmentation, Visualization and Analysis of Medical Images
    • 医学图像自动分割,可视化和分析的系统和方法
    • US20070276214A1
    • 2007-11-29
    • US10580763
    • 2004-11-26
    • Frank DachilleDongqing ChenMichael MeissnerWenli Cai
    • Frank DachilleDongqing ChenMichael MeissnerWenli Cai
    • A61B5/00
    • G06T7/0012G06F19/321G06T19/00G06T2207/30004G06T2210/41G06T2219/008
    • An imaging system for automated segmentation and visualization of medical images (100) includes an image processing module (107) for automatically processing image data using a set of directives (109) to identify a target object in the image data and process the image data according to a specified protocol, a rendering module (105) for automatically generating one or more images of the target object based on one or more of the directives (109) and a digital archive (110) for storing the one or more generated images. The image data may be DICOM-formatted image data (103), wherein the imaging processing module (107) extracts and processes meta-data in DICOM fields of the image data to identify the target object. The image processing module (107) directs a segmentation module (108) to segment the target object using processing parameters specified by one or more of the directives (109).
    • 一种用于医疗图像(100)的自动分割和可视化的成像系统包括图像处理模块(107),用于使用一组指令(109)自动处理图像数据,以识别图像数据中的目标对象,并根据 根据指定协议,基于所述指令(109)中的一个或多个自动生成所述目标对象的一个​​或多个图像的再现模块(105)和用于存储所述一个或多个所生成的图像的数字存档(110)。 图像数据可以是DICOM格式的图像数据(103),其中成像处理模块(107)提取并处理图像数据的DICOM字段中的元数据以识别目标对象。 图像处理模块(107)引导分割模块(108)使用由一个或多个指令(109)指定的处理参数来分割目标对象。