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    • 1. 发明授权
    • Determining a four-dimensional CT image based on three-dimensional CT data and four-dimensional model data
    • 基于三维CT数据和四维模型数据确定四维CT图像
    • US09367926B2
    • 2016-06-14
    • US14437789
    • 2012-10-26
    • Brainlab AG
    • Stefan VilsmeierAndreas BlumhoferStefan Seifert
    • G06K9/00G06T7/20G06T13/20
    • G06T7/2046G06T7/251G06T13/20G06T2207/10076G06T2207/30048G06T2207/30061
    • The invention relates to a data processing method of determining a change of an image of an anatomical body part of a patient's body, the method being executed by a computer and comprising the following steps: a) acquiring static medical image data comprising static medical image information describing anatomical body part in a first anatomical spatial state of an anatomical vital spatial change of the anatomical body part; b) acquiring patient model data comprising patient model information describing a model body part corresponding to the anatomical body part, wherein the patient model information describes the model body part in a plurality of model spatial states of a model vital spatial change corresponding to the anatomical vital spatial change; c) determining spatial state mapping data comprising spatial state mapping information describing at least one of a first mapping from the model body part in a first one of the plurality of model spatial states to the model body part in a second, different one of the plurality of model spatial states, the first model spatial state corresponding to the first anatomical spatial state, and a second mapping from the model body part in the first model spatial state to the anatomical body part in the first anatomical spatial state; d) determining, based on the static medical image data and the spatial state mapping data, transformed medical image data comprising transformed medical image information describing the anatomical body part in a second anatomical spatial state of the anatomical vital spatial change, the second anatomical spatial state corresponding to the second model spatial state.
    • 本发明涉及一种确定患者身体的解剖体部分的图像变化的数据处理方法,该方法由计算机执行,并且包括以下步骤:a)获取包括静态医学图像信息的静态医学图像数据 在解剖体部分的解剖学重要空间变化的第一解剖空间状态中描述解剖体部分; b)获取患者模型数据,所述患者模型数据包括描述对应于所述解剖体部分的模型身体部位的患者模型信息,其中所述患者模型信息描述模型重要空间变化的多个模型空间状态中的模型身体部位, 空间变化 c)确定空间状态映射数据,所述空间状态映射数据包括描述从所述多个模型空间状态中的第一模型空间状态中的模型主体部分到所述多个模型空间状态中的第二不同模型主体部分中的模型主体部分的至少一个映射信息 模型空间状态,对应于第一解剖空间状态的第一模型空间状态,以及从第一模型空间状态中的模型身体部分到第一解剖空间状态中的解剖体部分的第二映射; d)基于静态医学图像数据和空间状态映射数据确定包括描述解剖学主体部分的解剖学空间变化的第二解剖空间状态的变换的医学图像信息的转换的医学图像数据,第二解剖空间状态 对应于第二模型空间状态。
    • 2. 发明申请
    • Determining a Four-Dimensional CT Image Based on Three-Dimensional CT Data and Four-Dimensional Model Data
    • 基于三维CT数据和四维模型数据确定四维CT图像
    • US20150302608A1
    • 2015-10-22
    • US14437789
    • 2012-10-26
    • Brainlab AG
    • Stefan VilsmeierAndreas BlumhoferStefan Seifert
    • G06T7/20G06T13/20
    • G06T7/2046G06T7/251G06T13/20G06T2207/10076G06T2207/30048G06T2207/30061
    • The invention relates to a data processing method of determining a change of an image of an anatomical body part of a patient's body, the method being executed by a computer and comprising the following steps: a) acquiring static medical image data comprising static medical image information describing anatomical body part in a first anatomical spatial state of an anatomical vital spatial change of the anatomical body part; b) acquiring patient model data comprising patient model information describing a model body part corresponding to the anatomical body part, wherein the patient model information describes the model body part in a plurality of model spatial states of a model vital spatial change corresponding to the anatomical vital spatial change; c) determining spatial state mapping data comprising spatial state mapping information describing at least one of a first mapping from the model body part in a first one of the plurality of model spatial states to the model body part in a second, different one of the plurality of model spatial states, the first model spatial state corresponding to the first anatomical spatial state, and a second mapping from the model body part in the first model spatial state to the anatomical body part in the first anatomical spatial state; d) determining, based on the static medical image data and the spatial state mapping data, transformed medical image data comprising transformed medical image information describing the anatomical body part in a second anatomical spatial state of the anatomical vital spatial change, the second anatomical spatial state corresponding to the second model spatial state.
    • 本发明涉及一种确定患者身体的解剖体部分的图像变化的数据处理方法,该方法由计算机执行,并且包括以下步骤:a)获取包括静态医学图像信息的静态医学图像数据 在解剖体部分的解剖学重要空间变化的第一解剖空间状态中描述解剖体部分; b)获取患者模型数据,所述患者模型数据包括描述对应于所述解剖体部分的模型身体部位的患者模型信息,其中所述患者模型信息描述模型重要空间变化的多个模型空间状态中的模型身体部位, 空间变化 c)确定空间状态映射数据,所述空间状态映射数据包括描述从所述多个模型空间状态中的第一模型空间状态中的模型主体部分到所述多个模型空间状态中的第二不同模型主体部分中的模型主体部分的至少一个映射信息 模型空间状态,对应于第一解剖空间状态的第一模型空间状态,以及从第一模型空间状态中的模型身体部分到第一解剖空间状态中的解剖体部分的第二映射; d)基于静态医学图像数据和空间状态映射数据确定包括描述解剖学主体部分的解剖学空间变化的第二解剖空间状态的变换的医学图像信息的转换的医学图像数据,第二解剖空间状态 对应于第二模型空间状态。
    • 8. 发明授权
    • Systems and methods for tracking objects
    • 跟踪对象的系统和方法
    • US08971575B2
    • 2015-03-03
    • US13684451
    • 2012-11-23
    • Cyberlink Corp.
    • Ming-Hsiu ChangChih-Chao Ma
    • G06K9/00G06T7/20
    • G06T7/2046G06T7/251G06T7/277G06T2207/10016G06T2207/20076
    • Various embodiments are disclosed for performing object tracking. One embodiment is a system for tracking an object in a plurality of frames, comprising a probability map generator configured to generate a probability map by estimating probability values of pixels in the frame, wherein the probability of each pixel corresponds to a likelihood of the pixel being located within the object. The system further comprises a contour model generator configured to identify a contour model of the object based on a temporal prediction method, a contour weighting map generator configured to derive a contour weighting map based on thickness characteristics of the contour model, a tracking refinement module configured to refine the probability map according to weight values specified in the contour weighting map, and an object tracker configured to track a location of the object within the plurality of frames based on the refined probability map.
    • 公开了用于执行对象跟踪的各种实施例。 一个实施例是用于跟踪多个帧中的对象的系统,包括概率图生成器,其被配置为通过估计帧中的像素的概率值来生成概率图,其中每个像素的概率对应于像素的可能性 位于对象内。 该系统还包括轮廓模型发生器,其被配置为基于时间预测方法来识别对象的轮廓模型;轮廓加权映射发生器,被配置为基于轮廓模型的厚度特征导出轮廓加权图;跟踪细化模块, 根据轮廓加权图中指定的权重值来细化概率图,以及对象跟踪器,被配置为基于精确的概率图来跟踪多个帧内的对象的位置。
    • 9. 发明授权
    • Apparatus and method for tracking facial motion through a sequence of
images
    • 用于通过一系列图像跟踪面部运动的装置和方法
    • US5802220A
    • 1998-09-01
    • US574176
    • 1995-12-15
    • Michael J. BlackYaser Yacoob
    • Michael J. BlackYaser Yacoob
    • G06K9/00G06T7/20G06F9/36
    • G06K9/00248G06K9/00315G06T7/2006G06T7/2046G06T2207/10016G06T2207/30201
    • A system tracks human head and facial features over time by analyzing a sequence of images. The system provides descriptions of motion of both head and facial features between two image frames. These descriptions of motion are further analyzed by the system to recognize facial movement and expression. The system analyzes motion between two images using parameterized models of image motion. Initially, a first image in a sequence of images is segmented into a face region and a plurality of facial feature regions. A planar model is used to recover motion parameters that estimate motion between the segmented face region in the first image and a second image in the sequence of images. The second image is warped or shifted back towards the first image using the estimated motion parameters of the planar model, in order to model the facial features relative to the first image. An affine model and an affine model with curvature are used to recover motion parameters that estimate the image motion between the segmented facial feature regions and the warped second image. The recovered motion parameters of the facial feature regions represent the relative motions of the facial features between the first image and the warped image. The face region in the second image is tracked using the recovered motion parameters of the face region. The facial feature regions in the second image are tracked using both the recovered motion parameters for the face region and the motion parameters for the facial feature regions. The parameters describing the motion of the face and facial features are filtered to derive mid-level predicates that define facial gestures occurring between the two images. These mid-level predicates are evaluated over time to determine facial expression and gestures occurring in the image sequence.
    • 系统通过分析图像序列来跟踪人的头部和脸部特征。 该系统提供两个图像帧之间的头部和面部特征的运动的描述。 运动的这些描述进一步被系统分析以识别面部运动和表达。 该系统使用图像运动的参数化模型分析两幅图像之间的运动。 首先,图像序列中的第一图像被分割成面部区域和多个面部特征区域。 平面模型用于恢复估计第一图像中的分割面部区域与图像序列中的第二图像之间的运动的运动参数。 使用平面模型的估计运动参数将第二图像扭曲或向后移回第一图像,以便相对于第一图像对面部特征进行建模。 使用仿射模型和具有曲率的仿射模型来恢复估计分割的面部特征区域和翘曲的第二图像之间的图像运动的运动参数。 面部特征区域的恢复的运动参数表示第一图像和翘曲图像之间的面部特征的相对运动。 使用面部区域的恢复的运动参数来跟踪第二图像中的面部区域。 使用恢复的面部区域的运动参数和面部特征区域的运动参数来跟踪第二图像中的面部特征区域。 描述描述脸部和面部特征的运动的参数被过滤以得出定义在两个图像之间出现的面部手势的中间谓词。 随着时间的推移评估这些中级谓词以确定图像序列中出现的面部表情和手势。