公开(公告)号：US20150317793A1

公开(公告)日：2015-11-05

申请号：US14698894

申请日：2015-04-29

Applicant: Huazhong University of Science and Technology

Inventor： Tianxu ZHANG ,  Yining HUANG ,  Zhenghua HUANG ,  Yaxun WEI ,  Longwei HAO

IPC: G06T7/00 ,  G06T5/50 ,  G06T7/20

CPC classification number: G06T7/0012 ,  G06T5/50 ,  G06T7/20 ,  G06T7/246 ,  G06T7/262 ,  G06T2207/10116 ,  G06T2207/20224 ,  G06T2207/30048 ,  G06T2207/30101 ,  G06T2211/404

Abstract: A method for separating and estimating multiple motion parameters in an X-ray angiogram image. The method includes: determining a cardiac motion signal cycle and a variation frame sequence of translational motion according to an angiogram image sequence, tracing structure feature points of vessels in the angiogram image sequence whereby obtaining a motion sequence, processing the motion sequence via multivariable optimization and Fourier frequency-domain filtering, separating an optimum translational motion curve, a cardiac motion curve, a respiratory motion curve and a high-frequency motion curve according to the variation frame sequence of translational motion, a cycle of the cardiac motion signal, a range of a respiratory motion signal cycle, and a range of a high-frequency motion signal cycle.

Abstract translation: 一种用于在X射线血管造影图像中分离和估计多个运动参数的方法。 该方法包括：根据血管造影图像序列确定心脏运动信号循环和平移运动的变化帧序列，跟踪血管造影图像序列中血管的结构特征点，从而获得运动序列，通过多变量优化处理运动序列;以及 傅里叶频域滤波，根据平移运动的变化帧序列分离最佳平移运动曲线，心脏运动曲线，呼吸运动曲线和高频运动曲线，心脏运动信号的周期， 呼吸运动信号周期以及高频运动信号周期的范围。

公开(公告)号：US20160189394A1

公开(公告)日：2016-06-30

申请号：US14960461

申请日：2015-12-07

Applicant: Huazhong University of Science and Technology

Inventor： Tianxu ZHANG ,  Zhenghua HUANG ,  Lihua DENG ,  Yaozong ZHANG ,  Qiong SONG

IPC: G06T7/20 ,  G06T17/00 ,  A61B6/00 ,  G06T7/00

CPC classification number: A61B6/504 ,  A61B6/486 ,  A61B6/5217 ,  G06T7/246 ,  G06T7/262 ,  G06T2207/10016 ,  G06T2207/10116 ,  G06T2207/20056 ,  G06T2207/30048

Abstract: A method for extracting motion parameters from angiography images using a multi-parameter model. The method includes: 1) extracting I vascular structural feature points automatically from a medical image of an angiography image sequence, and auto-tracking the feature points respectively in the angiography image sequence to obtain a tracking sequence of each feature point; 2) performing a discrete Fourier transformation on the tracking sequence of each feature point to obtain a discrete Fourier transformation result; initializing an iterative parameter, and obtaining amplitude range and frequency range of each frequency point of the discrete Fourier transformation result; 3) performing a Fourier transformation on a tracking sequence of each frequency point in the amplitude range and the frequency range thereof to obtain Fourier transformation results; and 4) performing an inverse Fourier transformation on the Fourier transformation results, and obtaining an estimated minimum mean square error of each frequency point.

Abstract translation: 一种使用多参数模型从血管造影图像中提取运动参数的方法。 该方法包括：1）从血管造影图像序列的医学图像自动提取I血管结构特征点，并在血管造影图像序列中自动跟踪特征点，获得每个特征点的跟踪序列; 2）对每个特征点的跟踪序列执行离散傅里叶变换以获得离散傅里叶变换结果; 初始化迭代参数，并获得离散傅立叶变换结果的每个频点的幅度范围和频率范围; 3）对振幅范围及其频率范围内的每个频点的跟踪序列执行傅里叶变换，以获得傅里叶变换结果; 以及4）对所述傅立叶变换结果执行逆傅立叶变换，并且获得每个频点的估计的最小均方误差。

公开(公告)号：US20160363451A1

公开(公告)日：2016-12-15

申请号：US15105459

申请日：2015-02-10

Applicant: HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Tianxu ZHANG ,  Sufei FAN ,  Yimeng CHEN ,  Heng YAO ,  Zhengtao LI ,  Zheng WANG ,  Wei HUANG ,  Zhenghua HUANG

IPC: G01C21/20

CPC classification number: G01C21/20 ,  B64G1/24 ,  G01C3/14 ,  G01S17/023 ,  G01S17/08 ,  G01S17/875 ,  G06T7/70 ,  G06T2207/10021 ,  G06T2207/30261

Abstract: The present invention discloses a multi-sensor merging based super-close distance autonomous navigation apparatus and method. The apparatus includes a sensor subsystem, an information merging subsystem, a sensor scanning structure, and an orientation guiding structure, wherein a visible light imaging sensor and an infrared imaging sensor are combined together, and data are acquired by combining a passive measurement mode composed of an optical imaging sensor and an active measurement mode composed of a laser distance measuring sensor. Autonomous navigation is divided into three stages, that is, a remote distance stage, implemented by adopting a navigation mode where a binocular visible light imaging sensor and a binocular infrared imaging sensor are combined, a close distance stage, implemented by adopting a navigation mode where a binocular visible light imaging sensor, a binocular infrared imaging sensor and a laser distance measuring sensor array are combined, and an ultra-close distance stage, implemented by adopting a navigation mode of a laser distance measuring sensor array. Through the present invention, the field of view and the exploration range are widened, the problem of shielding existing in passive measurement is effectively solved, the precision of data measurement is ensured, and the navigation efficiency and the safety and reliability of navigation are improved.

Abstract translation: 本发明公开了一种基于多传感器合并的超近距离自主导航装置和方法。 该装置包括传感器子系统，信息合并子系统，传感器扫描结构和定向引导结构，其中可见光成像传感器和红外成像传感器组合在一起，并且通过组合由 光学成像传感器和由激光测距传感器组成的主动测量模式。 自主导航分为三个阶段，即通过采用双目可见光成像传感器和双目红外成像传感器组合的导航模式实现的远距离舞台，近距离舞台，采用导航模式 将双目可见光成像传感器，双目红外成像传感器和激光距离测量传感器阵列组合在一起，并采用激光测距传感器阵列的导航模式实现的超近距离级。 通过本发明，扩大了视野和探索范围，有效解决了被动测量中存在的屏蔽问题，确保了数据测量的精度，提高了导航效率和导航的安全可靠性。