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    • 2. 发明授权
    • Marker detection in X-ray images
    • X射线图像中的标记检测
    • US08411927B2
    • 2013-04-02
    • US12898018
    • 2010-10-05
    • Ti-chiun ChangYunqiang ChenMichelle xiaohong YanTong Fang
    • Ti-chiun ChangYunqiang ChenMichelle xiaohong YanTong Fang
    • G06K9/00
    • G06K9/00G06K2209/057G06T7/73G06T2207/10016G06T2207/10121G06T2207/30004G06T2207/30204
    • A method for detecting markers within X-ray images includes applying directional filters to a sequence of X-ray image frames. Marker candidate pixels are determined based on the output of the directional filters. Candidate pixels are grouped into clusters and distances between each possible pair of clusters is determined and the most frequently occurring distance is considered an estimated distance between markers. A first marker is detected at the cluster that most closely resembles a marker based on certain criteria and a second marker is then detected at a cluster that is the estimated distance from the first marker. The pair of first and second marker detections is scored to determine detection quality. If the detected marker pair has an acceptable score then the detected marker pair is used.
    • 用于检测X射线图像内的标记的方法包括对X射线图像帧序列应用方向滤波器。 基于方向滤波器的输出来确定标记候选像素。 候选像素被分组成簇,并且确定每个可能的一对簇之间的距离,并且将最常发生的距离视为标记之间的估计距离。 基于某些标准,在最接近类似于标记的群集处检测到第一标记,然后在作为距离第一标记的估计距离的群集处检测到第二标记。 对一对第一和第二标记检测进行评分以确定检测质量。 如果检测到的标记对具有可接受的分数,则使用检测到的标记对。
    • 8. 发明授权
    • Method and system for human vision model guided medical image quality assessment
    • 人类视觉模型的方法与系统指导医学图像质量评估
    • US08086007B2
    • 2011-12-27
    • US12286970
    • 2008-10-03
    • Michelle Xiao-Hong YanTi-chiun ChangMarkus LendlStefan BoehmTong FangPeter Durlak
    • Michelle Xiao-Hong YanTi-chiun ChangMarkus LendlStefan BoehmTong FangPeter Durlak
    • G06K9/00
    • G06K9/4619G06K2209/05G06T7/0012
    • A method and system for image quality assessment is disclosed. The image quality assessment method is a no-reference method for objectively assessing the quality of medical images. This method is guided by the human vision model in order to accurately reflect human perception. A region of interest (ROI) of medical image is divided into non-overlapping blocks of equal size. Each of the blocks is categorized as a smooth block, a texture block, or an edge block. A perceptual sharpness measure, which is weighted by local contrast, is calculated for each of the edge blocks. A perceptual noise level measure, which is weighted by background luminance, is calculated for each of the smooth blocks. A sharpness quality index is determined based on the perceptual sharpness measures of all of the edge blocks, and a noise level quality index is determined based on the perceptual noise level measures of all of the smooth blocks. An overall image quality index can be determined by using task specific machine learning of samples of annotated images. The image quality assessment method can be used in applications, such as video/image compression and storage in healthcare and homeland security, and band-width limited wireless communication.
    • 公开了一种用于图像质量评估的方法和系统。 图像质量评估方法是客观评估医学图像质量的非参考方法。 这种方法是以人类视觉模型为指导,以准确反映人类的感知。 医学图像的感兴趣区域(ROI)被划分成相等大小的不重叠块。 每个块被分类为平滑块,纹理块或边缘块。 针对每个边缘块计算出由局部对比度加权的感知锐度度量。 为每个平滑块计算感知噪声水平测量,其由背景亮度加权。 基于所有边缘块的感知锐度测量来确定锐度质量指标,并且基于所有平滑块的感知噪声水平测量来确定噪声水平质量指数。 可以通过使用注释图像的样本的任务特定机器学习来确定整体图像质量指数。 图像质量评估方法可用于医疗保健和国土安全中的视频/图像压缩和存储等应用,以及带宽有限的无线通信。