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    • 2. 发明申请
    • METHOD AND APPARATUS FOR IMAGE SEGMENTATION
    • 图像分割的方法和装置
    • US20090129671A1
    • 2009-05-21
    • US11910304
    • 2006-03-28
    • Qingmao HuWieslaw L. Nowinski
    • Qingmao HuWieslaw L. Nowinski
    • G06K9/34G06K9/00
    • G06K9/38G06T7/11G06T7/136G06T2207/10072G06T2207/30016
    • A 3D image may be segmented based on one or more intensity thresholds determined from a subset of the voxels in the 3D image. The subset may contain voxels in a 2D reference slice. A low threshold and a high threshold may be used for segmenting an image, and they may be determined using different thresholding methods, depending on the image type. In one method, two sets of bordering pixels are selected from an image. A statistical measure of intensity of each set of pixels is determined. An intensity threshold value is calculated from the statistical measures for segmenting the image. In another method, the pixels of an image are clustered into clusters of different intensity ranges. An intensity threshold for segmenting the image is calculated as a function of a mean intensity and a standard deviation for pixels in one of the clusters. A further method is a supervised range-constrained thresholding method.
    • 可以基于从3D图像中的体素的子集确定的一个或多个强度阈值来分割3D图像。 子集可以包含二维参考片中的体元。 可以使用低阈值和高阈值来分割图像,并且它们可以使用不同的阈值方法来确定,这取决于图像类型。 在一种方法中,从图像中选择两组边界像素。 确定每组像素的强度的统计量度。 根据用于分割图像的统计度量计算强度阈值。 在另一种方法中,图像的像素被聚集成不同强度范围的簇。 根据平均强度和其中一个簇中的像素的标准偏差来计算用于分割图像的强度阈值。 另一种方法是监督范围约束阈值法。
    • 3. 发明授权
    • Method and device for detecting bright brain regions from computed tomography images
    • 用于从计算机断层摄影图像检测亮脑区域的方法和装置
    • US08849000B2
    • 2014-09-30
    • US13513453
    • 2009-12-04
    • Qingmao Hu
    • Qingmao Hu
    • G06K9/00G06T7/00G06T7/60A61B6/00A61B6/03
    • G06T7/0012A61B6/03A61B6/501G06T7/11G06T7/136G06T7/155G06T7/62G06T7/68G06T2207/10081G06T2207/30016
    • A method and devices are disclosed to detect bright brain regions (BBRs) from clinical non-enhanced computed tomography images through large grayscale, large grayscale asymmetry with respect to the midsagittal plane (MSP), and large grayscale local contrast. An adaptive approach is disclosed to determine thresholds of the 3 features and adjust the window width for data conversion. The substantial grayscale variability of BBRs for a subject is addressed by finding the bright portion followed by recovering. Those BBR voxels symmetrical to the MSP are recovered, partial volume effects are compensated and the high grayscale regions which may not correspond to intracerebral hemorrhage are excluded. The disclosed method and system could be a useful tool to aid classifying stroke types, quantifying intracerebral hemorrhage and enhancing stroke therapy.
    • 公开了一种方法和装置,用于从临床非增强计算机断层摄影图像通过大灰度,相对于中位面(MSP)的大灰度不对称性和大灰度局部对比度来检测亮脑区域(BBR)。 公开了一种自适应方法来确定3个特征的阈值并调整数据转换的窗口宽度。 通过找到明亮部分,然后进行恢复来解决受试者的BBR的实质灰度变化。 恢复与MSP对称的那些BBR体素,补偿部分体积效应,排除可能不对应于脑内出血的高灰度区域。 所公开的方法和系统可以是帮助分类卒中类型,量化脑内出血和增强中风治疗的有用工具。
    • 5. 发明授权
    • Method and apparatus for identifying pathology in brain images
    • 用于识别脑图像病理学的方法和装置
    • US07889895B2
    • 2011-02-15
    • US10582725
    • 2003-12-12
    • Wieslaw Lucjan NowinskiQingmao Hu
    • Wieslaw Lucjan NowinskiQingmao Hu
    • G06K9/00A61B5/05
    • G06T7/0012G06T7/11G06T2207/10072G06T2207/30016
    • A method and apparatus for identifying pathology in a brain image comprises the steps of firstly determining the location of the midsagittal plane (MSP) of the brain illustrated in the image under examination by identifying the symmetry of the two hemispheres based on the determination of up to 16 approximated fissure line segments (AFLSs). Those AFLSs with a larger angular deviation from the MSP than a predefined threshold are considered as outlier AFLSs while the rest are taken as inlier AFLSs. The ratio of the number of the outlier AFLSs to the number of inlier AFLSs is then calculated. A comparison of the ratio with a further predetermined threshold value is made and if the ratio exceeds the further predetermined threshold value, pathology is present in the brain image.
    • 一种用于识别脑图像中的病理学的方法和装置包括以下步骤:首先通过基于最多至多的确定来确定两个半球的对称性来确定所检查的图像中所示的脑的中间正面(MSP)的位置 16个近似裂缝线段(AFLS)。 与MSP相比具有较大角度偏离的那些AFLS比预定义的阈值被认为是异常AFLS,而其余的被认为是非线性AFLS。 然后计算异常值AFLS的数量与异常AFLS的数量的比率。 进行比率与另外的预定阈值的比较,如果比率超过另外的预定阈值,则脑图像中存在病理学。
    • 9. 发明授权
    • Method and apparatus for determining symmetry in 2d and 3d images
    • 用于确定2d和3d图像中的对称性的方法和装置
    • US07409085B2
    • 2008-08-05
    • US10477295
    • 2002-01-18
    • Qingmao HuWieslaw Lucjan Nowinski
    • Qingmao HuWieslaw Lucjan Nowinski
    • G06K9/00G06K9/32G06K9/36
    • G06T7/68G06T2207/30016
    • A method of determining symmetry in an image, a method of determining a symmetry plane line segment of a 3D image, a method of determining a symmetry line of a 2D image, and a computer program product. The method includes a) determining at least one searching line segment within a predefined search area of an image portion, the at least one searching line segment including a reference point (x, y) at its center and an angle θ with respect to a predetermined axis of the image portion; b) for each searching line segment, determining a first local characteristic in accordance with a measurement at points adjacent the searching line segment; c) determining the symmetry in the image in accordance with a calculation based on the first local characteristic.
    • 一种确定图像中的对称性的方法,确定3D图像的对称平面线段的方法,确定2D图像的对称线的方法以及计算机程序产品。 该方法包括:a)确定图像部分的预定义搜索区域内的至少一个搜索线段,所述至少一个搜索线段包括其中心处的参考点(x,y)和相对于预定的 图像部分的轴线; b)对于每个搜索线段,根据与搜索线段相邻的点处的测量来确定第一局部特征; c)根据基于第一局部特性的计算确定图像中的对称性。
    • 10. 发明申请
    • Methods and apparatus for binarising images
    • 二值化图像的方法和装置
    • US20070122033A1
    • 2007-05-31
    • US10582439
    • 2004-12-09
    • Qingmao HuZujun HouWieslaw Nowinski
    • Qingmao HuZujun HouWieslaw Nowinski
    • G06K9/34G06K9/38G06K9/00
    • G06K9/38G06T7/11G06T7/136G06T7/194G06T2207/10088G06T2207/20012G06T2207/30016
    • A method is proposed for binarising an image by deriving an intensity threshold and classifying pixels according to whether their intensity is below or above the threshold. In the derivation of the threshold, prior konwledge is used to define a region of interest (ROI) in the image. Furthermore, prior knowledge is used to select a range in the frequency distribution of the intensities of the pixels in the ROI, and that only data within this frequency range is used to derive the threshold. These techniques provide a highly effective mechanism for incorporating prior knowledge into the threshold selection which is critical whether the image is a medical image or not. In particular, a threshold can be found to binarise images which exhibits high robustness to imaging artefacts such a gray level inhomogeneity and noise.
    • 提出了一种通过导出强度阈值来对图像进行二进制化的方法,并根据它们的强度是否低于阈值来分类像素。 在阈值的推导中,先前的konwledge用于定义图像中的感兴趣区域(ROI)。 此外,使用现有知识来选择ROI中的像素的强度的频率分布中的范围,并且仅使用该频率范围内的数据来导出阈值。 这些技术提供了将先验知识结合到阈值选择中的非常有效的机制,这对于图像是否是医学图像是至关重要的。 特别地,可以发现阈值对于对成像伪像(如灰度不均匀性和噪声)表现出高鲁棒性的图像进行二值化。