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1. 发明授权

US07596256B1 Computer assisted detection of lesions in volumetric medical images 有权
标题翻译：计算机辅助检测体积医学图像中的病变
公开(公告)号：US07596256B1
公开(公告)日：2009-09-29
申请号：US10246070
申请日：2002-09-16
申请人： Kaufman Arie , Dongqing Chen , Jerome Liang , Mark R. Wax
发明人： Kaufman Arie , Dongqing Chen , Jerome Liang , Mark R. Wax
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , G06T7/12 , G06T2207/10072 , G06T2207/30032
摘要： 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.
摘要翻译：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。

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