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Finding CLS Using Multiresolution Oriented Local Energy Feature Detection

机译:使用多分辨率导向的本地能量特征检测来查找CLS

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In this paper, we present a novel technique for the detection of the curvilinear structures (CLS) in a mammogram based on a multiresolution, oriented local energy analysis. Local energy enables the detection not only of linear structures; but also features of several different kinds in a unified framework. It is possible to distinguish between such feature types using the local phase. In a separate post-processing stage, the behaviour of energy over multiple scales can be used to determine a) whether a response is due to a feature or to noise and b) to estimate at each location the local width of a CLS. Orientation information computed from steerable filters is used in the same post-processing stage to distinguish between curvilinear structures and speck-like responses such as microcalcifications which, on a micro-scale, resemble CLS. By combining scale, phase and orientation information we can distinguish the CLS from non-CLS locally linear features as well as localised structures with high gradients and thus remove only the CLS whilst leaving the remaining important image information intact.
机译:在本文中,我们提出了一种新的技术,用于检测基于多分辨率的局部局部能量分析的乳房X线照片中的曲线结构(CLS)。局部能量不仅可以检测不仅是线性结构;但是在统一框架中的几种不同种类的特征。可以使用本地阶段区分这些特征类型。在单独的后处理阶段,可以使用多个尺度上的能量的行为来确定a)响应是否由于特征或噪声和b)来估计CLS的局部宽度的每个位置。从可操纵过滤器计算的取向信息用于相同的后处理阶段,以区分曲线结构和诸如微透露的微钙化的曲线结构和诸如微钙化的诸如微级的响应。通过组合规模,阶段和方向信息,我们可以将来自非CLS局部线性特征的CL和具有高梯度的局部结构区分开,因此仅留下剩余的重要图像信息完整地移除CLS。

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