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Multilayer Perceptron Based Level Sets for Robust Ultrasound Image Segmentation

机译:基于多层感知器的水平集,用于稳健的超声图像分割

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摘要

The class of geometric deformable models, also known as level sets, has brought tremendous impact on medical imagery due to its capability of topology preservation and fast shape recovery. Ultrasound images are often characterized by a high level of speckle causing erroneous detection of contours. This work proposes a new stopping term for level sets, based on the coefficient of variation and a multilayer perceptron, in order to robustly detect the contours in ultrasound images. Successful applications of the MLP-Level Sets to detection of contours on synthetics and real images are presented.
机译:一类几何可变形模型,也称为水平集,由于其拓扑结构保留和快速形状恢复的能力而给医学图像带来了巨大影响。超声图像的特征通常是斑点高,导致轮廓检测错误。这项工作基于变异系数和多层感知器,为水平集提出了一个新的中止术语,以便可靠地检测超声图像中的轮廓。提出了MLP水平集在合成图像和真实图像上检测轮廓的成功应用。

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