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A Novel Level Set Based Shape Prior Method for Liver Segmentation from MRI Images

机译:基于新型基于级别的MRI图像肝脏分割的形状方法

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Liver segmentation in MR Image is the first step of our automated liver perfusion analysis project. Traditional Level Set methods and active contours were often used to segment the liver, but the results were not always promising due to noise and the low gradient response on the liver boundary. In this paper we propose a novel level set based variational approach that incorporates shape prior knowledge into the improved Chan-Vese's model [1] which can overcome the leakage and over-segmentation problems. The experiments are taken on abdomen MRI series and the results reveal that our improved level set based shape prior method can segment liver shape precisely and a refined liver perfusion curve without respiration affection can be achieved.
机译:MR Image中的肝脏分割是我们自动肝灌注分析项目的第一步。传统的水平设定方法和活性轮廓通常用于分割肝脏,但由于噪声和肝脏边界的低梯度响应,结果并不总是有效。在本文中,我们提出了一种基于新型的基于水平集的变分方法,该方法将形状纳入了改进的Chan-Vese的模型[1],这可以克服泄漏和过分分割问题。实验涉及腹部MRI系列,结果表明,我们的改进水平组的形状现实方法可以精确地分段肝脏形状,并且可以实现没有呼吸浓厚的细化肝灌注曲线。

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