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Liver Segmentation from CT Images Using a Modified Distance Regularized Level Set Model Based on a Novel Balloon Force

机译:基于新型气球力的修正距离正则化水平集模型从CT图像进行肝脏分割

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摘要

Organ segmentation from medical images is still an open problem and liver segmentation is a much more challenging task among other organ segmentations. This paper presents a liver egmentation method from a sequence of computer tomography images.We propose a novel balloon force that controls the direction of the evolution process and slows down the evolving contour in regions with weak or without edges and discourages the evolving contour from going far away from the liver boundary or from leaking at a region that has a weak edge, or does not have an edge. The model is implemented using a modified Distance Regularized Level Set (DRLS) model. The experimental results show that the method can achieve a satisfactory result. Comparing with the original DRLS model, our model is more effective in dealing with over segmentation problems.
机译:从医学图像进行器官分割仍然是一个未解决的问题,在其他器官分割中,肝脏分割是一项更具挑战性的任务。本文从一系列计算机断层扫描图像中提出了一种肝脏分割方法。我们提出了一种新颖的气球力,该气球力可控制演化过程的方向并减缓边缘较弱或无边缘的区域的轮廓发展,并阻止轮廓不断发展远离肝脏边界或在边缘较弱或没有边缘的区域泄漏。该模型是使用修改后的距离正则化水平集(DRLS)模型实现的。实验结果表明,该方法可以取得满意的效果。与原始的DRLS模型相比,我们的模型在处理过度分割问题上更为有效。

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