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Active contours based on weighted gradient vector flow and balloon forces for medical image segmentation

机译:基于加权梯度矢量流和气球力的主动轮廓用于医学图像分割

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摘要

Active contours, or snakes, have been widely used for image segmentation purposes. However, high noise sensitivity and poor performance over weak edges are the most acute issues that hinder the segmentation accuracy of these curves, particularly in medical images. In order to overcome these issues, we propose a novel external force that integrates gradient vector flow (GVF) field forces and balloon forces based on a weighting factor computed according to local image features. The proposed external force reduces noise sensitivity, improves performance over weak edges and allows initialization with a single manually selected point. We evaluate the proposed external force for segmentation of various regions on real MRI and CT slices. Evaluation results show that the proposed approach leads to more accurate segmentation than snakes using traditional external forces
机译:活动轮廓或蛇已被广泛用于图像分割。但是,高噪声灵敏度和较弱边缘的较差性能是最严重的问题,这些问题阻碍了这些曲线的分割精度,尤其是在医学图像中。为了克服这些问题,我们提出了一种新颖的外力,该外力基于根据局部图像特征计算的加权因子,将梯度矢量流(GVF)场力和球囊力整合在一起。建议的外力降低了噪声敏感性,改善了弱边缘的性能,并允许通过单个手动选择的点进行初始化。我们评估了建议的外力,用于在真实的MRI和CT切片上分割各个区域。评估结果表明,与使用传统外力的蛇相比,该方法可实现更精确的分割

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