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Automatic image segmentation using a deformable model based on charged particles

机译:使用基于带电粒子的可变形模型进行自动图像分割

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摘要

We present a method for automatic segmentation of grey-scale images, based on a recently introduced deformable model, the charged-particle model (CPM). The model is inspired by classical electrodynamics and is based on a simulation of charged particles moving in an electrostatic field. The charges are attracted towards the contours of the objects of interest by an electrostatic field, whose sources are computed based on the gradient-magnitude image. Unlike the case of active contours, extensive user interaction in the initialization phase is not mandatory, and segmentation can be performed automatically. To demonstrate the reliability of the model, we conducted experiments on a large database of microscopic images of diatom shells. Since the shells are highly textured, a post-processing step is necessary in order to extract only their outlines.
机译:我们基于最近推出的可变形模型,带电粒子模型(CPM),提出了一种自动分割灰度图像的方法。该模型的灵感来自经典的电动力学原理,并且基于带电粒子在静电场中运动的模拟。电荷被静电场吸引到感兴趣对象的轮廓,该静电场的源是根据梯度幅度图像计算的。与活动轮廓的情况不同,在初始化阶段不必进行广泛的用户交互,并且可以自动执行分段。为了证明该模型的可靠性,我们在大型硅藻壳显微图像数据库上进行了实验。由于外壳具有很高的质感,因此必须进行后处理步骤,以便仅提取其轮廓。

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