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Level set issues for efficient image segmentation

机译：水平集问题以实现有效的图像分割

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摘要

Distance mapping possessing computational advantage largely decides the effectiveness of level sets for image segmentation. This article presents the impact of several distance mapping and level set methods suggested in the literature and provides an effective way of handling it. Different distance metric schemes such as the Euclidean, city-block and chessboard distances have been very much prevalent in the literature. This article highlights the use of an effective, fast and efficient distance-mapping technique, i.e. distance mapping using scanning and filling technique, proposed by the authors in their earlier work. Further, this article emphasises the need of periodic reinitialisation of the level set function to a signed distance function which makes curvature term become redundant. As the curve evolves, the level set function loses its signed distance property, leading to reduction in the evolution speed. Frequent reinitialisation of the level set to signed distance function overcomes this limitation and increases the speed of evolution.
机译：具有计算优势的距离映射很大程度上决定了水平集用于图像分割的有效性。本文介绍了文献中建议的几种距离映射和水平集方法的影响，并提供了一种有效的处理方法。诸如欧几里得距离，城市街区距离和棋盘距离之类的不同距离度量方案在文献中已经非常普遍。本文重点介绍了作者在早期工作中提出的一种有效，快速且有效的距离映射技术，即使用扫描和填充技术的距离映射。此外，本文强调需要将级别集函数定期重新初始化为有符号距离函数，这会使曲率项变得多余。随着曲线的发展，水平集功能会失去其有符号距离属性，从而导致发展速度降低。频繁重新初始化设置为有符号距离函数的水平可克服此限制，并提高进化速度。

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