基于多相水平集框架的混合活动轮廓图像分割

Y. Boutiche
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引用次数: 1

摘要

图像分割的一个主要问题是如何建立能够处理各种图像的模型。这是由于图像来源的多样性。然而,目标是尽可能扩大模型分割多个图像模态的能力。一些模型之间的杂交似乎是实现这一目标的一个很好的选择。本文采用了融合了几种图像信息的函数:边缘检测函数、局部均值和方差函数、全局均值函数。这样的选择可以获得成功的分割结果,将在实验部分显示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid active contours in multiphase level set framework for images segmentation
A major problem with image segmentation is the building of model that is able to deal with all kind of image. This is due to the diversity of the image sources. However, the aim is to widen, as much as possible, the capability of the model to segment several image modalities. Hybridization between some models seems a good alternative to achieve that. In this paper, functional that incorporate several kinds of image information is used: edge detector function, local means and variances, and global means. Such choice allows getting successful segmentation results as it will be shown in the experimental section.
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