A variational model with hybrid images data fitting energies for segmentation of images with intensity inhomogeneity
A variational model with hybrid images data fitting energies for segmentation of images with intensity inhomogeneity
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DOI：
10.1016/j.patcog.2015.08.022
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发表时间：
2016-03-01
影响因子：
8
通讯作者：
Khan, Gulzar Ali
中科院分区：
文献类型：
--
作者：
Ali, Haider;Badshah, Noor;Khan, Gulzar Ali
基于水平集函数的变分图像分割模型提供了可靠的方法来捕获给定图像中的对象/区域的边界，前提是底层强度具有均匀性。在Chan-Vese（2001）的著名工作及其许多变体中，满意地处理了具有基本上分段恒定强度的图像的情况。然而，对于在对象的前景内具有强度不均匀性或多相位的图像，这样的模型变得不充分，因为检测到的边缘甚至相位不表示对象，因此没有意义。针对这一问题，本文提出了一种新的基于区域和边缘增强量的双拟合项变分模型。测试和比较将表明，我们的新模型优于以前的两个模型。合成和真实的生活图像被用来说明我们的新模型的可靠性和优势。(C)2015爱思唯尔有限公司版权所有。
Level set functions based variational image segmentation models provide reliable methods to capture boundaries of objects/regions in a given image, provided that the underlying intensity has homogeneity. The case of images with essentially piecewise constant intensities is satisfactorily dealt with in the well-known work of Chan-Vese (2001) and its many variants. However for images with intensity inhomogeneity or multiphases within the foreground of objects, such models become inadequate because the detected edges and even phases do not represent objects and are hence not meaningful. To deal with such problems, in this paper, we have proposed a new variational model with two fitting terms based on regions and edges enhanced quantities respectively from multiplicative and difference images. Tests and comparisons will show that our new model outperforms two previous models. Both synthetic and real life images are used to illustrate the reliability and advantages of our new model. (C) 2015 Elsevier Ltd. All rights reserved.