Iterative relative fuzzy connectedness for multiple objects with multiple seeds
Iterative relative fuzzy connectedness for multiple objects with multiple seeds
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DOI：
10.1016/j.cviu.2006.10.005
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发表时间：
2007-09-01
影响因子：
4.5
通讯作者：
Zhuge, Ying
中科院分区：
文献类型：
--
作者：
Cieslelski, Krzysztof Chris;Udupa, Jayaram K.;Zhuge, Ying
本文提出了一种基于每对图像元素之间的连接强度的图像分割新理论和算法。分割算法中使用的对象定义利用迭代相对模糊连通性（IRFC）的概念。在先前发表的研究中，IRFC理论仅针对分割仅涉及两个片段（对象和背景）并且每个片段由单个种子指示的情况而开发。(See Udupa等人[J.K.乌杜帕峰萨哈河Lotufo，相对模糊连通性和对象定义：理论，算法和图像分割应用。IEEE传输模式分析马赫内特尔24（2002）1485-1500]和Saba和Udupa [P.K.萨哈，J.K. Udupa，迭代相对模糊连通性和对象定义：理论，算法和图像分割中的应用，在：生物医学图像分析数学方法IEEE研讨会论文集，希尔顿头，南卡罗来纳州，2002年，第100页。（第28-35段）我们的理论解决了[J.K.乌杜帕峰Salta，Fuzzy connectedness in image segmentation，Proc. IEEE 91（10）（2003）1649-1669]，允许涉及任意数量的对象的同时分割。此外，每个片段可以由多于一个种子指示，这通常比单个种子对象标识更自然和更容易。IRFC算法的第一个迭代步骤给出了一个被称为相对模糊连通性（RFC）的分割。因此，IRFC技术是RFC方法的扩展。尽管RFC理论，由于Saha和Udupa [P.K.萨哈，J.K. Udupa，多个对象之间的相对模糊连通性：图像分割中的理论、算法和应用，Comput。维斯。图像理解82（1）（2001）42-56]是在多对象/多种子框架中开发的，这里提出的理论结果本质上要微妙得多，并且不使用[PK]的结果萨尔塔，J.K. Udupa，多个对象之间的相对模糊连通性：理论，算法和图像分割中的应用，计算机。维斯。图像理解82（1）（2001）42-56]。另一方面，[P.K.萨尔塔，J.K. Udupa，多个对象之间的相对模糊连通性：理论，算法和图像分割中的应用，计算机。维斯。图像理解82（1）（2001）42-56]是本文所述结果的直接结果。此外，新的框架不仅包含以前的模糊连通性描述，但也揭示了新的光。因此，本文在理论上取得了一些基本进展。我们提出的例子，通过我们的基于IRFC的算法在多对象/多种子环境中获得的分割，并将其与基于RFC的算法获得的结果进行比较。我们的研究结果表明，在许多情况下，IRFC优于RFC，但也存在的情况下，在性能上的增益可以忽略不计。(c)2006年爱思唯尔公司All rights reserved.
In this paper we present a new theory and an algorithm for image segmentation based on a strength of connectedness between every pair of image elements. The object definition used in the segmentation algorithm utilizes the notion of iterative relative fuzzy connectedness, IRFC. In previously published research, the IRFC theory was developed only for the case when the segmentation was involved with just two segments, an object and a background, and each of the segments was indicated by a single seed. (See Udupa et al. [J.K. Udupa, P.K. Saha, R.A. Lotufo, Relative fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 24 (2002) 1485-1500] and Saba and Udupa [P.K. Saha, J.K. Udupa, Iterative relative fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation, in: Proceedings of IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, Hilton Head, South Carolina, 2002, pp. 28-35].) Our theory, which solves a problem of Udupa and Saha from [J.K. Udupa, P.K. Salta, Fuzzy connectedness in image segmentation, Proc. IEEE 91 (10) (2003) 1649-1669], allows simultaneous segmentation involving an arbitrary number of objects. Moreover, each segment can be indicated by more than one seed, which is often more natural and easier than a single seed object identification. The first iteration step of the IRFC algorithm gives a segmentation known as relative fuzzy connectedness, RFC, segmentation. Thus, the IRFC technique is an extension of the RFC method. Although the RFC theory, due to Saha and Udupa [P.K. Saha, J.K. Udupa, Relative fuzzy connectedness among multiple objects: theory, algorithms, and applications in image segmentation, Comput. Vis. Image Understand. 82 (1) (2001) 42-56], is developed in the multi object/multi seed framework, the theoretical results presented here are considerably more delicate in nature and do not use the results from [P.K. Salta, J.K. Udupa, Relative fuzzy connectedness among multiple objects: theory, algorithms, and applications in image segmentation, Comput. Vis. Image Understand. 82 (1) (2001) 42-56]. On the other hand, the theoretical results from [P.K. Salta, J.K. Udupa, Relative fuzzy connectedness among multiple objects: theory, algorithms, and applications in image segmentation, Comput. Vis. Image Understand. 82 (1) (2001) 42-56] are immediate consequences of the results presented here. Moreover, the new framework not only subsumes previous fuzzy connectedness descriptions but also sheds new light on them. Thus, there are fundamental theoretical advances made in this paper. We present examples of segmentations obtained via our IRFC-based algorithm in the multi-object/multi-seed environment, and compare it with the results obtained with the RFC-based algorithm. Our results indicate that, in many situations, IRFC outperforms RFC, but there also exist instances where the gain in performance is negligible.(c) 2006 Elsevier Inc. All rights reserved.