Joint graph cut and relative fuzzy connectedness image segmentation algorithm.

Joint graph cut and relative fuzzy connectedness image segmentation algorithm.
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DOI:
10.1016/j.media.2013.06.006
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发表时间:
2013-12
影响因子:
10.9
通讯作者:
Udupa, Jayaram K.
Udupa, Jayaram K.
中科院分区:
工程技术1区
文献类型:
--
作者:
Ciesielski, Krzysztof Chris;Miranda, Paulo A. V.;Falcao, Alexandre X.;Udupa, Jayaram K.

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我们介绍了一种图像分割算法,称为,它以一种新颖的方式结合了两种流行算法的优势:相对模糊连接(RFC)和(标准)图切(GC)。我们在理论上和实验上都表明,这保留了RFC在种子选择方面的鲁棒性(因此,避免了GC的“收缩问题”),同时保持了GC对“尽管定义不清的边界段泄漏”问题的更强控制。我们最近的理论结果极大地促进了RFC可以在广义GC (GGC)分割算法框架内描述的分析。在我们的实现中,我们使用RFC算法的一个版本(基于图像森林变换)作为子例程,它(可证明)在相对于图像大小的线性时间内运行。这导致运行时间接近线性。基于各种医学和非医学图像的实验比较表明,与GC、迭代版本的RFC (IRFC)和功率分水岭(PW)相比,这些方法的精度性能优于其他方法。
We introduce an image segmentation algorithm, called , which combines, in novel manner, the strengths of two popular algorithms: Relative Fuzzy Connectedness (RFC) and (standard) Graph Cut (GC). We show, both theoretically and experimentally, that preserves robustness of RFC with respect to the seed choice (thus, avoiding “shrinking problem” of GC), while keeping GC’s stronger control over the problem of “leaking though poorly defined boundary segments.” The analysis of is greatly facilitated by our recent theoretical results that RFC can be described within the framework of Generalized GC (GGC) segmentation algorithms. In our implementation of we use, as a subroutine, a version of RFC algorithm (based on Image Forest Transform) that runs (provably) in linear time with respect to the image size. This results in running in a time close to linear. Experimental comparison of to GC, an iterative version of RFC (IRFC), and power watershed (PW), based on a variety medical and non-medical images, indicates superior accuracy performance of over these other methods, resulting in a rank ordering of .
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