9 March 2011 Comparison of fuzzy connectedness and graph cut segmentation algorithms
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Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 796203 (2011) https://doi.org/10.1117/12.872522
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States
Abstract
The goal of this paper is a theoretical and experimental comparison of two popular image segmentation algorithms: fuzzy connectedness (FC) and graph cut (GC). On the theoretical side, our emphasis will be on describing a common framework in which both of these methods can be expressed. We will give a full analysis of the framework and describe precisely a place which each of the two methods occupies in it. Within the same framework, other region based segmentation methods, like watershed, can also be expressed. We will also discuss in detail the relationship between FC segmentations obtained via image forest transform (IFT) algorithms, as opposed to FC segmentations obtained by other standard versions of FC algorithms. We also present an experimental comparison of the performance of FC and GC algorithms. This concentrates on comparing the actual (as opposed to provable worst scenario) algorithms' running time, as well as influence of the choice of the seeds on the output.
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Krzysztof Chris Ciesielski, Jayaram K. Udupa, A. X. Falcão, P. A. V. Miranda, "Comparison of fuzzy connectedness and graph cut segmentation algorithms", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 796203 (9 March 2011); doi: 10.1117/12.872522; https://doi.org/10.1117/12.872522
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