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Type: Artigo de periódico
Title: Joint graph cut and relative fuzzy connectedness image segmentation algorithm
Author: Ciesielski, KC
Miranda, PAV
Falcao, AX
Udupa, JK
Abstract: We introduce an image segmentation algorithm, called GC(sum)(max), 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 GC(sum)(max) 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 GC(sum)(max) 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 GC(sum)(max) 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 GC(sum)(max) running in a time close to linear. Experimental comparison of GC(sum)(max) 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 GC(sum)(max) over these other methods, resulting in a rank ordering of GC(sum)(max) > PW similar to IRFC > GC. (C) 2013 Elsevier B.V. All rights reserved.
Subject: Image segmentation
Fuzzy connectedness
Graph cut
Country: Holanda
Editor: Elsevier Science Bv
Citation: Medical Image Analysis. Elsevier Science Bv, v. 17, n. 8, n. 1046, n. 1057, 2013.
Rights: fechado
Identifier DOI: 10.1016/
Date Issue: 2013
Appears in Collections:Unicamp - Artigos e Outros Documentos

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