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Type: Artigo de periódico
Title: Data Clustering as an Optimum-Path Forest Problem with Applications in Image Analysis
Author: Rocha, LM
Cappabianco, FAM
Falcao, AX
Abstract: We propose an approach for data clustering based on optimum-path forest. The samples are taken as nodes of a graph, whose arcs are defined by an adjacency relation. The nodes are weighted by their probability density values (pdf) and a connectivity function is maximized, such that each maximum of the pdf becomes root of an optimum-path tree (cluster), composed by samples 'more strongly connected' to that maximum than to any other root. We discuss the advantages over other pdf-based approaches and present extensions to large datasets with results for interactive image segmentation and for fast, accurate, and automatic brain tissue classification in magnetic resonance (MR) images. We also include experimental comparisons with other clustering approaches. (C) 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 50-68, 2009; Published online in Wiley InterScience ( DOI 10.1002/ima.20191
Subject: optimum-path forest
image segmentation
gm/wm classification
Country: EUA
Editor: John Wiley & Sons Inc
Citation: International Journal Of Imaging Systems And Technology. John Wiley & Sons Inc, v. 19, n. 2, n. 50, n. 68, 2009.
Rights: fechado
Identifier DOI: 10.1002/ima.20191
Date Issue: 2009
Appears in Collections:Unicamp - Artigos e Outros Documentos

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