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Please use this identifier to cite or link to this item: http://hdl.handle.net/10198/2631

Título: Region-based clustering for lung segmentation in low-dose CT images
Autor: Monteiro, Fernando C.
Palavras-chave: Lung segmentation
Graph clustering
Watershed transform
Pulmonary CT image
Issue Date: 2010
Editora: Theodore E. Simos, George Psihoyios, Ch. Tsitouras
Citação: Monteiro, Fernando C. (2010) - Region-based clustering for lung segmentation in low-dose CT images. In CNAAM: International Conference of Numerical Analysis and Applied Mathematics. Rhodes, Greece. ISBN 978-0-7354-0834-0. p.2061-2064.
Relatório da Série N.º: AIP Conference Proceedings;1281
Resumo: Lung segmentation in thoracic computed tomography scans is essential for the development of computer-aided diagnostic methods for identifying the lung diseases. Low-dose CT scans are increasingly utilized in lung studies, but segmenting them with traditional threshold segmentation algorithms often yields less than satisfying results. In this paper we present a hybrid framework to lung segmentation which joints region-based information based on watershed transform with clustering techniques. The proposed method eliminates the task of finding an optimal threshold and the over-segmentation produced by watershed. We have applied our approach on several pulmonary low-dose CT images and the results reveal the robustness and accuracy of this method.
URI: http://hdl.handle.net/10198/2631
ISBN: 978-0-7354-0834-0
Versão do Editor: http://link.aip.org/link/?APCPCS/1281/2061/1
Appears in Collections:DE - Publicações em Proceedings Indexadas ao ISI/Scopus

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