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Publicação
Region-based clustering for lung segmentation in low-dose CT images
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Autores
Orientador(es)
Resumo(s)
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.
Descrição
Palavras-chave
Lung segmentation Graph clustering Watershed transform Pulmonary CT image
Contexto Educativo
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.
Editora
Theodore E. Simos, George Psihoyios, Ch. Tsitouras