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Authors
Advisor(s)
Abstract(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.
Description
Keywords
Lung segmentation Graph clustering Watershed transform Pulmonary CT image
Citation
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.
Publisher
Theodore E. Simos, George Psihoyios, Ch. Tsitouras