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Efficient Segmentation of Lung Abnormalities in CT Images

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Image Analysis and Recognition (ICIAR 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5627))

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Abstract

This paper introduces an efficient technique for lung abnormalities segmentation in CT images based on the use of dual-tree complex wavelet transform (DT-CWT) and multilevel histogram thresholding. Recently, a scalar wavelet-based method has shown favorable results compared with previous approaches in honeycomb detection in pediatric CT images. Using our recently designed dual-tree complex filter bank and employing high resolution intensity similarities, we show that DT-CWT outperforms the results obtained with discrete wavelet transform (DWT) in general. Our early experiments show that multi-wavelets (MW) can also present a promising performance than DWT. The results indicate that DT-CWT performs slightly better than multi-wavelets, however, it can significantly outperform scalar wavelets. The former is probably due to better edge preserving property of multi-wavelets, while the latter is obtained because of good directionality and shift-invariance of dual-tree complex wavelet transform.

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Author information

Authors and Affiliations

  1. University of Windsor, Ontario, N9B3P4

    Aryaz Baradarani & Q. M. Jonathan Wu

Authors
  1. Aryaz Baradarani
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  2. Q. M. Jonathan Wu
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo, N2L 3G1, Waterloo, Ontario, Canada

    Mohamed Kamel

  2. Faculty of Engineering, Institute of Biomedical Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio Campilho

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© 2009 Springer-Verlag Berlin Heidelberg

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Baradarani, A., Wu, Q.M.J. (2009). Efficient Segmentation of Lung Abnormalities in CT Images. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2009. Lecture Notes in Computer Science, vol 5627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02611-9_74

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  • DOI: https://doi.org/10.1007/978-3-642-02611-9_74

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02610-2

  • Online ISBN: 978-3-642-02611-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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