Pulmonary parenchyma segmentation in thin CT image sequences with spectral clustering and geodesic active contour model based on similarity

Nana He, Xiaolong Zhang, Juanjuan Zhao, Huilan Zhao, Yan Qiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

While the popular thin layer scanning technology of spiral CT has helped to improve diagnoses of lung diseases, the large volumes of scanning images produced by the technology also dramatically increase the load of physicians in lesion detection. Computer-aided diagnosis techniques like lesions segmentation in thin CT sequences have been developed to address this issue, but it remains a challenge to achieve high segmentation efficiency and accuracy without much involvement of human manual intervention. In this paper, we present our research on automated segmentation of lung parenchyma with an improved geodesic active contour model that is geodesic active contour model based on similarity (GACBS). Combining spectral clustering algorithm based on Nystrom (SCN) with GACBS, this algorithm first extracts key image slices, then uses these slices to generate an initial contour of pulmonary parenchyma of un-segmented slices with an interpolation algorithm, and finally segments lung parenchyma of un-segmented slices. Experimental results show that the segmentation results generated by our method are close to what manual segmentation can produce, with an average volume overlap ratio of 91.48%.

Original languageEnglish (US)
Title of host publicationNinth International Conference on Digital Image Processing, ICDIP 2017
EditorsXudong Jiang, Charles M. Falco
PublisherSPIE
ISBN (Electronic)9781510613041
DOIs
Publication statusPublished - Jan 1 2017
Event9th International Conference on Digital Image Processing, ICDIP 2017 - Hong Kong, China
Duration: May 19 2017May 22 2017

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10420
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

Other9th International Conference on Digital Image Processing, ICDIP 2017
CountryChina
CityHong Kong
Period5/19/175/22/17

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All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

He, N., Zhang, X., Zhao, J., Zhao, H., & Qiang, Y. (2017). Pulmonary parenchyma segmentation in thin CT image sequences with spectral clustering and geodesic active contour model based on similarity. In X. Jiang, & C. M. Falco (Eds.), Ninth International Conference on Digital Image Processing, ICDIP 2017 [104202G] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10420). SPIE. https://doi.org/10.1117/12.2281942