Skip to main content

Learning Longitudinal Deformations for Adaptive Segmentation of Lung Fields from Serial Chest Radiographs

  • Conference paper
Medical Imaging and Augmented Reality (MIAR 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5128))

Included in the following conference series:


We previously developed a deformable model for segmenting lung fields in serial chest radiographs by using both population-based and patient-specific shape statistics, and obtained higher accuracy compared to other methods. However, this method uses an ad hoc way to evenly partition the boundary of lung fields into some short segments, in order to capture the patient-specific shape statistics from a small number of samples by principal component analysis (PCA). This ad hoc partition can lead to a segment including points with different amounts of longitudinal deformations, thus rendering it difficult to capture principal variations from a small number of samples using PCA. In this paper, we propose a learning technique to adaptively partition the boundary of lung fields into short segments according to the longitudinal deformations learned for each boundary point. Therefore, all points in the same short segment own similar longitudinal deformations and thus small variations within all longitudinal samples of a patient, which enables effective capture of patient-specific shape statistics by PCA. Experimental results show the improved performance of the proposed method in segmenting the lung fields from serial chest radiographs.

This work is supported by Science and Technology Commission of Shanghai Municipality of China (Grant number: 06dz22103).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. van Ginneken, B., ter Haar Romeny, B.M., Viergever, M.A.: Computer-Aided Diagnosis in Chest Radiography: a Survey. IEEE Trans. On Medical Imaging 20(12), 1228–1241 (2001)

    Article  Google Scholar 

  2. van Ginneken, B., Stegmann, M.B., Loog, M.: Segmentation of Anatomical Structures in Chest Radiographs using supervised methods: a Comparative Study on a Public Database. Medical Image Analysis 10, 19–40 (2006)

    Article  Google Scholar 

  3. Cootes, T.F., Taylor, C.J.: Statistical Models of appearance for Computer Vision. Technical Report, Wolfson Image Analysis Unit, University of Manchester (2001)

    Google Scholar 

  4. Shi, Y., Qi, F., Xue, Z., Chen, L., Ito, K., Matsuo, H., Shen, D.: Segmenting Lung Fields in Serial Chest Radiographs Using Both Population-based and Patient-specific Shape Statistics. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 83–91. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  5. Davatzikos, C., Tao, X., Shen, D.: Hierarchical Active Shape Models Using the Wavelet Transform. IEEE Trans on Medical Imaging 22(3), 414–423 (2003)

    Article  Google Scholar 

  6. Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  7. Hontani, H., Deguchi, K.: Primitive Curve Generation Based on Multiscale Contour Figure Approximation. In: Proceeding of 15th International Conference on Pattern Recognition, vol. 2, pp. 887–890 (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Editor information

Takeyoshi Dohi Ichiro Sakuma Hongen Liao

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Shi, Y., Shen, D. (2008). Learning Longitudinal Deformations for Adaptive Segmentation of Lung Fields from Serial Chest Radiographs. In: Dohi, T., Sakuma, I., Liao, H. (eds) Medical Imaging and Augmented Reality. MIAR 2008. Lecture Notes in Computer Science, vol 5128. Springer, Berlin, Heidelberg.

Download citation

  • DOI:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-79981-8

  • Online ISBN: 978-3-540-79982-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics