Skip to main content

CLASSIC: Consistent Longitudinal Alignment and Segmentation for Serial Image Computing

  • Conference paper
Information Processing in Medical Imaging (IPMI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3565))

Abstract

This paper proposes a temporally-consistent and spatially-adaptive longitudinal MR brain image segmentation algorithm, referred to as CLASSIC, which aims at obtaining accurate measurements of rates of change of regional and global brain volumes from serial MR images. The algorithm incorporates image-adaptive clustering, spatiotemporal smoothness constraints, and image warping to jointly segment a series of 3-D MR brain images of the same subject that might be undergoing changes due to development, aging or disease. Morphological changes, such as growth or atrophy, are also estimated as part of the algorithm. Experimental results on simulated and real longitudinal MR brain images show both segmentation accuracy and longitudinal consistency.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Bezdek, J., Hall, L., Clarke, L.: Review of MR image segmentation techniques using pattern recognition. Medical Physics 20, 1033–1048 (1993)

    Article  Google Scholar 

  2. Pappas, T.: An adaptive clustering algorithm for image segmentation. IEEE Trans. on Signal Processing 40, 901–914 (1992)

    Article  Google Scholar 

  3. Udupa, J., Samarasekera, S.: Fuzzy connectedness and object definition: theory, algorithms and applications in image segmentation. Graph. Models Images Processing 58, 246–261 (1996)

    Article  Google Scholar 

  4. Brandt, M., Bohan, T., Kranmer, L., Fletcher, J.: Estimation of CSF, white and gray matter volumes in hydrocephalic children using fuzzy clustering of MR images. Comput. Med. Imag. Graph. 18, 25–34 (1994)

    Article  Google Scholar 

  5. Lim, K., Prefferbaum, A.: Segmentation of MR brain images into cerebrospinal fluid, white and gray matter. Journal of Comput. Assisted Tomogr. 13, 588–593 (1989)

    Article  Google Scholar 

  6. Pham, D., Prince, J.: Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans. on Medical Imaging 18, 737–752 (1999)

    Article  Google Scholar 

  7. Chen, W., Giger, M.: A fuzzy c-mean (FCM) based algorithm for intensity inhomogeneity correction and segmentation of MR images. In: IEEE International Symposium on Biomedical Imaging (ISBI 2004), Arlington, VA, pp. 1307–1310 (2004)

    Google Scholar 

  8. Rezaee, M., van der Zwet, P., Lelieveldt, B., van der Geest, R., Reiber, J.: A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering. IEEE Trans. on Image Processing 9, 1238–1248 (2000)

    Article  Google Scholar 

  9. Resnick, S., Goldszal, A., Davatzikos, C., Golski, S., Kraut, M., Metter, E., Bryan, R., Zonderman, A.: One-year age changes in MRI brain volumes in older adults. Cerebral Cortex 10, 464–472 (2000)

    Article  Google Scholar 

  10. Bezdek, J., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Computers and Geosciences 10, 191–203 (1984)

    Article  Google Scholar 

  11. Guillemaud, R., Brady, M.: Estimating the bias field of MR images. IEEE Trans. on Medical Imaging 20, 57–68 (1998)

    Google Scholar 

  12. Ahmed, M., Yamany, S., Mohamed, N., Farag, A., Moriarty, T.: A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans. on Medical Imaging 21, 193–199 (2002)

    Article  Google Scholar 

  13. Liew, A., Leung, S., Lau, W.: Fuzzy image clustering incorporating spatial continuity. IEE Proc. Vis. Image Signal Process. 147, 185–192 (2000)

    Article  Google Scholar 

  14. Pham, D.: Spatial model for fuzzy clustering. Computer Vision and Image Understanding 84, 285–297 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  15. Shen, D., Davatzikos, C.: Measuring temporal morphological changes robustly in brain MR images via 4-D template warping. NeuroImage 21, 1508–1517 (2004)

    Article  Google Scholar 

  16. Shen, D., Davatzikos, C.: HAMMER: Hierarchical attribute matching mechanism for elastic registration. IEEE Trans. on Medical Imaging 21, 1421–1439 (2002)

    Article  Google Scholar 

  17. Nyul, G., Udupa, J., Zhang, X.: New variants of a method of MRI scale standardization. IEEE Trans. on Medical Imaging 19, 143–150 (2000)

    Article  Google Scholar 

  18. Zhu, C., Liu, F., Zhu, L., Jiang, T.: Anatomy dependent multi-context fuzzy clustering for separation of brain tissues in mr images. In: 2nd International Workshop on Medial Imaging and Augmented Reality, China, pp. 197–203 (2004)

    Google Scholar 

  19. Karacali, B., Davatzikos, C.: Simulation of tissue atrophy using a topology preserving transformation model. Submit to IEEE Trans. on Medical Imaging (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Xue, Z., Shen, D., Davatzikos, C. (2005). CLASSIC: Consistent Longitudinal Alignment and Segmentation for Serial Image Computing. In: Christensen, G.E., Sonka, M. (eds) Information Processing in Medical Imaging. IPMI 2005. Lecture Notes in Computer Science, vol 3565. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11505730_9

Download citation

  • DOI: https://doi.org/10.1007/11505730_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26545-0

  • Online ISBN: 978-3-540-31676-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics