Skip to main content

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 247))

Abstract

Change detection is a process of identifying the changes in a state of an object over time. We use the phenomena of change detection to detect the changes occurring in MRI of brain having cancerous and non cancerous lesions. A Hybrid Particle Swarm Optimization algorithm that incorporates a Wavelet theory based mutation operation is used for segmentation of lesions in Magnetic Resonance Images. The segmented lesions are the Region of Interest. This method of using change detection algorithm would be helpful in detecting changes in Region of Interests of MRI with lesions and also to view the progress of treatment for cancerous lesions.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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.

References

  1. Rao, S.S.: Engineering optimization:Theory and practice, 4th edn., pp. 709–711. John Wiley and Sons (2009)

    Google Scholar 

  2. De, A., Das, R.L., Bhattacharjee, A.K., Sharma, D.: Masking based segmentation of diseased MRI images. In: Proceedings of the IEEE International Conference on Information Science and Applications, ICISA 2010, Seoul chapter, Seoul, Korea, pp. 230–236 (2010)

    Google Scholar 

  3. Kabir, Y., Dojat, M., Scherrer, B., Forbes, F., Garbay, C.: Multimodal MRI Segmentation of Ischemic Stroke lesions. In: Proceedings of the 29th Annual International Conference of the IEEE EMBS, Cite Internationale, Lyon France (2007)

    Google Scholar 

  4. De, A., Bhattacharjee, A.K., Chanda, C.K., Maji, B.: MRI Segmentation using Entropy Maximization and HybridParticle Swarm Optimization with Wavelet Mutation. In: Proceedings of World Congress on Information and Communication Technologies (WICT 2011), Mumbai, pp. 362–367 (2011)

    Google Scholar 

  5. De, A., Bhattacharjee, A.K., Chanda, C.K., Maji, B.: Hybrid Particle Swarm Optimization with Wavelet Mutation based Segmentation and Progressive Transmission Technique for MRI Images. International Journal of Innovative Computing, Information and Control 8(7(B)), 5179–5197 (2012)

    Google Scholar 

  6. Saha, P.K., Udupa, J.K.: Optimum image thresholding via class uncertainty and region homogeneity. IEEE Trans. Pattern Anal. Mach. Intell. 23(7), 689–706 (2001)

    Article  Google Scholar 

  7. Otsu, N.: A thresholding selection method from gray- level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

  8. Kittler, J., Illingworth, J.: On threshold selection using clustering criteria. IEEE Trans. Syst. Man Cybern. 15(5), 652–655 (1985)

    Article  Google Scholar 

  9. Hu, Q., Hou, Z., Nowinski, W.L.: Supervised range- constrained thresholding. IEEE Trans. Image Process 15(1), 228–240 (2006)

    Article  Google Scholar 

  10. Qiao, Y., Hu, Q., Qian, G., Luo, S., Nowinski, W.L.: Thresholding based on variance and intensity contrast. Pattern Recognition 40, 596–608 (2007)

    Article  MATH  Google Scholar 

  11. Singh, A.: Digital change detection techniques using remotely-sensed data. Int. J. Remote Sens. 10, 989–1003 (1989)

    Article  Google Scholar 

  12. Lunetta, R.S., Elvidge, C.D.: Remote Sensing change Detection: Environmental Monitoring Methods and Applications. Ann Arbor Press, Chelsea (1998)

    Google Scholar 

  13. Ling, S.H., Iu, H.H.C., Leung, F.H.F., Chan, K.Y.: Improved Hybrid Particle Swarm Optimized Wavelet Neural Network for Modeling the Development of Fluid Dispensing for Electronic Packaging. IEEE Transactions on Industrial Electronics 55(9), 3447–3460 (2008)

    Article  Google Scholar 

  14. Duabechies, I.: Ten lectures on Wavelets. Society for Industrial and Applied Mathematics, Philadelphia (1992)

    Book  Google Scholar 

  15. Ahmed, A.A.E., Germano, L.T., Antonio, Z.C.: A hybrid particle swarm optimization applied to loss power minimization. IEEE Transactions on Power Systems 20(2), 859–866 (2005)

    Article  Google Scholar 

  16. Radke, R.J., Andra, S., Al-Kofahi, O., Roysam, B.: Image Change Detection Algorithms: A Systematic Survey. IEEE Trans. Image. Process 14, 294–307 (2005)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ankita Mitra .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Mitra, A., De, A., Bhattacharjee, A.K. (2014). Detection of Progression of Lesions in MRI Using Change Detection. In: Satapathy, S., Udgata, S., Biswal, B. (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013. Advances in Intelligent Systems and Computing, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-319-02931-3_53

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-02931-3_53

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02930-6

  • Online ISBN: 978-3-319-02931-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics