Skip to main content

Diagnosing Heterogeneous Dynamics for CT Scan Images of Human Brain in Wavelet and MFDFA Domain

  • Conference paper
  • First Online:
Advances in Optical Science and Engineering

Abstract

CT scan images of human brain of a particular patient in different cross sections are taken, on which wavelet transform and multi-fractal analysis are applied. The vertical and horizontal unfolding of images are done before analyzing these images. Discrete wavelet transform (DWT) through Daubechies basis are done for identifying fluctuations over polynomial trends for clear characterization of CT scan images of human brain in different cross-sections. A systematic investigation of de-noised images are carried out through wavelet normalized energy and wavelet semi-log plots, which clearly points out the mismatch between results of vertical and horizontal unfolding. The mismatch of results confirms the heterogeneity in spatial domain. Using the multi-fractal de-trended fluctuation analysis (MFDFA), the mismatch between the values of Hurst exponent and width of singularity spectrum by vertical and horizontal unfolding confirms the same.

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 EPUB and 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
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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

Similar content being viewed by others

References

  1. Alfano RR, Das BB, Cleary J, Prudente R, Celmer E (1991) Light sheds light on cancer distinguishing malignant tumors from benign tissues and tumors. Bull NY Acad Med 67(2):143–150

    Google Scholar 

  2. Ramanujam N (2000) Fluorescence spectroscopy of neoplastic and non-neoplastic tissues. Neoplasia 2(12):89

    Google Scholar 

  3. Boustany NN, Boppart SA, Backman V (2010) microscopic imaging and spectroscopy with scattered light. Ann Rev Biomed Eng 12(1):285–314

    Article  Google Scholar 

  4. Schantz SP, Kolli V, Savage HE, Yu G, Shah JP, Harris DE, Katz A, Alfano RR, Huvos AG (1998) Invivo native cellular fluorescence and histological characteristics of head and neck cancer. Clin Cancer Res 4(5):1177–1182

    Google Scholar 

  5. Das N, Chatterjee S, Soni J, Jagtap J, Pradhan A, Sengupta TK, Panigrahi PK, Vitkin IA, Ghosh N (2013) Probing multifractality in tissue refractive index: prospects for precancer detection. Opt Lett 38(2):211–213

    Article  ADS  Google Scholar 

  6. Kantelhardt JW et al (2002) Multifractal detrended fluctuation analysis of nonstationary time series. Phys A 316:87–114

    Article  MATH  Google Scholar 

  7. Daubechies I (1990) The wavelet transform, time-frequency localization and signal analysis. IEEE Trans Inf Theory 36(5):961–1005

    Article  ADS  MATH  MathSciNet  Google Scholar 

  8. Modi JK, Nanavati SP, Phadke AS, Panigrahi PK (2004) Wavelet transforms: application to data analysis-II. Resonance 8–13

    Google Scholar 

  9. Mukhopadhyay S, Das N, Pradhan A, Ghosh N, Panigrahi PK (2014) Pre-cancer detection by wavelet transform and multi-fractality in various grades of DIC stromal images. In: SPIE BIOS, USA

    Google Scholar 

  10. Mukhopadhyay S, Panigrahi PK (2013) Wind speed data analysis for various seasons during a decade by wavelet and S transform. Int J Comput Sci Technol 3(4)

    Google Scholar 

  11. Mukhopadhyay S, Das NK, Kumar R, Dash D, Mitra A, Panigrahi PK (2014) Study of the dynamics of wind data fluctuations: a wavelet and MFDFA based novel method. In: Elsevier Science and Technology Proceeding, IEMCONGRESS, India

    Google Scholar 

  12. Mukhopadhyay S, Das NK, Pradhan A, Ghosh N, Panigrahi PK (2014) Wavelet and multi-fractal based analysis on DIC images in epithelium region to detect and diagnose the cancer progress among different grades of tissues. In: Proceedings of SPIE photonics Europe

    Google Scholar 

Download references

Acknowledgments

The authors thank Bankura Sammilani Medical College and Hospital, Bankura, West Bengal for providing the CT images of human brain in different cross-section.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Soham Mandal .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer India

About this paper

Cite this paper

Mukhopadhyay, S. et al. (2015). Diagnosing Heterogeneous Dynamics for CT Scan Images of Human Brain in Wavelet and MFDFA Domain. In: Lakshminarayanan, V., Bhattacharya, I. (eds) Advances in Optical Science and Engineering. Springer Proceedings in Physics, vol 166. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2367-2_42

Download citation

Publish with us

Policies and ethics