[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Texture quantification of medical images using a novel complex space-frequency transform

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Objective

To develop an efficient space-frequency transform for texture analysis and demonstrate its application on magnetic resonance (MR) images of multiple sclerosis (MS) patients.

Materials and methods

We applied our new transform to MR images of three enhancing lesions from two relapsing-remitting MS patients acquired serially over 9 months. Local spectra of the images were generated using our new technique, and spatial frequencies corresponding to MS lesion activity were extracted by applying a band-pass filter and inverting. We examined the changes in T2 intensity and low-frequency energy (LFE) over time within the lesion, surrounding tissue and a region of normal-appearing white matter (NAWM).

Results

We calculated complex local spectra of 428 × 428 images in approximately 1 min and achieved a spatial frequency resolution of 0.05 cm−1. We observed an increase in LFE within the lesion and a drop in LFE in the hyperintense border of tissue surrounding the lesion.

Conclusion

We have developed an efficient, invertible transform that produces high-resolution local frequency spectra of an MR image in approximately 1 min. Negative LFE values in the boundaries of an active lesion may help discriminate between the core lesion undergoing demyelination and a border of inflammation.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Kent DL, Haynor DR, Longstreth WT, Larson EB (1994) The clinical efficacy of magnetic resonance imaging in neuroimaging. Ann Intern Med 120: 856–871

    PubMed  CAS  Google Scholar 

  2. Weiller C, May A, Sach M, Buhmann C, Rijntjes M (2006) Role of functional imaging in neurological disorders. JMRI 23: 840–850

    Article  PubMed  Google Scholar 

  3. Matsuda H (2007) The role of neuroimaging in mild cognitive impariment. Neuropathology 27: 570–577

    Article  PubMed  Google Scholar 

  4. Neema M, Stankiewicz J, Arora A, Guss ZD, Bakshi R (2007) MRI in multiple sclerosis: what’s inside the toolbox?. Neurotherapeutics 4: 602–617

    Article  PubMed  Google Scholar 

  5. Bakshi R, Minagar A, Jaisani Z, Wolinsky JS (2005) Imaging of multiple sclerosis: role in neurotherapeutics. NeuroRx 2: 277–303

    Article  PubMed  Google Scholar 

  6. Bakshi R, Hutton GJ, Miller JR, Radue EW (2004) The use of magnetic resonance imaging in the diagnosis and long-term management of multiple sclerosis. Neurology 63(Suppl 5): S3–S11

    PubMed  Google Scholar 

  7. Zivadinov R, Cox JL (2007) Neuroimaging in multiple sclerosis. Int Rev Neurobiol 79: 449–474

    Article  PubMed  Google Scholar 

  8. Zhang Y, Metz LM, Zhu H, Yong VW, Mitchell JR (2006) Texture response of deep gray matter in patients with multiple sclerosis treated with minocycline. Proc 14th Int Soc Magn Reson, p 2100

  9. Zhang Y, Zhu H, Metz LM, Mitchell JR (2004) A new MRI texture measure to quantify MS lesion progression. Proc 12th Int Soc Magn Reson Med, p 1500

  10. Lerski RA, Straughan K, Schad LR, Boyce D, Blüml S, Zuna I (1993) MR image texture analysis—an approach to tissue characterization. Magn Reson Imaging 11: 873–887

    Article  PubMed  CAS  Google Scholar 

  11. Castellano G, Bonilha L, Li LM, Cendes F (2004) Texture analysis of medical images. Clin Radiol 59: 1061–1069

    Article  PubMed  CAS  Google Scholar 

  12. Krumm J, Shafer SA (1990) Local spatial frequency analysis of image texture. Proc IEEE Int Conf Computer Vision, pp 354–358

  13. Zhu H, Goodyear BG, Lauzon ML, Brown RA, Mayer GS, Law AG, Mansinha L, Mitchell JR (2003) A new local multiscale Fourier analysis for medical imaging. Med Phys 30: 1134–1141

    Article  PubMed  CAS  Google Scholar 

  14. Zlatescu M, Sijben A, Roldan G, Easaw J, Forsyth P, Parney I, Sevick R, Yan E, Demetrick D, Mitchell R (2008) The use of magnetic resonance imaging to noninvasively detect genetic signatures in oligodendroglioma. Clin Cancer Res 14: 2357–2362

    Article  PubMed  Google Scholar 

  15. Zhu H, Zhang Y, Wei X, Metz LM, Law AG, Mitchell JR (2004) MR multi-spectral texture analysis using space-frequency information. Proc Int Conf METMBS, pp 173–179

  16. Zhu H, Wei X, Zhang Y, Mayer GS, Mitchell JR (2003) Temporal texture analysis of normal appearing white matter in multiple sclerosis. Proc 11th Int Soc Magn Reson Med, p 277

  17. Zhang Y, Wells J, Buist R, Peeling J, Yong VW, Mitchell JR (2006) A novel MRI texture analysis of demyelination and inflammation in relapsing-remitting experimental allergic encephalomyelitis. Lect Notes Comput Sci 4190: 760–767

    Article  Google Scholar 

  18. Brown RA, Zhu H, Mitchell JR (2005) Distributed vector processing of a new local multiscale Fourier transform for medical imaging applications. IEEE Trans Med Imaging 24: 689–691

    Article  PubMed  Google Scholar 

  19. Mansinha L, Stockwell RG, Lowe RP, Eramian M, Schincariol RA (1997) Local S-spectrum analysis of 1-D and 2-D data. Phys Earth Planet Inter 103: 329–336

    Article  Google Scholar 

  20. Mansinha L, Stockwell RG, Lowe RP (1997) Pattern analysis with two-dimensional spectral localisation: Applications of two- dimensional S transforms. Physica A 239: 286–295

    Article  Google Scholar 

  21. Mitchell R, Zhu H, Zhang Y, Law A (2007) Image texture segmentation using the polar S-transform and principal component analysis. US Patent 7,259,767

  22. Schimmel M, Gallart J (2005) The inverse S-transform in filters with time-frequency localization. IEEE Trans Signal Process 53: 4417–4422

    Article  Google Scholar 

  23. Pinnegar CR (2007) Comments on “The inverse S-transform in filters with time-frequency localization”. IEEE Trans Signal Process 55: 5117–5120

    Article  Google Scholar 

  24. Cotton F, Weiner HL, Jolesz FA, Guttmann CRG (2003) MRI contrast uptake in new lesions in relapsing-remitting MS followed at weekly intervals. Neurology 60: 640–646

    Article  PubMed  Google Scholar 

  25. Meier DS, Guttmann CRG (2006) MRI time series modeling of MS lesion development. Neuroimage 32: 531–537

    Article  PubMed  Google Scholar 

  26. Meier DS, Guttmann CRG (2003) Time-series analysis of MRI intensity patterns in multiple sclerosis. Neuroimage 20: 1193–1209

    Article  PubMed  Google Scholar 

  27. Meier DS, Weiner HL, Guttmann CRG (2007) Time-series modeling of multiple sclerosis disease activity: a promising window on disease progression and repair potential?. Neurotherapeutics 4: 485–498

    Article  PubMed  Google Scholar 

  28. Sahraian MA, Radue EW (2008) MS lesions in T2-weighted images. MRI atlas of MS lesions. Springer, Berlin, pp 3–9

    Chapter  Google Scholar 

  29. Drabycz S, Mitchell JR (2008) A novel pixel-by-pixel texture analysis technique improves frequency resolution of local MS spectra. Proc 16th Int Soc Magn Reson Med, p 1543

  30. Metz LM, Zhang Y, Yeung M, Patry DG, Bell RB, Stoian CA, Yong VW, PattenSB Duquette P, Antel JP, Mitchell JR (2004) Minocycline reduces gadolinium-enhancing magnetic resonance imaging lesions in multiple sclerosis. Ann Neurol 55: 756

    Article  PubMed  Google Scholar 

  31. Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 17: 87–97

    Article  PubMed  CAS  Google Scholar 

  32. McAuliffe MJ, Lalonde FM, McGarry D, Gandler W, Csaky K, Trus BL (2001) Medical image processing, analysis & visualization in clinical research. IEEE Computer-Based Medical Systems (CBMS), pp 381–386

  33. Rombouts SARB, Scheltens P, Kuijer JPA, Barkhof F (2007) Whole brain analysis of T2* weighted baseline fMRI signal in dementia. Hum Brain Mapp 28: 1313–1317

    Article  PubMed  Google Scholar 

  34. Pham DL, Prince JL (1999) Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans Med Imaging 18: 737–752

    Article  PubMed  CAS  Google Scholar 

  35. Nesbit GM, Forbes GS, Scheithauer BW, Okazaki H, Rodriguez M (1991) Multiple sclerosis: Histopathologic and MR and/or CT correlation in 37 cases at biopsy and three cases at autopsy. Neuroradiology 180: 467–474

    CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB, Canada, T2N 1N4

    Sylvia Drabycz

  2. Departments of Radiology and Clinical Neurosciences, University of Calgary, 2500 University Dr NW, Calgary, AB, Canada, T2N 1N4

    J. Ross Mitchell

Authors
  1. Sylvia Drabycz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Ross Mitchell
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. Ross Mitchell.

Additional information

This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Alberta Heritage Foundation for Medical Research (AHFMR). The authors would like to acknowledge the support of the Alberta Informatics Circle of Research Excellece (iCORE).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Drabycz, S., Mitchell, J.R. Texture quantification of medical images using a novel complex space-frequency transform. Int J CARS 3, 465–475 (2008). https://doi.org/10.1007/s11548-008-0219-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-008-0219-4

Keywords

Search

Navigation