Skip to main content
Journal of the Brazilian Computer Society
Download PDF

Detection of point landmarks in 3D medical images via phase congruency model

Journal of the Brazilian Computer Society volume 17, pages 117–132 (2011)Cite this article

Abstract

This paper presents a novel technique for detection of point landmarks in volumetric medical images based on a three-dimensional (3D) Phase Congruency (PC) model. A bank of 3D log-Gabor filters is specially designed in the frequency domain and used to compute 3D energy maps, which are further combined to form the phase congruency measure. The PC measure is invariant to intensity variations and contrast resolution and provides a good indication of feature significance in an image. To detect significant 3D point landmarks, eigen-analysis of a 3×3 matrix of second-order PC moments, computed for each point in the image, is performed followed by local maxima detection. Two different application scenarios in radiation therapy planning of the head and neck anatomy are used to illustrate the feasibility and usefulness of the proposed method.

References

  1. 1.

    Allaire S, Kim J, Breen S, Jaffray D, Pekar V (2008) Full orientation invariance and improved feature selectivity of 3D SIFT with application to medical image analysis. In: MMBIA-2008, Anchorage, AK, USA, pp 1–8

    Google Scholar 

  2. 2.

    Bookstein FL (1989) Principal warps: Thin-plate splines and decomposition of deformations. IEEE Trans Pattern Anal Mach Intell 11(6):567–585

    Article  Google Scholar 

  3. 3.

    Cao G, Shi P, Hu B (2006) Ultrasonic liver discrimination using 2-D phase congruency. IEEE Trans Biomed Eng 53(10):2116–2119

    Article  Google Scholar 

  4. 4.

    Cheung W, Hamarneh G (2007) N-sift: N-dimensional scale invariant feature transform for matching medical images. In: 4th international symposium on biomedical imaging: From nano to macro (ISBI) 2007, Washington, DC, USA, pp 12–15

    Google Scholar 

  5. 5.

    Collins DL, Zijdenbos AP, Kollokian V, Sled JG, Kabani NJ, Holmes CJ, Evans AC (1998) Design and construction of a realistic digital brain phantom. IEEE Trans Med Imaging 17(3):463–468

    Article  Google Scholar 

  6. 6.

    Dosil R, Pardo XM (2003) Generalized ellipsoids and anisotropic filtering for segmentation improvement in 3-D medical imaging. Image Vis Comput 21(4):325–343

    Article  Google Scholar 

  7. 7.

    Dosil R, Pardo XM, Fdez-Vidal XR (2005) Decomposition of three-dimensional medical images into visual patterns. IEEE Trans Biomed Eng 52(12):2115–2118

    Article  Google Scholar 

  8. 8.

    Ferrari RJ, Hill KA, Plewes DB, Martel AL (2008) Can bilateral asymmetry analysis of breast MR images provide additional information for detection of breast diseases. In: XXI Brazilian symposium on computer graphics and image processing—SIBGRAPI 2008, Campo Grande, MS, Brazil, pp 113–120

    Chapter  Google Scholar 

  9. 9.

    Field DJ (1987) Relations between the statistics of natural images and the response properties of cortical cells. J Opt Soc Am A 4(12):2379–2394

    Article  Google Scholar 

  10. 10.

    Field DJ (1993) Scale-invariance and self-similar wavelet transforms: an analysis of natural scenes and mammalian visual systems. Oxford University Press, New York

    Google Scholar 

  11. 11.

    Förstner W (1986) A feature based correspondence algorithm for image matching. Int Arch Photogramm Remote Sens 26(3):150–166

    Google Scholar 

  12. 12.

    Frigo M, Johnson SG (2005) The design and implementation of FFTW3. Proc IEEE 93(2):216–231. Special issue on “Program Generation, Optimization, and Platform Adaptation”

    Article  Google Scholar 

  13. 13.

    Gispert JD, Reig S, Pascau J, Vaquero JJ, Garcia-Barreno P, Desco M (2004) Method for bias field correction of brain T1-weighted magnetic resonance images minimizing segmentation error. Hum Brain Mapp 22:133–144

    Article  Google Scholar 

  14. 14.

    Granlund G, Knutsson H (1995) Signal processing for computer vision. Kluwer Academic, Boston

    Book  Google Scholar 

  15. 15.

    Jaffray D, Brock KK, Ferrari R, Pekar V (2008) Applications of image processing in image-guided radiation therapy. Medica Mundi 52(1):32–39

    Google Scholar 

  16. 16.

    Jähne B (1997) Digital image processing, 4th edn. Springer, San Diego

    Google Scholar 

  17. 17.

    Kaus MR, Brock KK (2009) In: Deformable image registration for radiation therapy planning. Biomechanical system technology: computational methods, vol 1. World Scientific, Singapore, pp 1–28

    Google Scholar 

  18. 18.

    Kitchen L, Rosenfeld A (1982) Gray-level corner detection. Pattern Recognit Lett 1:95–102

    Article  Google Scholar 

  19. 19.

    Kovesi P (1999) Image features from phase congruency. Videre, J Comput Vis Res 1(3):2–26

    Google Scholar 

  20. 20.

    Kovesi P (2000) Phase congruency: A low-level image invariant. Psychol Res 64:136–148

    Article  Google Scholar 

  21. 21.

    Kovesi P (2003) Phase congruency detects corners and edges. In: The Australian pattern recognition society conference: DICTA, Sydney, Australia, December, pp 309–318

    Google Scholar 

  22. 22.

    Leavens C, Vik T, Schulz H, Allaire S, Kim J, Dawson L, O’Sullivan B, Breen S, Jaffray D, Pekar V (2008) Validation of automatic landmark identification for atlas-based segmentation for radiation treatment planning of the head-and-neck region. In: Proceedings of the SPIE conference on medical imaging, vol 6914. San Diego, CA, USA, pp 3G1–3G7,

    Google Scholar 

  23. 23.

    Linguraru MG, Marias K, English R, Brady M (2006) A biologically inspired algorithm for microcalcification cluster detection. Med Image Anal 10(6):850–862

    Article  Google Scholar 

  24. 24.

    Liu J, Gao W, Huang S, Nowinski WL (2008) A model-based, semi-global segmentation approach for automatic 3-d point landmark localization in neuroimages. IEEE Trans Med Imaging 27:1034–1044

    Article  Google Scholar 

  25. 25.

    Lo C-H, Don H-S (1989) 3D moment forms: Their construction and application to object identification and positioning. IEEE Trans Pattern Anal Mach Intell 11(10):1053–1064

    Article  Google Scholar 

  26. 26.

    Mageras GS, Mechalakos J (2007) Planning in the igrt context: closing the loop. Semin Radiat Oncol 17(4):268–277

    Article  Google Scholar 

  27. 27.

    Mallat S (1989) A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693

    Article  Google Scholar 

  28. 28.

    Marquardt D (1963) An algorithm for least-squares estimation of nonlinear parameters. J Appl Math 11:431–441

    MathSciNet  Google Scholar 

  29. 29.

    McLoughlin KJ, Bones PJ, Kovesi PD (2002) Connective tissue representation for detection of microcalcifications in digital mammograms. Prog Biomed Opt Imaging 3(22):1246–1256

    Google Scholar 

  30. 30.

    Monga O, Benayoun S (1995) Using partial derivatives of 3-D images to extract typical surface features. Comput Vis Image Underst 61(2):171–189

    Article  Google Scholar 

  31. 31.

    Morrone MC, Burr DC (1988) Feature detection in human vision: A phase-dependent energy model. Proc R Soc Lond B 235:221–245

    Article  Google Scholar 

  32. 32.

    Morrone MC, Owens RA (1987) Feature detection from local energy. Pattern Recognit Lett 6(5):303–313

    Article  Google Scholar 

  33. 33.

    Morrone MC, Ross JR, Burr DC, Owens RA (1986) Mach bands are phase dependent. Nature 324(6094):250–253

    Article  Google Scholar 

  34. 34.

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

    MathSciNet  Article  Google Scholar 

  35. 35.

    Pekar V, McNutt TR, Kaus MR (2004) Automated model-based organ delineation for radiotherapy planning in prostatic region. Int J Radiat Oncol Biol Phys 60(3):973–980

    Article  Google Scholar 

  36. 36.

    Pudney C, Kovesi P, Robbins B (1995) Feature detection using oriented local energy for 3D confocal microscope images. Image Anal Appl Comput Graph 1024:274–282

    Article  Google Scholar 

  37. 37.

    Rohr K (1997) On 3D differential operators for detecting point landmarks. Image Vis Comput 15(3):220–233

    Article  Google Scholar 

  38. 38.

    Ruiz-Alzola J, Kikinis R, Westin CF (2001) Detection of point landmarks in multidimensional tensor data. Signal Process 81:2243–2247

    Article  Google Scholar 

  39. 39.

    Shi XW (2010) GPU implementation of fast Gabor filters. In: Proceedings of 2010 IEEE international symposium on circuits and systems, Paris, France, June, pp 373–376

    Google Scholar 

  40. 40.

    Slabaugh G, Kong K, Unal G, Fang T (2007) Variational guidewire tracking using phase congruency. In: Medical image computing and computer-assisted intervention (MCCAI) 2007, vol 4792, pp 612–619

    Chapter  Google Scholar 

  41. 41.

    Thirion J-H (1996) New feature points based on geometric invariants for 3-D image registration. Int J Comput Vis 18(2):121–137

    Article  Google Scholar 

  42. 42.

    Troost EGC, Schinagl DAX, Bussink J, Boerman OC, van der Kogel AJ, Oyen WJG, Kaanders JHAM (2009) Innovations in radiotherapy planning of head and neck cancers: Role of PET. J Nucl Med 51(1):66–76

    Article  Google Scholar 

  43. 43.

    Venkatesh S, Ownes R (1990) On the classification of image features. Pattern Recognit Lett 11(5):339–349

    Article  Google Scholar 

  44. 44.

    Wang H, Dong L, Lii MF, Lee AL, Crevoisier R, Mohan R, Cox JD, Kuban DA, Cheung R (2005) Implementation and validation of a three-dimensional deformable registration algorithm for target prostate cancer radiotherapy. Int J Radiat Oncol Biol Phys 61(3):725–735

    Article  Google Scholar 

  45. 45.

    Wong A, Bishop W (2008) Efficient least squares fusion of MRI and CT images using a phase congruency model. Pattern Recognit Lett 29(3):173–180

    Article  Google Scholar 

  46. 46.

    Wong A, Orchard J (2009) Robust multimodal registration using local phase-coherence representations. J Signal Process Syst 54:89–100

    Article  Google Scholar 

  47. 47.

    Wörz S, Rohr K (2006) Localization of anatomical point landmarks in 3-D medical images by fitting 3-D parametric intensity models. Med Image Anal 10(1):41–58

    Article  Google Scholar 

  48. 48.

    Xing L, Thorndyke B, Schreibmann E, Yang Y, Li T, Kim G, Luxton G, Koong A (2006) Overview of image-guided radiation therapy. Med Dosim 31(2):91–112

    Article  Google Scholar 

  49. 49.

    Xings L, Siebers J, Keall P (2007) Computational challenges for image-guided radiation therapy: Framework and current research. Semin Radiat Oncol 17(4):245–257

    Article  Google Scholar 

Download references

Author information

Affiliations

  1. Department of Computer Sciences, Federal University of São Carlos (UFSCar), Rod. Washington Luís, Km 235, Caixa Postal 676, 13565-905, São Carlos, SP, Brazil

    Ricardo J. Ferrari

  2. Princess Margaret Hospital, University of Toronto, Toronto, ON, Canada

    Stéphane Allaire, Andrew Hope, John Kim & David Jaffray

  3. Philips Research North America, Toronto, Markham, ON, Canada

    Vladimir Pekar

Authors
  1. Ricardo J. Ferrari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stéphane Allaire
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrew Hope
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. John Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. David Jaffray
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Vladimir Pekar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ricardo J. Ferrari.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Ferrari, R.J., Allaire, S., Hope, A. et al. Detection of point landmarks in 3D medical images via phase congruency model. J Braz Comput Soc 17, 117–132 (2011). https://doi.org/10.1007/s13173-011-0032-8

Download citation

Keywords

  • Point landmarks
  • 3D hase congruency
  • 3D log-Gabor filters
  • Wavelets
  • Nonrigid registration
  • Radiation therapy