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Reduction of false positives in the detection of architectural distortion in mammograms by using a geometrically constrained phase portrait model

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Objective One of the commonly missed signs of breast cancer is architectural distortion. We have developed techniques for the detection of architectural distortion in mammograms, based on the analysis of oriented texture through the application of Gabor filters and a linear phase portrait model. In this paper, we propose constraining the shape of the general phase portrait model as a means to reduce the false-positive rate in the detection of architectural distortion.

Material and methods The methods were tested with one set of 19 cases of architectural distortion and 41 normal mammograms, and with another set of 37 cases of architectural distortion.

Results Sensitivity rates of 84% with 4.5 false positives per image and 81% with 10 false positives per image were obtained for the two sets of images.

Conclusion The adoption of a constrained phase portrait model with a symmetric matrix and the incorporation of its condition number in the analysis resulted in a reduction in the false-positive rate in the detection of architectural distortion. The proposed techniques, dedicated for the detection and localization of architectural distortion, should lead to efficient detection of early signs of breast cancer.

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References

  1. National Cancer Institute of Canada, Canadian cancer statistics 2004, Toronto, ON, Canada (2004) available at http://www.cancer.ca/vgn/images/portal/cit_86751114/48/28/401594768cw_2005stats_en.pdf, accessed on March 7, 2006

  2. Schneider MA (2000) Better detection: improving our chances. In: Digital mammography: 5th international workshop on digital mammography. Yaffe MJ (ed) Medical Physics Publishing, Toronto, pp 3–6

  3. Heywang-Köbrunner SH, Schreer I, Dershaw DD (1997) Diagnostic breast imaging: mammography, sonography, magnetic resonance imaging, and interventional procedures. Thieme Medical Publishers, New York

    Google Scholar 

  4. Blanks RG, Wallis MG, Moss SM (1998) A comparison of cancer detection rates achieved by breast cancer screening programmes by number of readers, for one and two view mammography: Results from the UK National Health Service Breast Screening Programme. J Med Screen 5(4):195–201

    PubMed  CAS  Google Scholar 

  5. Burrell HC, Sibbering DM, Wilson ARM, Pinder SE, Evans AJ, Yeoman LJ, Elston CW, Ellis IO, Blamey RW, Robertson JFR (1996) Screening interval breast cancers: mammographic features and prognostic factors. Radiology 199(4):811–817

    PubMed  CAS  Google Scholar 

  6. Baker JA, Rosen EL, Lo JY, Gimenez EI, Walsh R, Soo MS (2003) Computer-aided detection (CAD) in screening mammography: sensitivity of commercial CAD systems for detecting architectural distortion. Am. J. Roentgenol. 181:1083–1088

    Google Scholar 

  7. American College of Radiology (ACR) (1998) Illustrated breast imaging reporting and data system (BI-RADS), 3rd edn. American College of Radiology, Reston

    Google Scholar 

  8. Sampat MP, Whitman GJ, Markey MK, Bovik AC (2005) Evidence based detection of spiculated masses and architectural distortion. In: Fitzpatrick JM, Reinhardt JM (eds) Proceedings of SPIE medical imaging 2005: image processing, vol. 5747, San Diego, pp 26–37

  9. Guo Q, Shao J, Ruiz V (2005) Investigation of support vector machine for the detection of architectural distortion in mammographic images. J Phys Conf Ser 15:88–94

    Article  Google Scholar 

  10. Tourassi GD, Delong DM, Floyd CE Jr (2006) A study on the computerized fractal analysis of architectural distortion in screening mammograms. Phys Med Biol 51(5):1299–1312

    Article  PubMed  Google Scholar 

  11. Eltonsy N, Tourassi G, Elmaghraby A (2006) Investigating performance of a morphology-based CAD scheme in detecting architectural distortion in screening mammograms. In: Proceedings of the 20th international congress and exhibition on computer assisted radiology and surgery (CARS 2006). Springer, Osaka, pp 336–338

  12. Matsubara T, Ichikawa T, Hara T, Fujita H, Kasai S, Endo T, Iwase T (2003) Automated detection methods for architectural distortions around skinline and within mammary gland on mammograms. In: Lemke HU, Vannier MW, Inamura K, Farman AG, Doi K, Reiber JHC (eds) International congress series: proceedings of the 17th international congress and exhibition on computer assisted radiology and surgery. Elsevier, London, pp 950–955

    Google Scholar 

  13. Ichikawa T, Matsubara T, Hara T, Fujita H, Endo T, Iwase T (2004) Automated detection method for architectural distortion areas on mammograms based on morphological processing and surface analysis. In: Fitzpatrick JM, Sonka M (eds) Proceedings of SPIE medical imaging 2004: image processing. SPIE, San Diego, pp 920–925

    Google Scholar 

  14. Hara T, Makita T, Matsubara T, Fujita H, Inenaga Y, Endo T, Iwase T (2006) Automated detection for architectural distortion based on distribution assessment of mammary gland on mammogram. In: Proceedings of the 20th international congress and exhibition on computer assisted radiology and surgery (CARS 2006). Springer, Osaka, pp 333–334

  15. Ayres FJ, Rangayyan RM (2005) Characterization of architectural distortion in mammograms. IEEE Eng Med Biol Mag 24(1):59–67

    Article  PubMed  Google Scholar 

  16. Ayres FJ, Rangayyan RM (2005) Detection of architectural distortion in mammograms via analysis of phase portraits and curvilinear structures. In: Hozman J, Kneppo P (eds) IFMBE proceedings, vol. 11, ISSN 1727–1983. Proceedings of the 3rd European medical and biological engineering conference: EMBEC’05, Prague, Czech Republic, pp 1768–1773

  17. Rangayyan RM, Ayres FJ (2006) Gabor filters and phase portraits for the detection of architectural distortion in mammograms. Med Biol Eng Comput. 44:883–894 doi: 10.1007/s11517–006–0088–3

    Article  PubMed  Google Scholar 

  18. Rangayyan RM, Ayres FJ (2006) Detection of architectural distortion in mammograms using a shape-constrained phase portrait model. In: Proceedings of the 20th international congress and exhibition on computer assisted radiology and surgery (CARS 2006). Springer, Osaka, pp 334–336

  19. Rao AR, Jain RC (1992) Computerized flow field. analysis: Oriented texture fields, IEEE Trans Pattern Anal Mach Intell 14(7):693–709

    Article  Google Scholar 

  20. Ayres FJ, Rangayyan RM (2006) Optimization procedures for the estimation of phase portraits of orientation fields. In: Dougherty ER, Astola JT, Egiazarian KO, Nasrabadi NM, Rizvi SA (eds) Proceedings of SPIE electronic imaging 2006: image processing: algorithms and systems, neural networks, and machine learning, vol. 6064, San Jose, eid 606407

  21. Abadir KM, Magnus JR (2005) Matrix algebra. Cambridge University Press, New York

    Google Scholar 

  22. Suckling J, Parker J, Dance DR, Astley S, Hutt I, Boggis CRM, Ricketts I, Stamakis E, Cerneaz N, Kok S-L, Taylor P, Betal D, Savage J (1994) The Mammographic Image Analysis Society digital mammogram database. In: Gale AG, Astley SM, Dance DD, Cairns AY (eds) Digital mammography: proceedings of the 2nd international workshop on digital mammography. Elsevier, York, pp 375–378

    Google Scholar 

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Correspondence to Rangaraj M. Rangayyan.

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Ayres, F.J., Rangayyan, R.M. Reduction of false positives in the detection of architectural distortion in mammograms by using a geometrically constrained phase portrait model. Int J CARS 1, 361–369 (2007). https://doi.org/10.1007/s11548-007-0072-x

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  • DOI: https://doi.org/10.1007/s11548-007-0072-x

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