Journal of Digital Imaging

, Volume 21, Issue 2, pp 177–187 | Cite as

An Automatic Correction Method for the Heel Effect in Digitized Mammography Images

  • Marcelo Zanchetta do Nascimento
  • Annie France Frère
  • Fernao Germano


The most significant radiation field nonuniformity is the well-known Heel effect. This nonuniform beam effect has a negative influence on the results of computer-aided diagnosis of mammograms, which is frequently used for early cancer detection. This paper presents a method to correct all pixels in the mammography image according to the excess or lack on radiation to which these have been submitted as a result of the this effect. The current simulation method calculates the intensities at all points of the image plane. In the simulated image, the percentage of radiation received by all the points takes the center of the field as reference. In the digitized mammography, the percentages of the optical density of all the pixels of the analyzed image are also calculated. The Heel effect causes a Gaussian distribution around the anode–cathode axis and a logarithmic distribution parallel to this axis. Those characteristic distributions are used to determine the center of the radiation field as well as the cathode–anode axis, allowing for the automatic determination of the correlation between these two sets of data. The measurements obtained with our proposed method differs on average by 2.49 mm in the direction perpendicular to the anode–cathode axis and 2.02 mm parallel to the anode–cathode axis of commercial equipment. The method eliminates around 94% of the Heel effect in the radiological image and the objects will reflect their x-ray absorption. To evaluate this method, experimental data was taken from known objects, but could also be done with clinical and digital images.

Key words

Heel effect images processing computational simulation x-rays mammography digitized film 



The authors wish to express their appreciation for the financial support of FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo).


  1. 1.
    Rezentes PS, de Almeida A, Barnes GT: Mammography grid performance. Radiology 210:227–232, 1999PubMedGoogle Scholar
  2. 2.
    Kimme-Smith C, Sayre J, McCombs M, Gold RH, Basset LW: Mammography fixed grid versus reciprocating grid: evaluation using cadaveric breasts as test objects. Med Phys 23:141–147, 1996PubMedCrossRefGoogle Scholar
  3. 3.
    Seibert JA, Boone JM: X-ray scatter removal by deconvolution. Med Phys 15:567–575, 1988PubMedCrossRefGoogle Scholar
  4. 4.
    Highnam R, Brady M, English R: Detecting film-screen artifacts in mammography using a model-based approach. IEEE Trans Med Imag 18:1016–1024, 1999CrossRefGoogle Scholar
  5. 5.
    Terry JA, Waggner RG, Blough MA: Half-value and intensity variations as a function of position in the radiation field for film-screen mammography. Med Phys 26:259–266, 1999PubMedCrossRefGoogle Scholar
  6. 6.
    Behiels G, Maes F, Vandermeulen D, Suetens P: Retrospective correction of the heel effect in hand radiographs. Med Image Anal 6:183–190, 2002PubMedCrossRefGoogle Scholar
  7. 7.
    Fung KKL, Gilboy WB: “Anode heel effect” on patient dose in lumbar spine radiography. Br J Radiol 73:531–536, 2000PubMedGoogle Scholar
  8. 8.
    Rangayyan RM, Ayres FJ, Desautels JEL: A review of computer-aided diagnosis of breast cancer: toward the detection of subtle signs. J Franklin Inst 344:312–348, 2007CrossRefGoogle Scholar
  9. 9.
    Ferrari RJ, Rangayyan RM, Desautels JEL, Borges RA, Frere AF: Identification of the breast boundary in mammograms using active contour models. Med Biol Eng Comput 42:201–208, 2004PubMedCrossRefGoogle Scholar
  10. 10.
    Nakayama R, Watanabe R, Namba K, Takeda K, Yamamoto K, Katsuragawa S, Doi K: Computer-aided diagnosis scheme for identifying histological classification of clustered microcalcifications by use of follow-up magnification mammograms. Acad Radiol 13:1219–1228, 2006PubMedCrossRefGoogle Scholar
  11. 11.
    Cowen AR, Brettle DS, Workman A: Technical note: Compensation for field non-uniformity on a mammographic X-ray unit. Br J Radiol 66:150–154, 1993PubMedCrossRefGoogle Scholar
  12. 12.
    Highnam R, Brady M, Shepstone B: A representation for mammographic image processing. Med Image Anal 1:1–18, 1996PubMedCrossRefGoogle Scholar
  13. 13.
    Pawluczyk O, Yaffe MJ: Field nonuniformity correction for quantitative analysis of digitized mammograms. Med Phys 28:438–444, 2001PubMedCrossRefGoogle Scholar
  14. 14.
    Meeson S, Young KC, Rust A, Wallis MG, Cooke J, Ramsdale ML: Implications of using high contrast mammography X-ray film-screen combinations. Br J Radiol 74:825–835, 2001PubMedGoogle Scholar
  15. 15.
    Marques MA, Frère AF, Oliveira HJQ, Azevedo-Marques PM, Schiabel H: Computerized method for radiologic systems parameters simulation intended to quality assurance programs. Med Biol Eng Comput 37:1244–1245, 1999Google Scholar
  16. 16.
    Fritz SL, Livingston WH: A comparison of computed and measured heel effect for various target angles. Med Phys 9:216–219, 1982PubMedCrossRefGoogle Scholar
  17. 17.
    Brice DK: Stopping Powers for Electrons and Positrons, ICRU Report 37. Bethesda, MD: International Commission On Radiation Units And Measurements, 1984Google Scholar
  18. 18.
    Silva MA, Frère AF, Marques MA, Mattos LS: Heel effect’s influence on the performance of screen-film combinations. Med Biol Eng Comput 37:1258–1259, 1999Google Scholar
  19. 19.
    Boone JM, Chavez AE: Comparison of x-ray sections for diagnostic and therapeutic medical physics. Med Phys 23:1997–2005, 1996PubMedCrossRefGoogle Scholar
  20. 20.
    Gonzalez RC, Woods RE: Digital Image Processing, 2nd edition. Englewood Cliffs, NJ: Prentice Hall, 2002Google Scholar
  21. 21.
    Press WH, Teukolsky SA, Vetterling WT, Flannery BP: Numerical Recipes in C. The Art of Scientific Computing, 2nd edition. Cambridge: Cambridge University Press, 1992Google Scholar
  22. 22.
    Lengyel E: Mathematics for 3D Games Programming and Computer Graphics, 2nd edition. Devon, UK: Charles River Media, 2003Google Scholar
  23. 23.
    Pratt WK: Correlation techniques of image registration. IEEE Trans Aerosp Electron Syst 10:353–358, 1974CrossRefGoogle Scholar
  24. 24.
    Dekker N, Ploeger LS, Herk MV: Evaluation of cost functions for gray value matching of two-dimensional image in radiotherapy. Med Phys 30:778–784, 2003PubMedCrossRefGoogle Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2007

Authors and Affiliations

  • Marcelo Zanchetta do Nascimento
    • 1
    • 2
  • Annie France Frère
    • 2
    • 3
  • Fernao Germano
    • 4
  1. 1.Centro de Matemática, Computação e CogniçãoUniversidade Federal do ABCSanto AndréBrazil
  2. 2.Departamento de Engenharia Elétrica, Escola de Engenharia de São CarlosUniversidade de São PauloSão CarlosBrazil
  3. 3.Centro de Pesquisas TecnológicasUniversidade de Mogi das CruzesMogi das CruzesBrazil
  4. 4.Instituto de Ciências Matemática e de ComputaçãoUniversidade de São PauloSão CarlosBrazil

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