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A New Collection of Preprocessed Digital Mammograms

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Advances in Artificial Intelligence and Its Applications (MICAI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8265))

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Abstract

We contribute with a publicly available repository of digital mammograms including both raw and preprocessed images. The study of mammographies is the most used and effective method to diagnose breast cancer. It is possible to improve quality of images for more accurate predictions of radiologists, by applying some preprocessing techniques. In this work we introduce a method for mammogram preprocessing. Our method includes the following processes: reduction of the work area, bit conversion, denoising using the adaptive median filter, contrast enhancement using histogram equalization, and image compression using histogram shrinking. Practical experiments were conducted on raw images in DICOM format from the Mammography Clinic at Hospital de Alta Especialidad Juan Graham Casasús located in Tabasco, Mexico. Results were evaluated by medical doctors.

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References

  1. Boyle, P., Levin, B., et al.: World Cancer Report 2008. IARC Press, International Agency for Research on Cancer (2008)

    Google Scholar 

  2. Nacional de Estadística y Geografía (2008), http://www.inegi.org.mx

  3. Ponraj, D.N., Jenifer, M.E., Poongodi, D.P., Manoharan, J.S.: A survey on the preprocessing techniques of mammogram for the detection of breast cancer. Journal of Emerging Trends in Computing and Information Sciences 2(12) (2011)

    Google Scholar 

  4. American College of Radiology, National Electrical Manufacturers Association, Digital Imaging and Communications in Medicine, http://medical.nema.org/standard.html (visited in August 2013)

  5. Suckling: The mini-mias database of mammograms. In: Excerpta Medica. International Congress Series, vol. 1069, pp. 375–378 (1994)

    Google Scholar 

  6. Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, P.: The digital database for screening mammography. In: Proceedings of the 5th International Workshop on Digital Mammography, pp. 212–218 (2000)

    Google Scholar 

  7. Lauria, A., Massafra, R., Tangaro, S.S., Bellotti, R., Fantacci, M., Delogu, P., Torres, E.L., Cerello, P., Fauci, F., Magro, R., Bottigli, U.: GPCALMA: an Italian mammographic database of digitized images for research. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds.) IWDM 2006. LNCS, vol. 4046, pp. 384–391. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  8. Oliveira, J.E.E., Gueld, M.O., de A. Araúio, A., Ott, B., Deserno, T.M.: Toward a standard reference database for computer-aided mammography. In: vol. 6915, pp. 69151Y–69151Y-9 (2008)

    Google Scholar 

  9. Antoniou, Z.C., Giannakopoulou, G.P., Andreadis, I.I., Nikita, K.S., Ligomenides, P.A., Spyrou, G.M.: A web-accessible mammographic image database dedicated to combined training and evaluation of radiologists and machines. In: 9th International Conference on Information Technology and Applications in Biomedicine, ITAB 2009, pp. 1–4. IEEE (2009)

    Google Scholar 

  10. Matheus, B.R.N., Schiabel, H.: Online mammographic images database for development and comparison of CAD schemes. Journal of Digital Imaging 24(3), 500–506 (2011)

    Article  Google Scholar 

  11. Fernandes, F., Bonifácio, R., Brasil, L., Guadagnin, R., Lamas, J.: Midas–mammographic image database for automated analysis. In: Mammography-Recent Advances, pp. 243–260. InTechOpen (2012)

    Google Scholar 

  12. Moreira, I.C., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M.J., Cardoso, J.S.: Inbreast: toward a full-field digital mammographic database. Academic Radiology 19(2), 236–248 (2012)

    Article  Google Scholar 

  13. Dehghani, S., Dezfooli, M.A.: A method for improve preprocessing images mammography. International Journal of Information and Education Technology 1(1) (2011)

    Google Scholar 

  14. Holguín, L.G.A., Álvarez, D., Guevara, M.L.: Pre-procesamiento de imágenes aplicadas a mamografías digitales. Scientia Et Technica (2006)

    Google Scholar 

  15. Otsu, N.: A threshold selection method from gray-level histograms. Automatica 11(285-296), 23–27 (1975)

    Google Scholar 

  16. Gonzalez, R.C.: Digital image processing using MATLAB, vol. 2. Pearson (2009)

    Google Scholar 

  17. Pianykh, O.S.: Digital Imaging and Communications in Medicine (DICOM): A practical introduction and survival guide. Springer (2011)

    Google Scholar 

  18. Mustra, M., Grgic, M., Delac, K.: Efficient presentation of DICOM mammography images using Matlab. In: 15th International Conference on Systems, Signals and Image Processing, IWSSIP 2008, pp. 13–16. IEEE (2008)

    Google Scholar 

  19. Motwani, M.C., Gadiya, M.C., Motwani, R.C., Harris Jr, F.C.: Survey of image denoising techniques. In: Proceedings of GSPX, Citeseer, pp. 27–30 (2004)

    Google Scholar 

  20. Mohan, S., Ravishankar, M.: Modified contrast limited adaptive histogram equalization based on local contrast enhancement for mammogram images. In: Das, V.V., Chaba, Y. (eds.) AIM/CCPE 2012. CCIS, vol. 296, pp. 397–403. Springer, Heidelberg (2013)

    Google Scholar 

  21. Pisano, E.D., Cole, E.B., Hemminger, B.M., Yaffe, M.J., Aylward, S.R., Maidment, A.D., Johnston, R.E., Williams, M.B., Niklason, L.T., Conant, E.F., et al.: Image processing algorithms for digital mammography: A pictorial essay. Radiographics 20(5), 1479–1491 (2000)

    Article  Google Scholar 

  22. Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Graphics gems IV, pp. 474–485. Academic Press Professional, Inc. (1994)

    Google Scholar 

  23. AbuBaker, A.A., Qahwaji, R.S., Aqel, M.J., Saleh, M.H.: Mammogram image size reduction using 16-8 bit conversion technique. International Journal of Biological and Medical Sciences 2, 103–110 (2006)

    Google Scholar 

  24. AbuBaker, A.A., Qahwaji, R., Aqel, M.J., Al-Osta, H., Saleh, M.H.: Efficient pre-processing of USF and MIAS mammogram images. Journal of Computer Science 3(2), 67–75 (2007)

    Article  Google Scholar 

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Authors and Affiliations

  1. División Académica de Informática y Sistemas (DAIS), Universidad Juárez Autónoma de Tabasco (UJAT), Cunduacán, Tabasco, Mexico

    Juana Canul-Reich & Omar Trinidad Gutiérrez Méndez

Authors
  1. Juana Canul-Reich
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  2. Omar Trinidad Gutiérrez Méndez
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Editor information

Editors and Affiliations

  1. Universidad Autónoma del Estado de Hidalgo, Ciudad Universitaria,, Carretera Pachuca–Tulancingo km 4.5, Hidalgo, Mexico

    Félix Castro

  2. Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan Dios Bátiz s/n, Col. Nueva Industrial Vallejo, 07738, Mexico City, Mexico

    Alexander Gelbukh

  3. Tecnológico de Monterrey, Campus Estado de México,, Carretera Lago de Guadalupe Km 3.5, Atizapán de Zaragoza,, CP 52926, Estado de México, Mexico

    Miguel González

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Canul-Reich, J., Gutiérrez Méndez, O.T. (2013). A New Collection of Preprocessed Digital Mammograms. In: Castro, F., Gelbukh, A., González, M. (eds) Advances in Artificial Intelligence and Its Applications. MICAI 2013. Lecture Notes in Computer Science(), vol 8265. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45114-0_45

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  • DOI: https://doi.org/10.1007/978-3-642-45114-0_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-45113-3

  • Online ISBN: 978-3-642-45114-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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