Diagnosis in Sonogram of Gall Bladder

  • Saad Tanveer
  • Omer Jamshaid
  • Abdul Mannan
  • Muhammad Aslam
  • Ana Maria Martinez-Enriquez
  • Afraz Zahra Syed
  • Gonzalo Escalada-Imaz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7094)


This paper describes the development and testing of a diagnostic system using sonograms. In far flung areas of under developing countries where the availability of specialists is a problem and sometimes not even a possibility, it is highly beneficial to have on site diagnosis computer application to support medical staff. Diagnose of sonograms to identify infected part in offline scenarios is not always easy. Besides, lack of infrastructure does not permit online solutions to be a practical option. We implement a system named Intelligent-Eye (I-Eye) which employs imaging and diagnostic techniques to support in the gallbladder diagnosis, saving time and cost of operating medical procedures. We implemented an algorithm highly capable of being used on different diagnostic ultrasonography machines, generating accurate information reports and diagnosis.


Disease diagnosis image processing Gall Bladder 


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  1. 1.
    Slawomir, B.: Automated Detecting Symptoms of Selected Gallbladder Illness based on Static Ultrasound Image Analysis. In: Bio-algorithms and Med-systems, pp. 35–44 (2006)Google Scholar
  2. 2.
    Mazumdar, B., Mediratta, A., Bhattacharyya, J., Banerjee, S.: A Real Time Speckle Noise Cleaning Filter for Ultrasound Images. In: 19th Int. Symp. on Comp. based Med. Systems, pp. 341–346 (2006)Google Scholar
  3. 3.
    Juang, P.-A., Wu, M.-N.: Ultrasound Speckle Image Process Using Wiener Pseudo-inverse Filtering. In: 23rd IEEE Conf. on Industrial Electronics Society, pp. 2446–2449 (2007)Google Scholar
  4. 4.
  5. 5.
  6. 6.
  7. 7.
  8. 8.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice Hall (2001)Google Scholar
  9. 9.
    Singh, E.J., Kaur, N.: Automated segmentation of gallstones in ultrasound images. In: IEEE Int. Conference on Systems, Man, and Cybernetics, vol. 4, p. 2855 (October 1996)Google Scholar
  10. 10.
    Semmlow, J.L.: Biosignal and Biomedical Image Processing MATLAB(2004)Google Scholar
  11. 11.
    York, G., Kim, Y.: Ultrasound Processing and Computing: Review and Future Directions. Annual Review of Biomedical Engg. 1, 559–588 (1999)CrossRefGoogle Scholar
  12. 12.
  13. 13.
  14. 14.
    Meilstrup, J.W.: Imaging Atlas of the Normal Gallbladder and Its Variants, p. 4. CRC Press, Boca Raton (1994)Google Scholar
  15. 15.
    Myers, R.P., Shaffer, E.A., Beck, P.L.: Gallbladder polyps: epidemiology, natural history and management. Can J. Gastroenterol. 16(3), 187–194 (2002); PMID 11930198Google Scholar
  16. 16.
    Lim, J.S.: Two-Dimensional Signal and Image Processing, p. 548. Prentice Hall, Englewood Cliffs (1990); equations 9.44 – 9.46Google Scholar
  17. 17.

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Saad Tanveer
    • 1
  • Omer Jamshaid
    • 2
  • Abdul Mannan
    • 2
  • Muhammad Aslam
    • 3
  • Ana Maria Martinez-Enriquez
    • 4
  • Afraz Zahra Syed
    • 3
  • Gonzalo Escalada-Imaz
    • 5
  1. 1.School of CSGIFT UniversityGujranwalaPakistan
  2. 2.Department of CENFC I. E. T.MultanPakistan
  3. 3.Department of CS & EUETLahorePakistan
  4. 4.Departmeent of CSCINVESTAV-IPNMexico
  5. 5.Artificial Intelligence Research InstituteIIIA-CSICSpain

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