ChestXthon: An algorithm for Abnormality Detection in Chest Radiographs

  • Authors

    • N. Sarada Research Scholar, KL University
    • K. Thirupathi Rao
    • K. V. Ramana
    2018-09-24
    https://doi.org/10.14419/ijet.v7i4.1.14216
  • CAD, Chest illness, CXR, Therapeutic Specialist
  • Chest illnesses like heart failure, lung tumor or lung tuberculosis, and so on is frequently in view of chest X-ray images (CXR). The ailments are treatable on the off chance that they are recognized in their beginning times. Analyzing CXR is a tedious procedure. Now and again, therapeutic specialists had ignored the illnesses in their first examinations on CXR, and when the pictures were reevaluated, the malady signs could be detected. Furthermore, the quantity of CXR to look at is various and a long ways past the capacity of accessible therapeutic staff, particularly in creating nations. A PC supported finding (CAD) framework can check presumed zones on CXR for cautious examination by restorative specialists, and can give caution in the cases that need critical consideration. This paper reports our persistent work on developing an algorithm that aids the radiologists for the diagnosis of chest radiographs.

     

     

  • References

    1. [1] Al-Absi HRH, Samir BB, Shaban KB, Sulaiman S. Computer aided diagnosis system based on machine learning techniques for lung cancer. In: ICCIS 2012: Proceedings of the 2012 International Conference on Computer Information Science; 2012 June 12-14; Kuala Lumpur, Malaysia. IEEE; 2012. p. 295-300. v. 1.

      [2] Computer-Aided Diagnosis in Chest Radiography: A Survey Bram van Ginneken*, Bart M. terHaarRomeny, and Max A. Viergever, Member IEEE.

      [3] R. Daffner, Clinical Radiology, the Essentials, 2nd ed. Baltimore, MD: Williams & Wilkins, 1999.

      [4] P. Jannin, J.M. Fitzpatrick, D.J. Hawkes, X. Pennec, R. Shahidi, and M.W. Vannier. Validation of medical image processing in image-guided therapy. IEEE Transactions on Medical Imaging, 21(12):1455–1449, 2002. https://doi.org/10.1109/TMI.2002.806568.

      [5] K.W. Bowyer, M.H. Loew, H.S. Stiehl, and M.A. Viergever. Methodology of evaluation in medical image computing. In Rep. Dagstuhl Workshop, 2001.

      [6] M. B. Stegmann, B. K. Ersbøll, and R. Larsen. FAME – a flexible appearance-modelling environment. IEEE Transactions on Medical Imaging, 22(10):1319–1331, 2003. https://doi.org/10.1109/TMI.2003.817780.

      [7] L. Li, Y. Zheng, M. Kallergi, and R.A. Clark. Improved method for automatic identification of lung regions on chest radiographs. Academic Radiology, 8(7):629–638, 2001. https://doi.org/10.1016/S1076-6332(03)80688-8.

      B. van Ginneken, S. Katsuragawa, B. M. terHaarRomeny, K. Doi, and M. A. Viergever. Automatic detection of abnormalities in chest radiographs using local texture analysis. IEEE Transactions on Medical Imaging, 21(2):139–149, 2002. https://doi.org/10.1109/42.993132
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  • How to Cite

    Sarada, N., Thirupathi Rao, K., & V. Ramana, K. (2018). ChestXthon: An algorithm for Abnormality Detection in Chest Radiographs. International Journal of Engineering & Technology, 7(4), 2528-2532. https://doi.org/10.14419/ijet.v7i4.1.14216