• DocumentCode
    541530
  • Title

    Automatic quantification of oedema from T2 weighted CMR image using a Hybrid Thresholding Oedema Sizing Algorithm (HTOSA)

  • Author

    Kadir, K. ; Payne, A. ; Soraghan, John J. ; Berry, C.

  • Author_Institution
    Dept of Electron. & Electr., Univ. of Strathcylde, Glasgow, UK
  • fYear
    2010
  • fDate
    26-29 Sept. 2010
  • Firstpage
    233
  • Lastpage
    236
  • Abstract
    Oedema is fluid retention within the myocardial tissue due to damage tissue causing swelling in the affected area after myocardial infarction (MI). Quantification of oedema area after an MI is an important step in medical prognosis to differentiate between viable and death myocardial tissue. In this paper a novel technique of Hybrid Thresholding Oedema Sizing Algorithm (HTOSA) is presented. To quantify the oedema a hybrid technique based on combination of morphological operation combined with statistical thresholding is used. The performance of the method was tested on real T2 weighted MRI data. The quantitative result of the automatic method compare to manual segmentation by a skill clinician is very encouraging with correlation score of 81.1%.
  • Keywords
    biological fluid dynamics; biomedical MRI; cardiology; diseases; image classification; image segmentation; medical image processing; muscle; statistical analysis; T2 weighted CMR image; T2 weighted MRI data; automatic method; automatic quantification; cardiac magnetic resonance image; fluid retention; hybrid thresholding oedema sizing algorithm; image classification; manual segmentation; medical prognosis; morphological operation; myocardial infarction; myocardial tissue; statistical thresholding; swelling; Cardiology; Correlation; Image segmentation; Magnetic resonance imaging; Manuals; Myocardium; Pixel;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computing in Cardiology, 2010
  • Conference_Location
    Belfast
  • ISSN
    0276-6547
  • Print_ISBN
    978-1-4244-7318-2
  • Type

    conf

  • Filename
    5737952