• DocumentCode
    1825779
  • Title

    Fractal Analysis of Tumoral Lesions in Brain

  • Author

    Martin-Landrove, M. ; Pereira, D. ; Caldeira, M.E. ; Itriago, S. ; Juliac, M.

  • Author_Institution
    Univ. Central de Venezuela, Caracas
  • fYear
    2007
  • fDate
    22-26 Aug. 2007
  • Firstpage
    1306
  • Lastpage
    1309
  • Abstract
    In this work, it is proposed a method for supervised characterization and classification of tumoral lesions in brain, based on the analysis of irregularities at the lesion contour on T2-weighted MR images. After the choice of a specific image, a segmentation procedure with a threshold selected from the histogram of intensity levels is applied to isolate the lesion, the contour is detected through the application of a gradient operator followed by a conversion to a ";time series"; using a chain code procedure. The correlation dimension is calculated and analyzed to discriminate between normal or malignant structures. The results found showed that it is possible to detect a differentiation between benign (cysts) and malignant (gliomas) lesions suggesting the potential of this method as a diagnostic tool.
  • Keywords
    brain; fractals; image segmentation; medical image processing; time series; tumours; T2-weighted MR images; brain; chain code procedure; contour detection; cysts; diagnostic tool; fractal analysis; gliomas; image segmentation; time series; tumoral lesions; Cancer; Fractals; Histograms; Image analysis; Image converters; Image segmentation; Laplace equations; Lesions; Magnetic resonance imaging; Relays; Algorithms; Brain Neoplasms; Expert Systems; Fractals; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Magnetic Resonance Imaging; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE
  • Conference_Location
    Lyon
  • ISSN
    1557-170X
  • Print_ISBN
    978-1-4244-0787-3
  • Type

    conf

  • DOI
    10.1109/IEMBS.2007.4352537
  • Filename
    4352537