• DocumentCode
    1370586
  • Title

    Morphological feature extraction for the classification of digital images of cancerous tissues

  • Author

    Thiran, Jean Philippe ; Macq, Benoiît

  • Author_Institution
    Lab. de Telecommun. et Teledetection, Univ. Catholique de Louvain, Belgium
  • Volume
    43
  • Issue
    10
  • fYear
    1996
  • Firstpage
    1011
  • Lastpage
    1020
  • Abstract
    Presents a new method for automatic recognition of cancerous tissues from an image of a microscopic section. Based on the shape and the size analysis of the observed cells, this method provides the physician with nonsubjective numerical values for four criteria of malignancy. This automatic approach is based on mathematical morphology, and more specifically on the use of geodesy. This technique is used first to remove the background noise from the image and then to operate a segmentation of the nuclei of the cells and an analysis of their shape, their size and their texture. From the values of the extracted criteria, an automatic classification of the image (cancerous or not) is finally operated.
  • Keywords
    cellular biophysics; feature extraction; image classification; image segmentation; image texture; medical image processing; optical microscopy; automatic classification; background noise removal; cancerous tissues; cell nuclei segmentation; cell size analysis; digital images classification; geodesy; malignancy; mathematical morphology; medical diagnostic imaging; microscopic section; morphological feature extraction; nonsubjective numerical values; Background noise; Digital images; Feature extraction; Geodesy; Image analysis; Image recognition; Image segmentation; Microscopy; Morphology; Shape; Biopsy; Cell Nucleus; Cytoplasm; Diagnosis, Computer-Assisted; Digestive System Neoplasms; Humans; Lung Neoplasms; Microscopy; Pattern Recognition, Automated; Reproducibility of Results;
  • fLanguage
    English
  • Journal_Title
    Biomedical Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9294
  • Type

    jour

  • DOI
    10.1109/10.536902
  • Filename
    536902