• DocumentCode
    320156
  • Title

    Lung nodule detection in curvature space with multilayer perceptron network

  • Author

    Penedo, M.G. ; Mosquera, A. ; Carreira, M.J. ; Cabello, D.

  • Author_Institution
    Dept. Computacion, Univ. de la Coruna, Spain
  • Volume
    3
  • fYear
    1996
  • fDate
    31 Oct-3 Nov 1996
  • Firstpage
    1130
  • Abstract
    Automatic methods developed for detection of lung nodules in chest radiographs usually present an excessive number of false positive detections. In this work the authors show how the local curvature image of suspected nodule pixels provides a new description that permits to distinguish true nodules from those false positives. A multilayer perceptron network with supervised learning is able to recognize the images of nodule local curvature peaks. The results obtained with a set of 23 chest images each one with at least one nodule show a sensibility in the global detection process of 93% with a mean number of 2 false positives per image
  • Keywords
    diagnostic radiography; lung; medical image processing; multilayer perceptrons; chest images; chest radiographs; curvature space; false positive detections; global detection process sensibility; lung nodule detection; medical diagnostic imaging; multilayer perceptron network; suspected nodule pixels; Image recognition; Intelligent networks; Lungs; Multilayer perceptrons; Nonhomogeneous media; Pixel; Proposals; Radiography; Supervised learning; Surface topography;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE
  • Conference_Location
    Amsterdam
  • Print_ISBN
    0-7803-3811-1
  • Type

    conf

  • DOI
    10.1109/IEMBS.1996.652741
  • Filename
    652741