Medical Images Analysis: An Application of Artificial Neural Networks in the Diagnosis of Human Tissues

  • Elias Restum Antonio
  • Luiz Biondi Neto
  • Vincenzo Junior
  • Fernando Hideo Fukuda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2085)


This article presents an Artificial Neural Networks (ANN) application in the image diagnosis process, by the tissues densities obtained in Computerized Tomography (CT) exams and related to Cerebral Vascular Accidents (CVAs). Among the usually analyzed aspects are the density, the form, the size and the location of these characteristic aspects of the image. As said by specialists in this area, the most relevant attribute is the analysis of the tissues densities. Considering this fact, our paper will investigate neurological pathologies in Computerized Tomography based in the tissues densities of the tomographic images.

The images to be diagnosed are digitalized, and then pre-processed, receiving an adequate mathematical treatment to be used as ANN training patterns and tests.


Diagnosis of CVA Diagnosis for Image Automatic Diagnosis of CVAs and ANNs 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Bibliographical References

  1. [1]
    Simon Haykin, Neural Networks, a comprehensive foundation, Prentice-Hall, EUA, 1994zbMATHGoogle Scholar
  2. [2]
    David E. Rumelhart, James L. McClelland and The PDP Research Group, Parallel Distributed Processing, Volume I, The MIT Press, 1986Google Scholar
  3. [3]
    GE Medical Systems, The Physics of Computerized Tomography, General Electric Company, EUA, 1991Google Scholar
  4. [4]
    GE Medical Systems, Sytec 3000 Introduction & Prerequisite Study Package, General Electric Company, EUA, 1992Google Scholar
  5. [5]
    Anne G. Osborn, Diagnostic Neuroradiology, Mosby-Yearbook, Inc., EUA, 1994Google Scholar
  6. [6]
    Krupp, Marcus Abraham, Current Diagnosis & Treatment, Lange Medical Publications, USA, 1982Google Scholar
  7. [8]
    Richard Arnold Johnson, Applied Multivariate Statistical Analysis, Prentice-Hall, EUA, 1998Google Scholar
  8. [9]
    Betty Trujillo Montoya, Atlas Básico de Tomografia Axial Computadorizada, Quimica Schering Colombiana, Colombia, 1990Google Scholar
  9. [10]
    Biondi L.N., Estellita Lins Et Al, Neuro-DEA: Novo Paradigma para Determinação da Eficiência Relativa de Unidades Tomadoras de Decisão, 90 Congresso da Associação Portuguesa de Investigação Operacional-APDIO, no 9, pp. 114, 2000Google Scholar
  10. [11]
    The Value and Importance of an Imaging Standard, Radiological Society of North America, Department of Informatics, EUA, 1997Google Scholar
  11. [12]
    Cichocki A. and Bargiela A., “Neural Networks for Solving Linear Inequality Systems”, Journal of Parallel Computing, http://, 1996
  12. [13]
    David, M. Skapura, Building Neural Networks, Addison-Wesley Publishing Company, New York, 1996Google Scholar
  13. [14]
    Jacek M. Zurada, Introduction to Artificial Neural Systems, West Publishing Company, New York, 1992Google Scholar
  14. [15]
    Rosenblatt F., Principles of Neurodynamics, Spartan Editions, New York, 1962Google Scholar
  15. [16]
    Rafael C. Gonzales and Paul Wintz, Digital Image Processing, Addison-Wesley Publishing Company, New York,1987.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Elias Restum Antonio
    • 1
  • Luiz Biondi Neto
    • 2
  • Vincenzo Junior
    • 1
  • Fernando Hideo Fukuda
    • 1
  1. 1.Departamento de Ciências Exatas e TecnologiaUniversidade Veiga de AlmeidaPortugal
  2. 2.Departamento de Eletrônica e TelecomunicaçõesUERJPortugal

Personalised recommendations