Diagnosis of Lung Nodule Using the Semivariogram Function

  • Aristófanes C. Silva
  • Perfilino Eugênio F. Junior
  • Paulo Cezar P. Carvalho
  • Marcelo Gattass
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)


This paper proposes using the semivariogram function, to help characterize lung nodules as malignant or benign in computerized tomography images.

The tests described in this paper were carried out using a sample of 36 nodules, 29 benign and 7 malignant. Fisher’s Linear Discriminant Analysis (FLDA), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) were performed to evaluate the ability of these features to predict the classification for each nodule. A leave-one-out procedure was performed to provide a less biased estimate of the classifiers performance. All analyzed classifers have value area under ROC curve above 0.9, which means that the results have excellent accuracy. The preliminary results of this approach are very promising in characterizing nodules using semivariogram function.


Support Vector Machine Hide Layer Receiver Operating Characteristic Curve Lung Nodule Malignant Nodule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Aristófanes C. Silva
    • 1
  • Perfilino Eugênio F. Junior
    • 2
  • Paulo Cezar P. Carvalho
    • 2
  • Marcelo Gattass
    • 1
  1. 1.Pontifical Catholic University of Rio de Janeiro- PUC-RioRio de JaneiroBrazil
  2. 2.Institute of Pure and Applied Mathematics - IMPARio de JaneiroBrazil

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