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MIRACLE at ImageCLEFannot 2008: Nearest Neighbour Classification of Image Feature Vectors for Medical Image Annotation

  • Sara Lana-Serrano
  • Julio Villena-Román
  • José Carlos González-Cristóbal
  • José Miguel Goñi-Menoyo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5706)

Abstract

This paper describes the participation of MIRACLE research consortium at the ImageCLEF Medical Image Annotation task of ImageCLEF 2008. During the last year, our own image analysis system was developed, based on MATLAB. This system extracts a variety of global and local features including histogram, image statistics, Gabor features, fractal dimension, DCT and DWT coefficients, Tamura features and co-occurrence matrix statistics. A classifier based on the k-Nearest Neighbour algorithm is trained on the extracted image feature vectors to determine the IRMA code associated to a given image. The focus of our participation was mainly to test and evaluate this system in-depth and to compare among diverse configuration parameters such as number of images for the relevance feedback to use in the classification module.

Keywords

Information Retrieval medical image image annotation classification IRMA code axis learning algorithms nearest-neighbour machine learning ImageCLEF Medical Automatic Image Annotation task CLEF 2008 

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References

  1. 1.
    Lana-Serrano, S., Villena-Román, J., González-Cristóbal, J.C., Goñi-Menoyo, J.M.: MIRACLE at ImageCLEFannot 2008: Classification of Image Features for Medical Image Annotation. In: Working Notes of the 2008 CLEF Workshop, Aarhus, Denmark (2008)Google Scholar
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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Sara Lana-Serrano
    • 1
    • 3
  • Julio Villena-Román
    • 2
    • 3
  • José Carlos González-Cristóbal
    • 1
    • 3
  • José Miguel Goñi-Menoyo
    • 1
  1. 1.Universidad Politécnica de MadridSpain
  2. 2.Universidad Carlos III de MadridSpain
  3. 3.DAEDALUS - Data, Decisions and Language, S.A.Spain

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