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Systematic Construction of Texture Features for Hashimoto’s Lymphocytic Thyroiditis Recognition from Sonographic Images

  • Radim Šára
  • Daniel Smutek
  • Petr Sucharda
  • Štěpán Svačina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2101)

Abstract

The success of discrimination between normal and inflamed parenchyma of thyroid gland by means of automatic texture analysis is largely determined by selecting descriptive yet simple and independent sonographic image features.We replace the standard non-systematic process of feature selection by systematic feature construction based on the search for the separation distances among a clique of n pixels that minimise conditional entropy of class label given all data. The procedure is fairly general and does not require any assumptions about the form of the class probability density function. We show that a network of weak Bayes classifiers using 4-cliques as features and combined by majority vote achieves diagnosis recognition accuracy of 92%, as evaluated on a set of 741 B-mode sonographic images from 39 subjects. The results sug- gest the possibility to use this method in clinical diagnostic process.

Keywords

Thyroid Gland Texture Feature Class Label Sampling System Conditional Entropy 
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 2001

Authors and Affiliations

  • Radim Šára
    • 1
  • Daniel Smutek
    • 2
  • Petr Sucharda
    • 2
  • Štěpán Svačina
    • 2
  1. 1.Center for Machine PerceptionCzech Technical UniversityPragueCzech Republic
  2. 2.1st Medical FacultyCharles University PragueCzech Republic

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