Multiple feature sets based categorization of laryngeal images

  • Authors:

  • A. Verikas;A. Gelzinis;D. Valincius;M. Bacauskiene;V. Uloza

  • Affiliations:

  • Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania and Intelligent Systems Laboratory, Halmstad University, Box 823, S-301 18 Halmstad, Swe ...;Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania;Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania;Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania;Department of Otolaryngology, Kaunas University of Medicine, LT-50009 Kaunas, Lithuania

  • Venue:
  • Computer Methods and Programs in Biomedicine
  • Year:
  • 2007

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Abstract

This paper is concerned with an automated analysis of laryngeal images aiming to categorize the images into three decision classes, namely healthy, nodular, and diffuse. The problem is treated as an image analysis and classification task. Aiming to obtain a comprehensive description of laryngeal images, multiple feature sets exploiting information on image colour, texture, geometry, image intensity gradient direction, and frequency content are extracted. A separate support vector machine (SVM) is used to categorize features of each type into the decision classes. The final image categorization is then obtained based on the decisions provided by a committee of support vector machines. Bearing in mind a high similarity of the decision classes, the correct classification rate of over 94% obtained when testing the system on 785 laryngeal images recorded at the Department of Otolaryngology, Kaunas University of Medicine is rather promising.