Diagnostically Useful Video Content Extraction for Integrated Computer-Aided Bronchoscopy Examination System

  • Rafał Jóźwiak
  • Artur Przelaskowski
  • Mariusz Duplaga
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 57)


The problem of diagnostically important content selection in bronchoscopy video was the subject of our research reported in this paper. The characteristic of illegible and redundant bronchoscopy video content was presented and analyzed. A method for diagnostically important frame extraction from non-informative, diagnostically useless content was proposed. Our methodology exploits region of interests segmentation, features extraction in multiresolution wavelet domain and frame classification. SVM with optimized kernels and quality criteria was applied for classification. Effectiveness of proposed method was verified experimentally on large image dataset containing about 1500 diversified video frames from different bronchoscopy examinations. Obtained results with mean sensitivity above 97% and mean specificity about 94% confirmed high effectiveness of proposed method.


Discrete Wavelet Transform Positive Frame Multiresolution Wavelet Bronchoscopy Video Important Frame 
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 2009

Authors and Affiliations

  • Rafał Jóźwiak
    • 1
  • Artur Przelaskowski
    • 1
  • Mariusz Duplaga
    • 2
  1. 1.Institute of Radioelectronics Warsaw University of TechnologyWarsawPoland
  2. 2.II Department of Internal Medicine and Department of Cardiac SurgeryJagiellonian University School of MedicineKrakowPoland

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