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Texture classification in lung CT using local binary patterns.

Lauge Sørensen1, Saher B Shaker, Marleen de Bruijne

  • 1Department of Computer Science, University of Copenhagen, Denmark. lauges@diku.dk

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|November 5, 2008
PubMed
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Local binary patterns (LBP) combined with intensity histograms offer a highly accurate method for classifying lung texture patterns in CT scans, outperforming existing techniques.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Radiology

Background:

  • Accurate classification of lung texture patterns in computed tomography (CT) is crucial for diagnosing conditions like emphysema.
  • Existing methods often rely on filter bank features, with varying degrees of success.

Purpose of the Study:

  • To introduce and evaluate a novel classification framework using local binary patterns (LBP) and intensity histograms for lung CT texture analysis.
  • To compare the performance of the proposed method against established techniques.

Main Methods:

  • Utilizing local binary patterns (LBP) as texture features.
  • Incorporating image intensity via a joint LBP and intensity histogram.
  • Employing a k-nearest neighbor classifier with histogram similarity as the distance metric.

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Main Results:

  • The joint LBP and intensity histogram achieved a classification accuracy of 95.2% on a dataset of 168 regions of interest.
  • The proposed method demonstrated superior performance compared to using moments of filter response histograms.
  • Performance was slightly better than using full filter response histograms and exceeded previously reported results in the literature.

Conclusions:

  • The joint LBP and intensity histogram approach provides a robust and highly accurate method for classifying lung CT texture patterns.
  • This technique shows significant potential for improving the diagnosis of lung diseases such as emphysema.