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Computer assisted gastric abnormalities detection using hybrid texture descriptors for chromoendoscopy images.

Hussam Ali1, Mussarat Yasmin1, Muhammad Sharif1

  • 1COMSATS Institute of Information Technology Wah, Pakistan.

Computer Methods and Programs in Biomedicine
|February 26, 2018
PubMed
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This study introduces a new Gabor-based gray-level co-occurrence matrix (G2LCM) method for automatically detecting abnormal frames in chromoendoscopy (CH) images. The system achieved high accuracy, aiding early stomach cancer diagnosis and reducing procedure time.

Keywords:
ChromoendoscopyCo-occurrence matrixFilter bankGabor filterStomach cancerTexture analysis

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Area of Science:

  • Medical imaging analysis
  • Computer vision in gastroenterology

Background:

  • Early stomach cancer detection relies on screening procedures like chromoendoscopy (CH).
  • Manual inspection of numerous endoscopic frames is time-consuming and exhaustive.
  • Automated analysis of gastroenterology images can assist endoscopists in identifying abnormalities.

Purpose of the Study:

  • To develop a computer-aided detection system for abnormal chromoendoscopy frames.
  • To introduce a novel hybrid texture extraction method for enhanced image analysis.

Main Methods:

  • A new feature extraction method, Gabor-based gray-level co-occurrence matrix (G2LCM), was developed.
  • G2LCM combines local and global texture descriptors by analyzing Gabor filter responses.
  • Second-order statistics of co-occurrence matrices represent image texture for classification.

Main Results:

  • The G2LCM method achieved high classification performance for abnormal frames.
  • Sensitivity, specificity, and accuracy were 91%, 82%, and 87% respectively.
  • Area under the curve (AUC) was 0.91, indicating strong discriminatory power.

Conclusions:

  • The proposed system effectively aids gastroenterologists in gastric tract screening.
  • Automated analysis using G2LCM can significantly reduce endoscopic procedure times.
  • This technology supports earlier and more efficient detection of potential abnormalities.