Classification of Endoscopic Image Based on Texture and Neural Network

Abstract

Computerized processing of medical images can ease the search of the representative features in the images. The endoscopic images possess rich information expressed by texture. Regions affected by diseases, such as ulcer or coli, may have different texture features. The texture model implemented in this study is Local Binary Pattern (LBP) and a log-likelihood ratio, called the G-statistic, is used to evaluate the similarity of regions based on LBP.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA409511

Entities

People

  • C. Kugean
  • M. P. Tjoa
  • Pingshan Wang
  • S. M. Krishnan

Organizations

  • Nanyang Technological University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Biomedical Engineering
  • Blood Vessels
  • Classification
  • Computations
  • Computer Vision
  • Control Systems
  • Engineering
  • Histograms
  • Image Classification
  • Intensity
  • Military Research
  • Neural Networks
  • Pattern Recognition
  • Recognition
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Environmental Engineering.
  • Software Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference