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WCE polyp detection based on novel feature descriptor with normalized variance locality-constrained linear coding.

Jianjun Yang1, Liping Chang2, Sheng Li1

  • 1College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang, People's Republic of China.

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|May 25, 2020
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Summary
This summary is machine-generated.

A new algorithm improves wireless capsule endoscopy (WCE) polyp detection using novel Histogram of Local Color Difference (LCDH) features and Normalized Variance Locality-Constrained Linear Coding (NVLLC). This method enhances diagnostic accuracy for intestinal diseases.

Keywords:
CADFeature codingLCDHNormalized variancePolyp detection

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Gastroenterology

Background:

  • Wireless capsule endoscopy (WCE) is crucial for detecting digestive tract diseases.
  • Improving the accuracy and efficiency of WCE polyp detection systems is essential for clinical practice.

Purpose of the Study:

  • To develop a novel algorithm for enhanced WCE polyp detection.
  • To improve the accuracy and efficiency of computer-aided diagnosis systems for intestinal polyps.

Main Methods:

  • Proposed a novel local color texture feature: Histogram of Local Color Difference (LCDH).
  • Introduced a codebook acquisition method based on positive samples for balanced visual words.
  • Developed the Normalized Variance Locality-Constrained Linear Coding (NVLLC) algorithm incorporating a normalized variance regular term.
  • Utilized a spatial matching pyramid model for final image representations and a support vector machine for classification.

Main Results:

  • The proposed method achieved 96.00% classification accuracy, 95.80% sensitivity, and 96.20% specificity on a WCE dataset.
  • Performance surpassed traditional methods in polyp detection.
  • The algorithm demonstrated effectiveness in distinguishing between polyp and normal intestinal images.

Conclusions:

  • A novel WCE polyp detection method combining LCDH features and NVLLC coding was successfully developed.
  • The method shows promising performance for clinical-assisted diagnosis of intestinal diseases.
  • This approach offers a significant advancement in automated polyp detection using WCE.