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Published on: November 30, 2022
Reduction of capsule endoscopy reading times by unsupervised image mining
D K Iakovidis1, S Tsevas, A Polydorou
1Department of Informatics and Computer Technology, Technological Educational Institute of Lamia, Greece. dimitris.iakovidis@ieee.org
Summary
This study introduces a new method to reduce wireless capsule endoscopy (WCE) reading time by automatically extracting key video frames. This technique significantly cuts examination time by up to 85% without missing any abnormalities.
Area of Science:
- Medical Imaging
- Gastroenterology
- Computer Vision
Background:
- Wireless capsule endoscopy (WCE) is a non-invasive technique for small intestine imaging.
- Current WCE video analysis is time-consuming, averaging 45-120 minutes per 8-hour examination.
- Efficient review of WCE data is crucial for timely diagnosis.
Purpose of the Study:
- To develop a novel approach for reducing WCE video reading time.
- To introduce an unsupervised data reduction algorithm for WCE frame extraction.
- To create a generalizable tool for WCE video summarization and bookmarking.
Main Methods:
- A novel data reduction algorithm was applied to extract representative video frames from full-length WCE videos.
- The method utilizes an unsupervised mining scheme for frame selection.
- A tunable parameter controls the number of extracted frames, allowing for automated adjustment.
Main Results:
- The proposed methodology significantly reduces WCE reading times.
- Experiments on real WCE videos demonstrated a reduction of up to 85% in reading time.
- Abnormality detection rates were maintained, with no loss of diagnostic information.
Conclusions:
- The developed approach offers a practical solution for efficient WCE data analysis.
- This method can serve as a valuable tool for video summarization and bookmarking in capsule endoscopy.
- The unsupervised and generalizable nature of the algorithm enhances its clinical applicability.
