Recent developments in wireless capsule endoscopy imaging: Compression and summarization techniques

Wireless capsule endoscopy (WCE) can be viewed as an innovative technology introduced in the medical domain to directly visualize the digestive system using a battery-powered electronic capsule. It is considered a desirable substitute for conventional digestive tract diagnostic methods for a comfort...

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Bibliographic Details
Published in:Computers in biology and medicine Vol. 149; p. 106087
Main Authors: B, Sushma, P, Aparna
Format: Journal Article
Language:English
Published: United States Elsevier Ltd 01.10.2022
Elsevier Limited
Subjects:
Abnormalities
Artificial neural networks
Capsule Endoscopy - methods
Complexity
Compression
Computer applications
Convolutional neural network
Data Compression - methods
Deep learning
Diagnostic systems
Digestive system
Endoscopy
Gastrointestinal Tract
Image and video compression
Image resolution
Inspection
Internal Medicine
Low complexity video coding
Medical imaging
Neural networks
Other
Power consumption
Reviews
Video compression
Video data
Video summarization
Wireless capsule endoscopy
Deep learning
Image and video compression
Video summarization
Wireless capsule endoscopy
Convolutional neural network
Low complexity video coding
ISSN:0010-4825, 1879-0534, 1879-0534
Online Access:Get full text
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Summary:Wireless capsule endoscopy (WCE) can be viewed as an innovative technology introduced in the medical domain to directly visualize the digestive system using a battery-powered electronic capsule. It is considered a desirable substitute for conventional digestive tract diagnostic methods for a comfortable and painless inspection. Despite many benefits, WCE results in poor video quality due to low frame resolution and diagnostic accuracy. Many research groups have presented diversified, low-complexity compression techniques to economize battery power consumed in the radio-frequency transmission of the captured video, which allows for capturing the images at high resolution. Many vision-based computational methods have been developed to improve the diagnostic yield. These methods include approaches for automatically detecting abnormalities and reducing the amount of time needed for video analysis. Though various research works have been put forth in the WCE imaging field, there is still a wide gap between the existing techniques and the current needs. Hence, this article systematically reviews recent WCE video compression and summarization techniques. The review’s objectives are as follows: First, to provide the details of the requirement, challenges and design percepts for the low complexity WCE video compressor. Second, to discuss the most recent compression methods, emphasizing simple distributed video coding methods. Next, to review the most recent summarization techniques and the significance of using deep neural networks. Further, this review aims to provide a quantitative analysis of the state-of-the-art methods along with their advantages and drawbacks. At last, to discuss existing problems and possible future directions for building a robust WCE imaging framework. •Describes the requirements, challenges and design principles for the low complexity WCE video compressor.•Discusses the recent WCE compression methods, emphasizing distributed video coding methods.•Provides the review of the recent WCE video summarization techniques and the significance of using deep neural networks.•Addresses current issues and prospective directions to build a robust WCE imaging framework.
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2022.106087