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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| Published in: | Computers in biology and medicine Vol. 149; p. 106087 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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United States
Elsevier Ltd
01.10.2022
Elsevier Limited |
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| ISSN: | 0010-4825, 1879-0534, 1879-0534 |
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| Abstract | 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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| AbstractList | 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. 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.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. 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. AbstractWireless 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. |
| ArticleNumber | 106087 |
| Author | B., Sushma P., Aparna |
| Author_xml | – sequence: 1 givenname: Sushma surname: B fullname: B, Sushma email: sushma.bg@gmail.com organization: Image Processing and Analysis Lab (iPAL), Department of Electronics and Communication Engineering, National Institute of Technology Karnataka-Surathkal, Mangalore 575025, Karnataka, India; Department of Electronics and Communication Engineering, CMR Institute of Technology, Bengaluru 560037, Karnataka, India. Electronic address: sushma.bg@gmail.com – sequence: 2 givenname: Aparna surname: P fullname: P, Aparna organization: Image Processing and Analysis Lab (iPAL), Department of Electronics and Communication Engineering, National Institute of Technology Karnataka-Surathkal, Mangalore 575025, Karnataka, India |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36115301$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_3390_diagnostics13193023 crossref_primary_10_3389_fonc_2025_1594332 crossref_primary_10_3390_diagnostics14060591 crossref_primary_10_1002_rcs_70094 crossref_primary_10_3390_bioengineering12060613 crossref_primary_10_1007_s11036_024_02334_8 crossref_primary_10_1007_s44174_023_00113_9 |
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| Keywords | Deep learning Image and video compression Video summarization Wireless capsule endoscopy Convolutional neural network Low complexity video coding |
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| Snippet | Wireless capsule endoscopy (WCE) can be viewed as an innovative technology introduced in the medical domain to directly visualize the digestive system using a... AbstractWireless capsule endoscopy (WCE) can be viewed as an innovative technology introduced in the medical domain to directly visualize the digestive system... |
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| SubjectTerms | 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 |
| Title | Recent developments in wireless capsule endoscopy imaging: Compression and summarization techniques |
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