SampleBoost for Capsule Endoscopy Categorization and Abnormality Detection

2012 ◽  
pp. 285-294
Author(s):  
Mohamed Abouelenien ◽  
Xiaohui Yuan
Keyword(s):  
Capsule Endoscopy ◽  
Abnormality Detection

scholarly journals Computer-Aided Diagnosis for Capsule Endoscopy: From Inception to Future

2019 ◽  
Vol 8 (4) ◽  
pp. 12261-12273
Keyword(s):  
Capsule Endoscopy ◽  
Computer Aided Diagnosis ◽  
Current Status ◽  
Human Beings ◽  
Abnormality Detection ◽  
Gi Tract ◽  
Cad Systems ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

Background: Gastrointestinal (GI) tract abnormalities are most common across the world, and it is a significant threat to the health of human beings. Capsule endoscopy is a non-sedative, non-invasive and patient-friendly procedure for the diagnosis of GI tract abnormalities. However, it is very time consuming and tiresome task for physicians due to length of endoscopy videos. Thus computer-aided diagnosis (CAD) system is a must. Methods: This systematic review aims to investigate state-of-the-art CAD systems for automatic abnormality detection in capsule endoscopy by examining publications from scientific databases namely IEEE Xplore, Science Direct, Springer, and Scopus. Results: Based on defined search criteria and applied inclusion and exclusion criteria, 44 articles are included out of 187. This study presents the current status and analysis of CAD systems for capsule endoscopy. Conclusion: Publicly available larger dataset and a deep learning based CAD system may help to improve the efficiency of automated abnormality detection in capsule endoscopy.

A Survey on Medical Image Analysis in Capsule Endoscopy

2019 ◽  
Vol 15 (7) ◽  
pp. 622-636 ◽  
Author(s):  
Kuntesh Ketan Jani ◽  
Rajeev Srivastava
Keyword(s):  
Image Analysis ◽  
Video Compression ◽  
Capsule Endoscopy ◽  
Medical Image ◽  
Detection System ◽  
Medical Image Analysis ◽  
Video Summarization ◽  
Complex Nature ◽  
Abnormality Detection ◽  
Non Invasive

Background and Objective: Capsule Endoscopy (CE) is a non-invasive, patient-friendly alternative to conventional endoscopy procedure. However, CE produces 6 to 8 hrs long video posing a tedious challenge to a gastroenterologist for abnormality detection. Major challenges to an expert are lengthy videos, need of constant concentration and subjectivity of the abnormality. To address these challenges along with high diagnostic accuracy, design and development of automated abnormality detection system is a must. Machine learning and computer vision techniques are devised to develop such automated systems. Methods: Study presents a review of quality research papers published in IEEE, Scopus, and Science Direct database with search criteria as capsule endoscopy, engineering, and journal papers. The initial search retrieved 144 publications. After evaluating all articles, 62 publications pertaining to image analysis are selected. Results: This paper presents a rigorous review comprising all the aspects of medical image analysis concerning capsule endoscopy namely video summarization and redundant image elimination, Image enhancement and interpretation, segmentation and region identification, Computer-aided abnormality detection in capsule endoscopy, Image and video compression. The study provides a comparative analysis of various approaches, experimental setup, performance, strengths, and limitations of the aspects stated above. Conclusion: The analyzed image analysis techniques for capsule endoscopy have not yet overcome all current challenges mainly due to lack of dataset and complex nature of the gastrointestinal tract.

Capsule Endoscopy Called Safe, Clinically Useful

2006 ◽  
Vol 40 (12) ◽  
pp. 49
Author(s):  
DAMIAN MCNAMARA
Keyword(s):  
Capsule Endoscopy
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