Next Article in Journal
Peach Flower Monitoring Using Aerial Multispectral Imaging
Previous Article in Journal
An Excursus on Infrared Thermography Imaging
Previous Article in Special Issue
3D Clumped Cell Segmentation Using Curvature Based Seeded Watershed
Article Menu
Issue 1 (March) cover image

Export Article

Open AccessReview
J. Imaging 2017, 3(1), 1;

Polyp Detection and Segmentation from Video Capsule Endoscopy: A Review

Computational Imaging and VisAnalysis (CIVA) Lab, Department of Computer Science, University of Missouri-Columbia, Columbia, MO 65211, USA
Academic Editor: Gonzalo Pajares Martinsanz
Received: 7 September 2016 / Revised: 12 December 2016 / Accepted: 14 December 2016 / Published: 23 December 2016
(This article belongs to the Special Issue Image and Video Processing in Medicine)
Full-Text   |   PDF [4312 KB, uploaded 23 December 2016]   |  


Video capsule endoscopy (VCE) is used widely nowadays for visualizing the gastrointestinal (GI) tract. Capsule endoscopy exams are prescribed usually as an additional monitoring mechanism and can help in identifying polyps, bleeding, etc. To analyze the large scale video data produced by VCE exams, automatic image processing, computer vision, and learning algorithms are required. Recently, automatic polyp detection algorithms have been proposed with various degrees of success. Though polyp detection in colonoscopy and other traditional endoscopy procedure based images is becoming a mature field, due to its unique imaging characteristics, detecting polyps automatically in VCE is a hard problem. We review different polyp detection approaches for VCE imagery and provide systematic analysis with challenges faced by standard image processing and computer vision methods. View Full-Text
Keywords: capsule endoscopy; colorectal; polyps; detection; segmentation; review capsule endoscopy; colorectal; polyps; detection; segmentation; review

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary materials

  • Supplementary File 1:

    ZIP-Document (ZIP, 16529 KB)

  • Externally hosted supplementary file 1
    Description: This Supplementary contains the following items: 1. PillCamCOLON2_Polyp.mp4 - Video clip showing a polyp from Pillcam(R) COLON2 capsule endoscopy exam corresponds to Figure 3 in the paper. Video courtesy of Given Imaging Inc. 2. PillCamCOLON2_Polyp_sequence - All the 251 frames extracted from the video PillCamCOLON2_Polyp.mp4 3. PillCamCOLON2_Polyp_allframes - Polyp sequence 55 frames identified by a experienced Gastroenterologist from the video PillCamCOLON2_Polyp.mp4 MATLAB .fig files - Viewable using MATLAB, corresponds to Figure 2 in the paper. ———————————————— 4. polyp1cut_3DSfS 5. polyp2cut_3DSfS 6. polyp4cut_3DSfS 7. polyp5cut_3DSfS 8. polyp7cut_3DSfS 9. polyp9cut_3DSfS 10. polyp11cut_3DSfS 11. polyp13cut_3DSfS 3D visualizations of VCE polyps (pedunculated and sessile) obtained using shape from shading technique Reference [30]. Author: Surya Prasath University of Missouri-Columbia USA [email protected] [30] Prasath, V.B.S.; Figueiredo, I.N.; Figueiredo, P.N.; Palaniappan, K. Mucosal region detection and 3D reconstruction in wireless capsule endoscopy videos using active contours. Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE. IEEE, 2012, pp. 4014–4017.

Share & Cite This Article

MDPI and ACS Style

Prasath, V.B.S. Polyp Detection and Segmentation from Video Capsule Endoscopy: A Review. J. Imaging 2017, 3, 1.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
J. Imaging EISSN 2313-433X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top