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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)
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
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    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.
MDPI and ACS Style

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

AMA Style

Prasath VBS. Polyp Detection and Segmentation from Video Capsule Endoscopy: A Review. Journal of Imaging. 2017; 3(1):1.

Chicago/Turabian Style

Prasath, V. B.S. 2017. "Polyp Detection and Segmentation from Video Capsule Endoscopy: A Review" J. Imaging 3, no. 1: 1.

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

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