Automatic Image Segmentation for Video Capsule Endoscopy

Chapter
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Abstract

Video capsule endoscopy (VCE) has proven to be a pain-free imaging technique of gastrointestinal (GI) tract and provides continuous stream of color imagery. Due to the amount of images captured automatic computer-aided diagnostic (CAD) methods are required to reduce the burden of gastroenterologists. In this work, we propose a fast and efficient method for obtaining segmentations of VCE images automatically without manual supervision. We utilize an efficient active contour without edges model which accounts for topological changes of the mucosal surface when the capsule moves through the GT tract. Comparison with related image segmentation methods indicate we obtain better results in terms of agreement with expert ground-truth boundary markings.

Keywords

Capsule endoscopy Image segmentation Active contours CAD 

References

  1. 1.
    Mohandas KM (2011) Colorectal cancer in india: controversies, enigmas and primary prevention. Indian J Gastroenterol 30(1):3–6CrossRefGoogle Scholar
  2. 2.
    Pathy S, Lambert R, Sauvaget C, Sankaranarayanan R (2012) The incidence and survival rates of colorectal cancer in India remain low compared with rising rates in east asia. Dis Colon Rectum 55(8):900–906CrossRefGoogle Scholar
  3. 3.
    Iddan G, Meron G, Glukhovsky A, Swain F (2000) Wireless capsule endoscopy. Nature 405(6785):417CrossRefGoogle Scholar
  4. 4.
    Figueiredo PN, Figueiredo IN, Prasath S, Tsai R (2011) Automatic polyp detection in pillcam colon 2 capsule images and videos: Preliminary feasibility report. Diagn Ther Endosc 2011:7 pp Article ID 182435Google Scholar
  5. 5.
    Figueiredo IN, Moreno JC, Prasath VBS, Figueiredo PN (2012) A segmentation model and application to endoscopic images. In: Campilho A, Kamel M (eds) International conference on image analysis and recognition (ICIAR 2012). Springer LNCS, vol 7325. Aveiro, Portugal, pp 164–171 (June 2012)Google Scholar
  6. 6.
    Prasath VBS, Pelapur R, Palaniappan K (2014) Multi-scale directional vesselness stamping based segmentation for polyps from wireless capsule endoscopy. Figshare (June 2014)Google Scholar
  7. 7.
    Karargyris A, Bourbakis N (2010) A survey on wireless capsule endoscopy and endoscopic imaging. a survey on various methodologies presented. IEEE Eng Med Biol Mag 29(1):72–83CrossRefGoogle Scholar
  8. 8.
    Asari KV (2000) A fast and accurate segmentation technique for the extraction of gastrointestinal lumen from endoscopic images. Med Eng Phys 22(2):89–96CrossRefGoogle Scholar
  9. 9.
    Asari KV, Srikanthan T (2002) Segmenting endoscopic images using adaptive progressive thresholding: a hardware perspective. J Syst Architect 47(9):759–761CrossRefGoogle Scholar
  10. 10.
    Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277CrossRefMATHGoogle Scholar
  11. 11.
    Prasath VBS, Figueiredo IN, Figueiredo PN, Palaniappan K (2012) Mucosal region detection and 3D reconstruction in wireless capsule endoscopy videos using active contours. In: 34th IEEE/EMBS international conference, San Diego, USA, pp 4014–4017 (September 2012)Google Scholar
  12. 12.
    Mumford D, Shah J (1989) Optimal approximations by piecewise smooth functions and associated variational problems. Commun Pure Appl Math 42(5):577–685CrossRefMATHMathSciNetGoogle Scholar
  13. 13.
    Osher S, Sethian JA (1988) Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J Comput Phys 79(1):12–49CrossRefMATHMathSciNetGoogle Scholar
  14. 14.
    Prasath VBS (2009) Color image segmentation based on vectorial multiscale diffusion with inter-scale linking. In: Chaudhury S, Mitra S, Murthy CA, Sastry PS, Sankar K.P (eds) Third international conference on pattern recognition and machine intelligence (PReMI-09). Springer LNCS, vol 5909. Delhi, India, pp 339–344 (December 2009)Google Scholar
  15. 15.
    Moreno JC, Prasath VBS, Proenca H, Palaniappan K (2014) Brain MRI segmentation with fast and globally convex multiphase active contours. Comput Vis Image Underst 125:237–250CrossRefGoogle Scholar
  16. 16.
    Prasath VBS, Pelapur R, Palaniappan K, Seetharaman G (2014) Feature fusion and label propagation for textured object video segmentation. In: SPIE Defense + Security (DSS). Baltimore, MD, USA (May 2014) In Geospatial Info Fusion and Video Analytics, IVGoogle Scholar
  17. 17.
    Comaniciu D, Meer P (2002) Mean shift: A robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619CrossRefGoogle Scholar
  18. 18.
    Sandberg B, Chan TF, Vese L (2002) A level-set and Gabor-based active contour algorithm for segmenting textured images. Technical Report, pp 02–39, UCLA CAM (2002)Google Scholar
  19. 19.
    Bresson X, Esedoglu S, Vandergheynst P, Thiran J, Osher S (2007) Fast global minimization of the active contour/snake model. J Math Imaging Vis 28(2):151–167CrossRefMathSciNetGoogle Scholar

Copyright information

© The Author(s) 2015

Authors and Affiliations

  • V. B. Surya Prasath
    • 1
  • Radhakrishnan Delhibabu
    • 2
    • 3
    • 4
  1. 1.University of Missouri-ColumbiaColumbiaUSA
  2. 2.Cognitive Modeling LabIT University InnopolisKazanRussia
  3. 3.Department of CSESSN Engineering CollegeChennaiIndia
  4. 4.Machine cognition labKazan Federal UniversityKazanRussia

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