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Smart Videocapsule for Early Diagnosis of Colorectal Cancer: Toward Embedded Image Analysis

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Computational Intelligence in Digital and Network Designs and Applications

Abstract

Wireless capsule endoscopy (WCE) enables screening of the gastrointestinal tract by a swallowable imaging system. However, contemporary WCE systems have several limitations—battery, low processing capabilities, among others—which often result in low diagnostic yield. In this chapter, after a technical presentation of the components of a standard WCE, the authors discuss the related limitations and introduce a new concept of smart capsule with embedded image processing capabilities based on a boosting approach using textural features. We discuss the feasibility of the hardware integration of the detection–recognition method, also with respect to the most recent FPGA technologies.

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References

  1. Parkin, M.C., Shin, F.J., Forman, B.F.: Globocan 2008 v1.2, cancer incidence and mortality worldwide: Iarc cancerbase no. 10. International Agency for Research on Cancer (2008)

    Google Scholar 

  2. Moglia, A., Menciassi, A., Dario, A., Cuschieri, A.: Capsule endoscopy: progress update and challenges ahead. Nat. Rev. Gastroenterol. Hepatol. 6, 352–362 (2009)

    Article  Google Scholar 

  3. Spada, C., Hassan, C., Munoz-Navas, M., Neuhaus, H., Deviere, J., Fockens, P., Coron, E., Gay, G., Toth, E., Riccioni, M.-E., Carretero, C., Charton, J.-P., Van Gossum, A., Wientjes, C.A., Sacher-Huvelin, S., Delvaux, M., Nemeth, A., Petruzziello, L., Prieto de Frias, C., Mayershofer, R., Aminejab, L., Dekker, E., Galmiche, J.-P., Frederic, M., Johansson, G.W., Cesaro, P., Costamagna, G.: Second-generation colon capsule endoscopy compared with colonoscopy. Gastrointest. Endosc. 74(3), 581–589 (2011)

    Article  Google Scholar 

  4. Bergwerk, A., Fleischer, D., Gerber, J.: A capsule endoscopy guide for the practising clinician: technology and troubleshooting. Medline 66(6), 1188–1195 (2007)

    Google Scholar 

  5. Bang, S., Park, J.Y., Jeong, S., Kim, Y.H., Shim, H.B., Kim, T.S., Lee, D.H., Song, S.Y.: First clinical trial of the MiRo capsule endoscope by using a novel transmission technology: electric-field propagation. Gastrointest. Endosc. 69(2), 253–259 (2009)

    Article  MATH  Google Scholar 

  6. Najm, F.N.: A survey of power estimation techniques in VLSI circuits. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 2(4), 446–455 (1994)

    Article  Google Scholar 

  7. Benini, L., Hodgson, R., Siegel, P.: System-level power estimation and optimization. In: Proceedings of the 1998 International Symposium on Low Power Electronics and Design, ISLPED’98, pp. 173–178, New York. ACM (1998)

    Google Scholar 

  8. Bogliolo, A., Benini, L.: Robust RTL power macromodels. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 6(4), 578–581 (1998)

    Article  Google Scholar 

  9. Gupta, S., Najm, F.N.: Power modeling for high-level power estimation. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 8(1), 18–29 (2000)

    Article  Google Scholar 

  10. Gupta, S., Najm, F.N.: Analytical models for RTL power estimation of combinational and sequential circuits. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 19(7), 808–814 (2000)

    Article  MATH  Google Scholar 

  11. Gupta, S., Najm, F.N.: Energy and peak-current per-cycle estimation at RTL. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 11(4), 525–537 (2003)

    Article  Google Scholar 

  12. Anderson, J.H., Najm, F.N.: Power estimation techniques for FPGAs. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 12(10), 1015–1027 (2004)

    Article  Google Scholar 

  13. Buyuksahin, K.M., Najm, F.N.: Early power estimation for VLSI circuits. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 24(7), 1076–1088 (2005)

    Article  Google Scholar 

  14. Naehyuck, C., Kwanho, K., Gyu, L.H.: . Cycle-accurate energy measurement and characterization with a case study of the ARM7TDMI [microprocessors]. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 10(2), 146–154 (2002)

    Google Scholar 

  15. Lee, H., Lee, K., Choi, Y., Chang, N.: Cycle-accurate energy measurement and characterization of FPGAs. Analog Integr. Circuits Signal Process. 42(3), 239–251 (2005)

    Article  Google Scholar 

  16. Suissa, A., Romain, O., Denoulet, J., Hachicha, K., Garda, P.: Empirical method based on neural networks for analog power modeling. IEEE Trans. Comput. Aided Des. Integ. Circuits Syst. 29(5), 839–844 (2010)

    Article  Google Scholar 

  17. Burch, R., Najm, F.N., Yang, B.S.P., Trick, T.N.: A Monte Carlo approach for power estimation. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 1(1), 63–71 (1993)

    Article  Google Scholar 

  18. Nemani, M., Najm, F.N.: High-level area and power estimation for VLSI circuits. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 18(6), 697–713 (1999)

    Article  Google Scholar 

  19. Sreeramaneni, R., Vrudhula, S.B.K.: Energy profiler for hardware/software co-design. In: Proceedings of 17th International Conference on VLSI Design, pp. 335–340 (2004)

    Google Scholar 

  20. Eliakim, R., Yassin, K., Niv, Y., Metzger, Y., Lachter, J., Gal, E., Sapoznikov, B., Konikoff, F., Leichtmann, G., Fireman, Z., Kopelman, Y., Adler, S.N.: Prospective multi center performance evaluation of the second generation colon capsule compared with colonoscopy. Endoscopy 41, 1026–1031 (2009)

    Article  Google Scholar 

  21. Kolar, A., Romain, O., Ayoub, J., Viateur, S., Granado, B.: Prototype of video endoscopic capsule with 3-D imaging capabilities. IEEE Trans. Biomed. Circuits Syst. 4(4), 239–249 (2010)

    Article  Google Scholar 

  22. Ayoub, J., Granado, B., Mhanna, Y., Romain, O.: SVM based colon polyps classifier in a wireless active stereo endoscope. In: 2010 IEEE EMBC, pp. 5585–5588 (2010)

    Google Scholar 

  23. Liu, M., Lu, L., Bi, J., Raykar, V., Wolf, M., Salganicoff, M.: Robust large scale prone-supine polyp matching using local features: a metric learning approach. In: Fichtinger, Gabor, Martel, Anne, Peters, Terry (eds.) Medical Image Computing and Computer-Assisted Intervention. Lecture Notes in Computer Science, vol. 6893, pp. 75–82. Springer, Berlin (2011)

    Google Scholar 

  24. Bernal, J., Sanchez, J., Vilariño, F.: Towards automatic polyp detection with a polyp appearance model. Pattern Recognit. 45(9), 3166–3182 (2012)

    Article  Google Scholar 

  25. Figueiredo, P.N., Figueiredo, I.N., Prasath, S., Tsai, R.: Automatic polyp detection in pillcam colon 2 capsule images and videos: preliminary feasibility report. Diagn. Therapeutic Endosc. 182435, 1–7 (2011)

    Article  Google Scholar 

  26. Karargyris, A., Bourbakis, N.: Identification of polyps in wireless capsule endoscopy videos using log gabor filters. In: IEEE Workshop LiSSA, pp. 143–147 (2009)

    Google Scholar 

  27. Kodogiannis, V., Boulougoura, M.: An adaptive neurofuzzy approach for the diagnosis in wireless capsule endoscopy imaging. Int. J. Inf. Technol. 13, 46–56 (2007)

    Google Scholar 

  28. Karkanis, S.A., Iakovidis, D.K., Maroulis, D.E., Karras, D.A., Tzivras, M.: Computer-aided tumor detection in endoscopic video using color wavelet features. IEEE Trans. Inf. Technol. Biomed. 7(3), 141–152 (2003)

    Article  Google Scholar 

  29. Tagzout, S., Achour, K., Djekoune, O.: Hough transform algorithm for FPGA implementation. Signal Process. 81(6), 1295–1301 (2001)

    Article  MATH  Google Scholar 

  30. Davis, L.S., Johns, S.A., Aggarwal, J.K.: Texture analysis using generalized co-occurrence matrices. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-1(3), 251–259 (1979)

    Google Scholar 

  31. Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67(5), 786–804 (1979)

    Article  Google Scholar 

  32. Iakovidis, D.K., Maroulis, D.E., Bariamis, D.G.: FPGA architecture for fast parallel computation of co-occurrence matrices. Microprocess. Microsyst. 31(2), 160–165 (2007)

    Article  Google Scholar 

  33. Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37(3), 297–336 (1999)

    Article  Google Scholar 

  34. Viola, S., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE CVPR Conference, pp. 511–518 (2001)

    Google Scholar 

  35. Kohonen, T.: Chapter learning vector quantization. The Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge (1995)

    Google Scholar 

  36. Elhossini, A., Moussa, M.: Memory efficient FPGA implementation of Hough transform for line and circle detection. In: CCECE, pp. 1–5 (2012)

    Google Scholar 

  37. Volder, J.E.: The CORDIC trigonometric computing technique. IRE Trans. Electron. Comput. EC-8(5), 335–339 (1959)

    Google Scholar 

  38. Ruen, J.J., Shie, M.S., Chen, C.: A circular Hough transform hardware for industrial circle detection applications. In: IEEE Conference on Industrial Electronics and Applications, pp. 1–6 (2006)

    Google Scholar 

  39. Souki, M.A., Boussaid, L., Abid, M.: An embedded system for real-time traffic sign recognizing. In Proceedings of the 3rd International Design and Test Workshop. IDT 2008, pp. 273–276 (2008)

    Google Scholar 

  40. Geninatti, S.R., Benavidez-Benitez, S.R., Hernandez-Calvino, M., Guil-Mata, N., Gomez-Luna, J.: FPGA implementation of the generalized Hough transform. In: Proceedings—2009, International Conference ReConFigurable Computing and FPGAs, pp. 172–177 (2009)

    Google Scholar 

  41. Hardzeyeu, V., Klefenz, F.: On using the Hough transform for driving assistance applications. In: 2008 International Conference on Intelligent Computer Communication and Processing, pp. 91–98 (2008)

    Google Scholar 

  42. Sieler, L., Tanougast, C., Bouridane, A.: A scalable and embedded FPGA architecture for efficient computation of grey level co-occurrence matrices and Haralick textures features. Microprocess. Microsyst. 34(1), 14–24 (2010)

    Article  Google Scholar 

  43. Tahir, M.A., Bouridane, A., Kurugollu, F.: An FPGA Based Coprocessor for the Classification of Tissue Patterns in Prostatic Cancer. Volume 3203 of Lecture Notes in Computer Science, pp. 771–780. Springer, Berlin (2004)

    Google Scholar 

  44. Mitéran, J., Matas, J., Bourennane, E., Paindavoine, M., Dubois, J.: Automatic hardware implementation tool for a discrete Adaboost-based decision algorithm. EURASIP J. Appl. Signal Process. 1035–1046, 2005 (2005)

    Google Scholar 

  45. Wang, A.Y., Sodini, C.G.: On the energy efficiency of wireless transceivers. In: IEEE International Conference on Communications 2006, vol. 8, pp. 3783–3788 (2006)

    Google Scholar 

  46. Kuon, I., Rose, J.: Measuring the gap between FPGAs and ASICs. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 26(2), 203–215 (2007)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. ETIS UMR CNRS 8051, ENSEA-University of Cergy-Pontoise, 6 Av. du Ponceau, 95000, Cergy, France

    Quentin Angermann, Aymeric Histace & Olivier Romain

  2. ETIS Lab and APHP Hôpital Lariboisière, Paris Diderot, Paris 7 University, Paris, France

    Xavier Dray

  3. LIP6 Lab, University Pierre Et Marie Curie, CNRS, Paris, France

    Andrea Pinna & Bertrand Granado

Authors
  1. Quentin Angermann
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  2. Aymeric Histace
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  3. Olivier Romain
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  4. Xavier Dray
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  5. Andrea Pinna
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  6. Bertrand Granado
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Corresponding author

Correspondence to Aymeric Histace .

Editor information

Editors and Affiliations

  1. Department of Electronics, ENET’Com, University of Sfax, Sfax, Tunisia

    Mourad Fakhfakh

  2. Department of Electronics, INAOE, Tonantzintla, Mexico

    Esteban Tlelo-Cuautle

  3. Laboratoire LiSSi (EA 3956), Université Paris-Est Créteil, Vitry sur Seine, France

    Patrick Siarry

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Angermann, Q., Histace, A., Romain, O., Dray, X., Pinna, A., Granado, B. (2015). Smart Videocapsule for Early Diagnosis of Colorectal Cancer: Toward Embedded Image Analysis. In: Fakhfakh, M., Tlelo-Cuautle, E., Siarry, P. (eds) Computational Intelligence in Digital and Network Designs and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-20071-2_12

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  • DOI: https://doi.org/10.1007/978-3-319-20071-2_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20070-5

  • Online ISBN: 978-3-319-20071-2

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