Skip to main content

Advertisement

Springer Nature Link
Log in

Can we see epithelium tissue structure below the surface using an optical probe?

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

This paper answers the question of whether it is possible to detect changes below the surface in epithelium layered structures using a Stochastic Decomposition Method (SDM) that models the scattered light reflected from the layered structure over an area (2-D scan) illuminated by an optical sensor (fibre) emitting light at either one wavelength or with white light. Our technique correlates the differential changes in the reflected tissue texture with the morphological and physical changes that occur in the tissue occurring inside the structure. This work has great potential for detecting changes in mucosal structures and may lead to enhanced endoscopy when the disease is developing to the outside of the mucosal structure and hence becoming hidden during colonoscopy or endoscopic examination. Tests are performed on layered tissue phantoms, and the results obtained show great effectiveness of the model and method in picking up changes in the morphology of the layered tissue phantoms occurring below the surface. We also establish the robustness of the model to changes in viewing depth by testing it on phantoms viewed at different depths. We show that the model is robust to within a 4-mm-deep viewing range.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Cohen FS, Taslidere E, Murthy S (2008) Classification of layered tissue phantoms for detection of changes in epithelial tissue below the surface using a stochastic decomposition model for scattered signal In: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. ISBI 2008, Paris, France, May 14–17, 2008, pp 1211–1214

  2. Dacosta RS, Wilson BC, Marcon NE (2002) New optical technologies for earlier endoscopic diagnosis of premalignant gastrointestinal lesions. J Gastroenterol Hepatol 17(Suppl):S85–S104

    Article  PubMed  Google Scholar 

  3. DaCosta RS, Wilson BC, Marcon NE (2005) Optical techniques for the endoscopic detection of dysplastic colonic lesions. Curr Opin Gastroenterol 21:70–79

    PubMed  Google Scholar 

  4. Dacosta RS, Wilson BC, Marcon NE (2006) Spectroscopy and fluorescence in esophageal diseases. Best Pract Res Clin Gastroenterol 20:41–57

    Article  PubMed  Google Scholar 

  5. Kiesslich R, Neurath MF (2006) Chromoendoscopy and other novel imaging techniques. Gastroenterol Clin North Am 35:605–619

    Article  PubMed  Google Scholar 

  6. Taylor JC, Kendall CA, Stone N, Cook TA (2007) Optical adjuncts for enhanced colonoscopic diagnosis. Br J Surg 94:6–16

    Article  CAS  PubMed  Google Scholar 

  7. Van Dam J (2003) Novel methods of enhanced endoscopic imaging. Gut 52(Suppl 4):12–16

    Google Scholar 

  8. Hurlstone DP, Sanders DS, Thomson M (2007) Detection and treatment of early flat and depressed colorectal cancer using high-magnification chromoscopic colonoscopy: a change in paradigm for Western endoscopists? Dig Dis Sci 52:1387–1393

    Article  PubMed  Google Scholar 

  9. Moreaux J, Catala M (1987) Carcinoma of the colon—long-term survival and prognosis after surgical-treatment in a series of 798 patients. World J Surg 11:804–808

    Article  CAS  PubMed  Google Scholar 

  10. Kiesslich R, Burg J, Vieth M, Gnaendiger J, Enders M, Delaney P, Polglase A, McLaren W, Janell D, Thomas S, Nafe B, Galle PR, Neurath MF (2004) Confocal laser endoscopy for diagnosing intraepithelial neoplasias and colorectal cancer in vivo. Gastroenterology 127:706–713

    Article  PubMed  Google Scholar 

  11. Kiesslich R, Neurath MF (2005) Endoscopic detection of early lower gastrointestinal cancer. Best Pract Res Clin Gastroenterol 19:941–961

    Article  CAS  PubMed  Google Scholar 

  12. Osdoit A, Lacombe F, Cavé C, Loiseau S, Peltier E (2007) To see the unseeable: confocal miniprobes for routine microscopic imaging during endoscopy In: Proc. SPIE 6432, 64320F 2007

  13. Viellerobe B, Osdoit A, Cavé C, Lacombe F, Loiseau S, Abrat B (2006) Mauna Kea technologies’ F400 prototype: a new tool for in vivo microscopic imaging during endoscopy In: Proc SPIE 6082, 60820C, 2006

  14. Hidovic-Rowe D, Claridge E (2005) Modelling and validation of spectral reflectance for the colon. Phys Med Biol 50:1071–1093

    Article  PubMed  Google Scholar 

  15. Liu Y, Kim YL, Backman V (2005) Development of a bioengineered tissue model and its application in the investigation of the depth selectivity of polarization gating. Appl Opt 44:2288–2299

    Article  PubMed  Google Scholar 

  16. Liu Q, Ramanujam N (2006) Sequential estimation of optical properties of a two-layered epithelial tissue model from depth-resolved ultraviolet-visible diffuse reflectance spectra. Appl Opt 45:4776–4790

    Article  PubMed  Google Scholar 

  17. Wang A, Nammalvar V, Drezek R (2007) Experimental evaluation of angularly-variable fiber geometry for targeting depth-resolved reflectance from layered epithelial tissue phantoms In: Proc. SPIE Optical Fibers and Sensors for Medical Diagnostics and Treatment Applications VII 64330B

  18. Arifler D, Guillaud M, Carraro A, Malpica A, Follen M, Richards-Kortum R (2003) Light scattering from normal and dysplastic cervical cells at different epithelial depths: finite-difference time-domain modeling with a perfectly matched layer boundary condition. J Biomed Opt 8:484–494

    Article  PubMed  Google Scholar 

  19. Liu Q, Ramanujam N (2007) Scaling method for fast Monte Carlo simulation of diffuse reflectance spectra from multilayered turbid media. J Opt Soc Am a Opt Image Sci Vis 24:1011–1025

    Article  PubMed  Google Scholar 

  20. Fockens P (2002) Future developments in endoscopic imaging. Best Pract Res Clin Gastroenterol 16:999–1012

    Article  PubMed  Google Scholar 

  21. Hagblad J, Lindberg LG, Andersson AK, Bergstrand S, Lindgren M, Ek AC, Folke M, Linden M (2010) A technique based on laser Doppler flowmetry and photoplethysmography for simultaneously monitoring blood flow at different tissue depths. Med Biol Eng Comput 48:415–422

    Article  CAS  PubMed  Google Scholar 

  22. Pandian PS, Kumaravel M, Singh M (2009) Multilayer imaging and compositional analysis of human male breast by laser reflectometry and Monte Carlo simulation. Med Biol Eng Comput 47:1197–1206

    Article  CAS  PubMed  Google Scholar 

  23. Cohen FS, Taslidere E, Hari DS, Murthy S (2008) Stochastic decomposition method (SDM) for modeling the scattered signal reflected of mucosal tissues. J Biomed Opt 13:14–054039

    Article  Google Scholar 

  24. Cohen FS, Taslidere E, Hari DS (2006) Tissue characterization and detection of dysplasia using scattered light In: 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro. ISBI 2006, Arlington, Virginia, USA, April 6–9, 2006, pp 590–593

  25. Taslidere E, Cohen FS (2006) Stochastic decomposition method for detection of epithelium dysplasia and inflammation using white light spectroscopy imaging,” In: 28th annual international conference of the IEEE Engineering in Medicine and Biology Society. EMBS ‘06, New York City, New York, USA, August 30-Sept. 3, 2006, pp 1956–1959

  26. Zangaro RA, Silveira L, Manoharan R, Zonios G, Itzkan I, Dasari RR, Van Dam J, Feld MS (1996) Rapid multiexcitation fluorescence spectroscopy system for in vivo tissue diagnosis. Appl Opt 35:5211–5219

    Article  CAS  PubMed  Google Scholar 

  27. Kudo S, Kashida H, Tamura T, Kogure E, Imai Y, Yamano H, Hart AR (2000) Colonoscopic diagnosis and management of nonpolypoid early colorectal cancer. World J Surg 24:1081–1090

    Article  CAS  PubMed  Google Scholar 

  28. Matsui T, Yao T, Iwashita A (2000) Natural history of early colorectal cancer. World J Surg 24:1022–1028

    Article  CAS  PubMed  Google Scholar 

  29. Wolber RA, Owen DA (1991) Flat adenomas of the colon. Hum Pathol 22:70–74

    Article  CAS  PubMed  Google Scholar 

  30. Waye JD, Rex DK, Williams CB, (2003) Flat and Depressed Colorectal Neoplasia in the Western Hemisphere In: Raju GS, Pasricha PJ (eds) Colonoscopy: principles and practice, 1st edn. Malden, Mass, Blackwell, pp x, 655 p

  31. Zonios GI, Cothren RM, Arendt JT, Wu J, VanDam J, Crawford JM, Manoharan R, Feld MS (1996) Morphological model of human colon tissue fluorescence. IEEE Trans Biomed Eng 43:113–122

    Article  CAS  PubMed  Google Scholar 

  32. Fenoglio-Preiser CM, Hutter RV (1985) Colorectal polyps: pathologic diagnosis and clinical significance. CA Cancer J Clin 35:322–344

    Article  CAS  PubMed  Google Scholar 

  33. Varma JR, Mills LR (1992) Colon polyps. J Fam Pract 35:194–200

    CAS  PubMed  Google Scholar 

  34. Brookner CK, Follen M, Boiko I, Galvan J, Thomsen S, Malpica A, Suzuki S, Lotan R, Richards-Kortum R (2000) Autofluorescence patterns in short-term cultures of normal cervical tissue. Photochem Photobiol 71:730–736

    Article  CAS  PubMed  Google Scholar 

  35. Huang ZW, Zheng W, Xie SS, Chen R, Zeng HS, McLean DI, Lui H (2004) Laser-induced autofluorescence microscopy of normal and tumor human colonic tissue. Int J Oncol 24:59–63

    CAS  PubMed  Google Scholar 

  36. Marchesini R, Pignoli E, Tomatis S, Fumagalli S, Sichirollo AE, Dipalma S, Dalfante M, Spinelli P, Croce AC, Bottiroli G (1994) Ex-vivo optical-properties of human colon tissue. Lasers Surg Med 15:351–357

    Article  CAS  PubMed  Google Scholar 

  37. Picinbono B (1993) Random signals and systems. Englewood Cliffs, Prentice Hall

    Google Scholar 

  38. Porat B (1994) Digital processing of random signals: theory and methods. Englewood Cliffs, Prentice Hall

    Google Scholar 

  39. Cohen FS, Georgiou G, Halpern EJ (1997) WOLD decomposition of the backscatter echo in ultrasound images of soft tissue organs. IEEE Trans Ultrason Ferroelectr Freq Control 44:460–472

    Article  CAS  PubMed  Google Scholar 

  40. Desai UB (1986) Modelling and application of stochastic processes. Kluwer Academic Publishers, Boston

    Google Scholar 

  41. Papoulis A, Pillai SU (2002) Probability, random variables, and stochastic processes, 4th edn. McGraw-Hill, Boston

    Google Scholar 

  42. Ross SM (1996) Stochastic processes, 2nd edn. Wiley, New York

    Google Scholar 

  43. Georgiou G, Cohen FS (2001) Tissue characterization using the continuous wavelet transform Part I: Decomposition method. IEEE Trans Ultrason Ferroelectr Freq Control 48:355–363

    Article  CAS  PubMed  Google Scholar 

  44. Taslidere E, Cohen FS, Georgiou G (2008) Classification of simulated hyperplastic stages in the breast ducts based on ultrasound RF echo. IEEE Trans Ultrason Ferroelectr Freq Control 55:50–63

    PubMed  Google Scholar 

  45. Vitol EA, Kurzweg TP, Nabet B (2005) Using white-light spectroscopy for size determination of tissue phantoms In: Proc. SPIE Photonics North, Toronto, Canada

  46. Sambongi M, Igarashi M, Obi T, Yamaguchi M, Ohyama N, Kobayashi M, Sano Y, Yoshida S, Gono K (2002) Analysis of spectral reflectance using normalization method from endoscopic spectroscopy system. Opt Rev 9:238–243

    Article  Google Scholar 

Download references

Acknowledgments

We would like to thank Photonics Lab members: Ms. Elina Vitol, Dr. Timothy Kurzweg and Dr. Bahram Nabet in the ECE Dept. at Drexel University for providing the optical device (probe, spectrometer and light source) and the phantoms used in this research. Special thanks are also due Ms. Elina Vitol for preparing the multi-layered tissue phantoms. We also thank Dr. Jim Reynolds from Drexel College of Medicine for many useful discussions on the subject of light endoscopy.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA

    Fernand S. Cohen & Ezgi Taslidere

  2. Division of Gastroenterology and Hepatology, College of Medicine, Drexel University, Philadelphia, PA, USA

    Sreekant Murthy

Authors
  1. Fernand S. Cohen
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ezgi Taslidere
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Sreekant Murthy
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Ezgi Taslidere.

Additional information

Part of this work has been published in the peer-reviewed IEEE International Symposium on Biomedical Imaging (ISBI 2008): From Nano to Macro; Paris, France, 14–17 May 2008 [1]. F. S. Cohen, E. Taslidere, and S. Murthy, “Classification of layered tissue phantoms for detection of changes in epithelial tissue below the surface using a stochastic decomposition model for scattered signal,” in 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. ISBI 2008, Paris, France, May 14–17, 2008, pp. 1211–1214.

Fernand S. Cohen and Ezgi Taslidere contributed equally to this work.

Appendix

Appendix

In this section, we take the reader through every step of our technique by showing the details on a real worked-out example that shows the raw 1D scan data for each structure, the fitted parameters of AR process and the d metric obtained based on a specific parameter of the AR process when comparing two 1D scans. It also shows the evaluated threshold 2σd that the metric dγ,λ is compared to in order to arrive at the binary decision (the two scans belong to the same or to different structures) for the selected example. The details of the data collection process using the experimental setup shown in Fig. 5 are given in Fig. 7, and it results into the 1D scans of the samples shown at the top left-hand side of Fig. 8. Then AR process is fitted on those signals, and the AR parameters are extracted. The goodness of the AR model is shown in Fig. 8a, top right hand side; which shows typical realizations of the fitted AR process. As we can see, the AR process captures well the 1D raw data scan characteristics for both structures. Based on the fitted AR parameters, σd is computed for each nominal model as explained in Sect. 2.2.2. For the two samples shown in Fig. 7a, the dγ,λ metric between two samples for each individual parameter (shown in Fig. 8b) for just the parameter) is calculated and expressed as function of the corresponding σd for the nominal model. For this data set, the dγ,λ is found to be 31σd, which is well above the threshold 2σd, resulting in the decision that the two scans originated from two different structures.

Fig. 7
figure 7

Zooming into the data collection process using the setup shown in Fig. 5

Full size image
Fig. 8
figure 8

Steps of the technique are shown for a sample data pair in detail; from the raw the 1D scan data for each structure, to the fitted parameters of AR process and to the d metric obtained based on a specific parameter of the AR process when comparing two 1D scans. The details are shown for a typical realizations of the AR process for sample 1D scans, b decision-making process for the parameter

Full size image

Rights and permissions

Reprints and permissions

About this article

Cite this article

Cohen, F.S., Taslidere, E. & Murthy, S. Can we see epithelium tissue structure below the surface using an optical probe?. Med Biol Eng Comput 49, 85–96 (2011). https://doi.org/10.1007/s11517-010-0672-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-010-0672-4

Keywords