Multimodal texture analysis of OCT images as a diagnostic application for skin tumors
Paper #3175 received 23 Mar 2017; revised manuscript received 25 Apr 2017; accepted for publication 25 Apr 2017; published online 29 Apr 2017.
Optical coherence tomography (OCT) is an effective tool for determination of pathological topology that reflects structural and textural metamorphoses of tissue. In this paper, we propose a report about our examining of the validity of OCT in identifying changes using a skin cancer texture analysis compiled from Haralick texture features, fractal dimension, complex directional field features and Markov random field method from different tissues. The experimental data set contains 530 OCT images with normal skin and tumors as Basal Cell Carcinoma (BCC), Malignant Melanoma (MM) and Nevus. Speckle reduction is an essential pre-processing part for OCT image analyze. In this work, we used an interval type-II fuzzy anisotropic diffusion algorithm for speckle noise reduction in OCT images (B- and/or C-scans). The Haralick texture features as contrast, correlation, energy, and homogeneity have been calculated in various directions. A box-counting method and other methodologies have been performed to evaluate fractal dimension of skin probes. The complex directional field calculated by the local gradient methodology provides important data for linear dividing of species. We also estimated autocorrelation function using Markov random fields. Additionally, the boosting has been used for the quality enhancing of the diagnosis method. And, finally, artificial neural network (ANN) has been utilized for comparing received rates. Our results demonstrate that these texture features may present helpful information to discriminate a tumor from healthy tissue. We obtained sensitivity about 92% and specificity about 95% for a task of discrimination between MM and healthy skin. Finally, a universal four classes classificatory has been built with average accuracy 75%.
1. D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178-1181 (1991).
2. A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, “Optical coherence tomography–principles and applications,” Rep. Prog. Phys. 66(2), 239-303 (2003).
3. A. M. Sergeev, L. S. Dolin, and D. N. Reitze, “Optical tomography of biotissues: past, present and future,” Optics&Photonics News 12(7), 28-35 (2001).
4. E. A. Genina, S. A. Kinder, A. N. Bashkatov, and V. V. Tuchin, “Liver images contrasting in optical coherence tomography with help of nanoparticles,” Proceedings of Saratov University 11(2), 10-13 (2011).
5. Y. Freund, and R. E. Schapire, “A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting,” J. of Computer and System Sciences 55(1), 119-139 (1997). Crossref
6. S. Wang, M. Singh, A. L. Lopez, C. Wu, R. Raghunathan, A. Schill, J. Li, K. V. Larin, and I. V. Larina, “Direct four-dimensional structural and functional imaging of cardiovascular dynamics in mouse embryos with 1.5 MHz optical coherence tomography,” Opt. Lett. 40(20), 4791-4794 (2015).
7. V. P. Zakharov, K. Larin, and I. A. Bratchenko, “Increasing the information content of optical coherence tomography skin pathology detection,” Proceedings of SSAU 26, 232-239 (2011).
8. V. P. Zakharov, I. A. Bratchenko, V. I. Belokonev, D. V. Kornilin, and O. O. Myakinin, “Complex optical characterization of mesh implants and encapsulation area,” J. Innov. Opt. Health Sci. 06, 1350007 (2013).
9. P. Puvanathasan, and K. Bizheva, “Interval type-II fuzzy anisotropic diffusion algorithm for speckle noise reduction in optical coherence tomography images,” Optics Express 17(2), 733-746 (2009). Crossref
10. H. Tizhoosh, “Image thresholding using type II fuzzy sets,” Pattern Recognition 38(12), 2363-2372 (2005). Crossref
11. M. Wojtkowski, “High-speed optical coherence tomography: basics and applications,” Applied Optics 49(16), 30-61 (2010).
12. A. M. Forsea, E. M. Carstea, L. Ghervase, C. Giurcaneanu, and G. Pavelescu, “Clinical application of optical coherence tomography for the imaging of non-melanocytic cutaneous tumors: a pilot multi-modal study,” Journal of medicine and Life 3(4), 381-389 (2010).
13. N. Sarkar, and B. B. Chaudhuri, “An efficient approach to estimate fractal dimension of textural images,” Pattern Recognition 25(9), 1035-1041 (1992). Crossref
14. J. Huang, and D. Turcotte, “Fractal image analysis: application to the topography of Oregon and synthetic images,” J. Opt. Soc. Am. 7(6), 1124-1130 (1990).
15. Y.-Z. Liu, F. A. South, Y. Xu, P. S. Carney, and S. A. Boppart, “Computational optical coherence tomography [Invited],” Biomed. Opt. Express 8, 1549-1574 (2017) Crossref
16. A. I. Plastinin, and A. V. Kupriyanov, “A model of Markov random field in texture image synthesis and analysis,” Proceedings of Samara State Aerospace University 2, 252-257 (2008).
17. N. U. Ilyasova, A. V. Ustinov, and A. G. Khramov, “Numerical methods and algorithms of building of directional fields in quasiperiodic structures,” Computer Optics 18, 150-164 (1998).
18. R. E. Schapire, Y. Freund, P. Bartlett, and W. S. Lee, “Boosting the margin: A new explanation for the effectiveness of voting methods,” Annals of Statistics 26(5), 1651-1686 (1991).
19. O. O. Myakinin, V. P. Zakharov, I. A. Bratchenko, D. V. Kornilin, and A. G. Khramov, “A complex noise reduction method for improving visualization of SD-OCT skin biomedical images,” Proc. SPIE 9129, 91292Y, (2014).
20. R. M. Haralick, K. Shanmugam, and I. Dinshtein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. 3(6), 610-621 (1973). Crossref
21. R. M. Haralick, and L. G. Shapiro, Computer and Robot Vision: Vol. 1, Addison-Wesley (1992). ISBN: 978-0201108774.
22. C. Flueraru, D. P. Popescu, Y. Mao, S. Chang, and M. G. Sowa, “Added soft tissue contrast using signal attenuation and the fractal dimension for optical coherence tomography images of porcine arterial tissue,” Physics in Medicine and Biology 55(8), 2317-2331 (2010). Crossref
23. A. C. Sullivan, J. P. Hunt, and A. L. Oldenburg, “Fractal analysis for classification of breast carcinoma in optical coherence tomography,” Journal of Biomedical Optics 16(6), 066010 (2013).
24. W. Gao, Improving the quantitative assessment of intraretinal features by determining both structural and optical properties of the retinal tissue with optical coherence tomography, Ph.D. thesis (2012).
25. R. F. Voss, “Random fractal forgeries” in: Fundamental Algorithms for Computer Graphics, R. A. Earnshaw (ed.), Springer-Verlag, Berlin, 805-835 (1985).
26. A. Annadhason, “Methods of Fractal Dimension Computation,” IRACST 2(1), 166-169 (2012).
27. N. Sarkar, and B. B. Chaudhuri, “An Efficient Differential Box-Counting Approach to Compute Fractal Dimension of Image,” IEEE Trans. Syst. Man Cybern. 24(1), 115-120 (1994). Crossref
28. J. B. Florindo, and O. M. Bruno, “Fractal Descriptors in the Fourier Domain Applied to Color Texture Analysis,” Chaos 21(4), 043112 (2011). Crossref
29. H. Ahammer, “Higuchi Dimension of Digital Images,” PLoS ONE 6(9), e24796 (2011).
30. G. Winkler, Image Analysis, Random Fields and Dynamic Monte Carlo Methods, Springer, Verlag (1995). ISBN: 978-3-642-97522-6. Crossref
31. A. I. Plastinin, The method of formation of textural features based on Markov models, Ph.D. thesis, 04200201565, 30-45 (2012).
32. J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks 61, 85-117 (2015).
33. D. Rutkovskaya, M. Pilinsky, and L. Rutkowski, Neural networks, genetic algorithms and fuzzy systems, Telecom, 5-20 (2006).
34. S. Haykin, Neural Networks: A Comprehensive Foundation Paperback, MacMillan Publishing Company, 30-40 (1994).
35. W. McCulloch, and W. Pitts, “A Logical Calculus of Ideas Immanent in Nervous Activity,” Bulletin of Mathematical Biophysics 5(4), 115-133 (1943). Crossref
36. M. F. Moller, “A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning,” Neural Networks 6(4), 525-533 (1993). Crossref
37. K. V. Vorontsov, “About the problem-oriented bases optimization of recognition problem,” Computational Mathematics and Mathematical Physics 38(5), 870-880 (1998).
38. K. V. Vorontsov, “Optimization methods for linear and monotone correction in the algebraic approach to the recognition problem,” Computational Mathematics and Mathematical Physics 40(1), 166-176 (2000).
39. L. Breinman, Classification and Regression Trees, Chapman&Hall, Boca Raton (1993).
40. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, Wiley (2001).
41. D. S. Raupov, O. O. Myakinin, I. A. Bratchenko, D. V. Kornilin, V. P. Zakharov, and A. G. Khramov, “Skin cancer texture analysis of OCT images based on Haralick, fractal dimension and the complex directional field features,” Proc. SPIE 9887, 98873F (2016).
42. D. S. Raupov, O. O. Myakinin, I. A. Bratchenko, D. V. Kornilin, V. P. Zakharov, and A. G. Khramov, “Skin cancer texture analysis of OCT images based on Haralick, fractal dimension, Markov random field features, and the complex directional field features,” Proc. SPIE 10024, 100244I (2016).
43. T. Gambichler, M. H. Schmid-Wendtner, I. Plura, P. Kampilafkos, M. Stücker, C. Berking, and T. Maier, “A multicentre pilot study investigating high-definition optical coherence tomography in the differentiation of cutaneous melanoma and melanocytic naevi,” Journal of the European Academy of Dermatology and Venereology 29(3), 537-541 (2015). Crossref
44. C. Wahrlich, S. A. Alawi, S. Batz, J. W. Fluhr, J. Lademann, and M. Ulrich, “Assessment of a scoring system for Basal Cell Carcinoma with multi-beam optical coherence tomography,” Journal of the European Academy of Dermatology and Venereology 29(8), 1562-1569 (2015). Crossref
45. V. P. Zakharov, I. A. Bratchenko, D. N. Artemyev, O. O. Myakinin, D. V. Kornilin, S. V. Kozlov, and A. A. Moryatov, “Comparative analysis of combined spectral and optical tomography methods for detection of skin and lung cancers,” Journal of Biomedical Optics 20(2), 025003 (2015). Crossref
46. Q. Abbas, M. E. Celebi, C. Serrano, I. Garcı´a, G. Ma, “Pattern classification of dermoscopy images: A perceptually uniform model,” Pattern Recognition 46(1), 86-97 (2013). Crossref
© 2014-2021 Samara National Research University. All Rights Reserved.
Public Media Certificate (RUS). 12+