Preview

Journal of the Russian Universities. Radioelectronics

Advanced search

Biomedical Data Analysis Systems for the Diagnosis of Skin Neoplasms

https://doi.org/10.32603/1993-8985-2020-23-3-80-92

Abstract

Introduction. The effectiveness of the diagnosis of malignant neoplasms of the skin remains unsatisfactory due to the complex process of interpretation of clinical features. On the other hand, in the last two decades, noninvasive optical diagnostic methods have been actively developed, for example, digital dermatoscopy for visualization of surface neoplasms and Optical Coherence Tomography (OCT) for obtaining spatial scans. Recent advances in the study of non-invasive diagnostic tools makes this area very promising for research in a clinical condition.
Aim. Developing of software modules based on the mathematical framework of texture analysis for biomedical data systems designed for the diagnosis of skin malignant neoplasms.
Materials and methods. Algorithms of software modules developed for optical systems of our own design are presented. Algorithms for a dermatoscopic module are based on the Haar transform, Local Binary Patterns and color features. Algorithms for OCT are based on the texture features of Haralick, Tamura, fractal dimension, complex directional field and Markov random field. Studies were conducted on sets of 106 dermatoscopic and 1008 OCT images of various classes of pathologies, including melanoma and Basal Cell Carcinoma (BCC).
Results. The values of sensitivity and specificity for the dermatoscopic system and OCT were experimentally obtained.
Conclusion. The sensitivity of the dermatoscopic system is 90 % versus 93 % for other authors, as well as the specificity is 86 % versus 80 %. One of the factors of the increase can be considered the introduction of a personalized mode - the addition of comparative features evaluating a difference between a tumor and a normal tissue in the software analysis module. The improved accuracy of OCT is up to 97 % for the diagnosis of melanoma and up to 96 % for the diagnosis of BCC.

About the Author

O. O. Myakinin
Samara National Research University
Russian Federation

Oleg O. Myakinin, master’s degrees of Applied Mathematics and Computer Science, Senior Lecturer of the Department Lasers and Biotechnical Systems of the Samara State Aerospace University, a Researcher of the "Photonics" Laboratory of named University. The author of more than 50 scientific publications. Area of expertise: computer vision; artificial intelligence; biomedical signal processing.

34 Moskovskoe Shosse, Samara 443086



References

1. Friedman R. J., Gutkowicz-Krusin D., Farber M. J., Warycha M., Schneider-Kels L., Papastathis N., Mihm Jr. M. C., Googe P., King R., Prieto V. G., Kopf A. W., Polsky D., Rabinovitz H., Oliviero M., Cognetta A., Rigel D. S., Marghoob A., Rivers J., Johr R., Grant-Kels J. M., Tsao H. The Diagnostic Performance of Expert Dermoscopists vs a ComputerVision System on Small-Diameter Melanomas. Arch Dermatol. 2008, vol. 144, no. 4, pp. 476-482. doi: 10.1001/archderm.144.4.476

2. Drexler W., Fujimoto J. G. Optical Coherence Tomography: Technology and Applications. Berlin Heidelberg, Springer-Verlag, 1375 p. doi: 10.1007/978-3-540-77550-8

3. Drexler W., Fujimoto J. G. State-of-the-art Retinal Optical Coherence Tomography. Progress in Retinal and Eye Research. 2008, vol. 27, no. 1, pp. 45–88. doi: 10.1016/j.preteyeres.2007.07.005

4. Mogensen M., Thrane L., Jørgensen T. M., Andersen P. E., Jemec G. B. OCT Imaging of Skin Cancer and Other Dermatological Diseases. J. of biophotonics. 2009, vol. 2, no. 6-7, pp. 442–451. doi: 10.1002/jbio.200910020

5. Mogensen M., Nürnberg B. M., Forman J. L., Thomsen J. B., Thrane L., Jemec G. B. E. In vivo Thickness Measurement of Basal Cell Carcinoma and Actinic Keratosis with Optical Coherence Tomography and 20-MHz Ultrasound. British J. of Dermatology. 2009, vol. 160, no. 5, pp. 1026–1033. doi: 10.1111/j.1365-2133.2008.09003.x

6. Massone C., Di Stefani A., Soyer H. P. Dermoscopy for Skin Cancer Detection. Current opinion in oncology. 2005, vol. 17, no. 2, pp. 147–153. doi: 10.1097/01.cco.0000152627.36243.26

7. Kaliyadan F. The Scope of the Dermoscope. Indian Dermatol Online J. 2016, vol. 7, pp. 359–363. doi: 10.4103/2229-5178.190496

8. Cotton S., Claridge E. Developing a Predictive Model of Human Skin Coloring. Medical Imaging 1996: Physics of Medical Imaging. International Society for Optics and Photonics. 1996, vol. 2708, pp. 814–825. doi: 10.1117/12.237846

9. Cotton S. D., Claridge E., Hall P. N. Noninvasive Skin Imaging. 15th Biennial Intern. Conf. on Information Processing in Medical Imaging (IPMI'97) Poultney, Vermont, USA, June 9–13, 1997. Berlin Heidelberg, Springer-Verlag. 1997, vol. 2, pp. 501–507. Lecture Notes in Computer Science, vol. 1230. doi: 10.1007/3-540-63046-5_50

10. Monheit G., Cognetta A. B., Ferris L., Rabinovitz H., Gross K., Martini M., Grichnik J. M., Mihm M., Prieto V. G., Googe P., King R., Toledano A., Kabelev N., Wojton M., Gutkowicz-Krusin D. The Performance of MelaFind. A Prospective Multicenter Study. Arch Dermatol. 2011, vol. 147, no. 2, pp. 188–194. doi: 10.1001/archdermatol.2010.302

11. Emery J. D., Hunter J., Hall P. N., Watson A. J., Moncrieff M., Walter F. M. Accuracy of SIAscopy for Pigmented Skin Lesions encountered in Primary Care: Development and Validation of a New Diagnostic Algorithm. BMC dermatology. 2010, vol. 10, 9 p. doi: 10.1186/1471-5945-10-9

12. Walter F. M., Morris H. C., Humphrys E., Hall P. N., Prevost A. T., Burrows N., Bradshaw L., Wilson E. C. F., Norris P., Walls J., Johnson M., Kinmonth A. L., Emery J. D. Effect of adding a Diagnostic Aid to Best Practice to Manage Suspicious Pigmented Lesions in Primary Care: Randomised Controlled Trial. Bmj. 2012, vol. 345, e4110. doi: 10.1136/bmj.e4110

13. Fink C., Jaeger C., Jaeger K., Haenssle H. A. Diagnostic Performance of the MelaFind Device in a Real‐Life Clinical Setting. JDDG: Journal der Deutschen Dermatologischen Gesellschaft. 2017, vol. 15, no. 4, pp. 414–419. doi: 10.1111/ddg.13220

14. Ferris L. K., Harkes J. A., Gilbert B., Winger D. G., Golubets K., Akilov O., Satyanarayanan M. ComputerAided Classification of Melanocytic Lesions using Dermoscopic Images. J. of the American Academy of Dermatology. 2015, vol. 73, no. 5, pp. 769–776. doi: 10.1016/j.jaad.2015.07.028

15. Dorj U. O., Lee K. K., Choi J. Y., Lee M. The Skin Cancer Classification using Deep Convolutional Neural Network. Multimedia Tools and Applications. 2018, vol. 77, no. 8, pp. 9909–9924. doi: 10.1007/s11042-018-5714-1

16. Choudhury D., Naug A., Ghosh S. Texture and Color Feature Based WLS framework Aided Skin Cancer Classification using MSVM and ELM. 2015 Annual IEEE India Conference (INDICON). New Delhi, India, 17–20 Dec. 2015. Piscataway, IEEE, 2015, 6 p. doi: 10.1109/INDICON.2015.7443780

17. Mirmehdi M., Xie X., Suri J. Handbook of Texture Analysis. London, Imperial College Press, 2008, 423 p.

18. Petrou M., Sevilla P. G. Image Processing: dealing with Texture. Chichester, John Wiley & Sons, 2006, 630 p.

19. Pietikäinen M. K. Texture Analysis in Machine Vision. Singapore, World Scientific, 2000, 280 p.

20. Haralick R. M. Statistical and Structural Approaches to Texture. Proc. of the IEEE. 1979, vol. 67, no. 5, pp. 786–804.

21. Dubes R. C., Jain A. K. Random Field Models in Image Analysis. J. of applied statistics. 1993, vol. 20, no. 5-6, pp. 121–154. doi: 10.1080/02664769300000062

22. Ahuja N., Rosenfeld A. Mosaic Models for Textures. IEEE Trans. on Pattern Analysis and Machine Intelligence. 1981, vol. PAMI-3, no. 1, pp. 1–11. doi: 10.1109/TPAMI.1981.4767045

23. Konovalov S. G., Melsitov O. A., Myakinin O. O., Bratchenko I. A., Moryatov A. A., Kozlov S. V., Zakharov V. P. Dermatoscopy Software Tool for In Vivo Automatic Malignant Lesions Detection. J. of Biomedical Photonics & Engineering. 2018, vol. 4, no. 4, pp. 040302(1–9). doi: 10.18287/JBPE18.04.040302

24. Walter F. M., Prevost A. T., Vasconcelos J., Hall P. N., Burrows N. P., Morris H. C., Kinmonth A. L., Emery J. D. Using the 7-point Checklist as a Diagnostic Aid for Pigmented Skin Lesions in General Practice: a Diagnostic Validation Study. British J. General Practice. 2013, vol. 63, no. 610, pp. e345–e353. doi: 10.3399/bjgp13X667213

25. Myakinin O. O., Zakharov V. P., Bratchenko I. A., Artemyev D. N., Neretin E. Y., Kozlov S. V. Dermoscopy Analysis of RGB-Images based on Comparative Features. Proc. SPIE. 2015. Vol. 9599. Applications of Digital Image Processing XXXVIII. 95992B. doi: 10.1117/12.2188165

26. Puvanathasan P., Bizheva K. Interval Type-II fuzzy Anisotropic Diffusion Algorithm for Speckle Noise Reduction in Optical Coherence Tomography Images. Optics express. 2009, vol. 17, no. 2, pp. 733–746. doi: 10.1364/OE.17.000733

27. Haralick R. M., Shanmugam K. Textural Features for Image Classification. IEEE Trans. on Systems, Man and Cybernetics. 1973, vol. SMC-3, no. 6, pp. 610–621. doi: 10.1109/TSMC.1973.4309314

28. Fogel I., Sagi D. Gabor Filters as Texture Discriminator. Biol. Cybern. 1989, vol. 61, no. 2, pp. 103–113. doi: 10.1007/BF00204594

29. Tamura H., Mori S., Yamawaki T. Textural Features corresponding to Visual Perception. IEEE Trans. on Systems, Man and Cybernetics. 1978, vol. SMC-8, no. 6, pp. 460–473. doi: 10.1109/TSMC.1978.4309999

30. Voss R. F. Random Fractal Forgeries. Fundamental Algorithms for Computer Graphics; ed. by R. A. Earnshaw. Berlin Heidelberg, Springer-Verlag, 1985, pp. 805–835.

31. Ahammer H. Higuchi Dimension of Digital Images. PLoS One. 2011, vol. 6, no. 9, pp. e24796. doi: 10.1371/journal.pone.0024796

32. Sarkar N., Chaudhuri B.B. An Efficient Differential Box-Counting Approach to Compute Fractal Dimension of Image. IEEE Trans. on Systems, Man and Cybernetics. 1994, vol. SMC-24, no. 1, pp. 115–120. doi: 10.1109/21.259692

33. Raupov D. S., Myakinin O. O., Bratchenko I. A., Zakharov V. P., Khramov A. G. Multimodal Texture Analysis of OCT Images as a Diagnostic Application for Skin Tumors. J. of Biomedical Photonics & Engineering. 2017, vol. 3, no. 1, pp. 010307(1–10). doi: 10.18287/JBPE17.03.010307

34. Raupov D. S., Myakinin O. O., Bratchenko I. A., Zakharov V. P., Khramov A. G. Skin Cancer Texture Analysis of OCT Images based on Haralick, Fractal Dimension, Markov Random Field Features, and the Complex Directional Field Features. Proc SPIE. 2016, vol. 10024. Optics in Health Care and Biomedical Optics VII, pp. 100244I. doi: 10.1117/12.2246446

35. Argenziano G., Fabbrocini G., Carli P., De Giorgi V., Sammarco E., Delfino M. Epiluminescence Microscopy for the Diagnosis of Doubtful Melanocytic Skin Lesions: Comparison of the ABCD Rule of Dermatoscopy and a New 7- point Checklist based on Pattern Analysis. Arch Dermatol. 1998, vol. 134, no. 12, pp. 1563–1570. doi: 10.1001/archderm.134.12.1563

36. Benvenuto-Andrade C., Dusza S. W., Agero A. L. C., Scope A., Rajadhyaksha M., Halpern A. C., Marghoob A. A. Differences Between Polarized Light Dermoscopy and Immersion Contact Dermoscopy for the Evaluation of Skin Lesions. Arch Dermatol. 2007, vol. 143, no. 3, pp. 329–338. doi: 10.1001/archderm.143.3.329

37. Wadhawan T., Situ N., Rui H., Lancaster K., Yuan X., Zouridakis G. Implementation of the 7-point Checklist for Melanoma Detection on Smart Handheld Devices. 2011 Annual Intern. Conf. of the IEEE Engineering in Medicine and Biology Society. Boston, MA, USA, 30 Aug.–3 Sept. 2011. Piscataway: IEEE, 2011, pp. 3180– 3183. doi: 10.1109/IEMBS.2011.6090866

38. Esteva A., Kuprel B., Novoa R. A., Ko J., Swetter S. M., Blau H. M., Thrun S. Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks. Nature. 2017, vol. 542, no. 7639, pp. 115–118. doi: 10.1038/nature21056

39. Marvdashti T., Duan L., Aasi S. Z., Tang J. Y., Bowden A. K. E. Classification of Basal Cell Carcinoma in Human Skin using Machine Learning and Quantitative Features captured by Polarization Sensitive Optical Coherence Tomography. Biomedical optics express. 2016, vol. 7, no. 9, pp. 3721–3735. doi: 10.1364/BOE.7.003721

40. Xiong Y. -Q., Mo Y., Wen Y. -Q., Cheng M. -J., Huo S. -T., Chen X. -J., Chen Q. Optical Coherence Tomography for the Diagnosis of Malignant Skin Tumors: a Meta-Analysis. J. Biomed. Opt. 2018, vol. 23, no. 2, 020902. doi: 10.1117/1.JBO.23.2.020902

41. Boone M. A. L. M., Suppa M., Dhaenens F., Miyamoto M., Marneffe A., Jemec G. B. E., Del Marmol V., Nebosis R. In Vivo Assessment of Optical Properties of Melanocytic Skin Lesions and Differentiation of Melanoma from NonMalignant Lesions by High-Definition Optical Coherence Tomograph. Arch Dermatol Res. 2016, vol. 308, pp. 7–20. doi: 10.1007/s00403-015-1608-5

42. Weszka J. S., Dyer C. R., Rosenfeld A. A Comparative Study of Texture Measures for Terrain Classification. IEEE Trans. on Systems, Man and Cybernetics. 1976, vol. SMC-6, no. 4, pp. 269–285. doi: 10.1109/TSMC.1976.5408777


Review

For citations:


Myakinin O.O. Biomedical Data Analysis Systems for the Diagnosis of Skin Neoplasms. Journal of the Russian Universities. Radioelectronics. 2020;23(3):80-92. (In Russ.) https://doi.org/10.32603/1993-8985-2020-23-3-80-92

Views: 675


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 1993-8985 (Print)
ISSN 2658-4794 (Online)