(43-4) 19 * << * >> * Russian * English * Content * All Issues

Hyperspectral in vivo analysis of normal skin chromophores and visualization of oncological pathologies

V.P. Sherendak1, I.A. Bratchenko1, O.O. Myakinin1, P.N. Volkhin1, Yu.A. Khristoforova1, A.A. Moryatov 2, A.S. Machikhin 3, V.E. Pozhar 3, S.G. Kozlov 2, V.P. Zakharov1

1 Samara National Research University, Moskovskoye Shosse 34, 443086, Samara, Russia,

2 Samara State Medical University, Samara, Russia,

3 Scientific and Technological Center of Unique Instrumentation RAS, Moscow, Russia

 PDF, 2196 kB

DOI: 10.18287/2412-6179-2019-43-4-661-670

Pages: 661-670.

Full text of article: Russian language.

In the paper, we present test results of methods for the noninvasive diagnosis of skin neoplasms, based on the hyperspectral registration of images by using a camera with an acousto-optic tunable filter. For the identification of oncological pathologies, an integral spectral index has been proposed for a set of concentric regions around the source of neoplasm growth for the tissue sample under study. As well as taking account of changes in the spectral properties of the tissue, the introduced index indirectly takes into account classical ABCD dermatoscopic features: asymmetry, border irregularity, color diversity, and the tumor diameter. Results of training set separating are presented and the applicability of the proposed approaches to the clinical practice is shown.

hyperspectral imaging, chromophores, melanin, hemoglobin, oncopathology, malignant melanoma, basal cell carcinoma, acousto-optical video spectrometer, optical density, chromophore index, classification

Sherendak VP, Bratchenko IA, Myakinin OO, Volkhin PN, Khristoforova YA, Moryatov AA, Machikhin AS, Pozhar VE, Kozlov SG, Zakharov VP. Hyperspectral in vivo analysis of normal skin chromophores and visualization of oncological pathologies. Computer Optics 2019; 43(4): 661-670. DOI: 10.18287/2412-6179-2019-43-4-661-670.


  1. Parkin DM, Bray F, Ferlay J, Pisani P. Global cancer statistics, 2002. CA Cancer J Clin 2005; 55(2): 74-108. DOI: 10.3322/canjclin.55.2.74.
  2. Boyle P, Parkin D, eds. World cancer report, 2008. Lyon: International Agency for Research on Cancer; 2008.
  3. Siegel R, Naishadham D, Jemal A. Cancer statistics, 2012. CA Cancer J Clin 2012; 62: 10-29. DOI: 10.3322/caac.20138.
  4. Kaprin AD, Starinsky VV, Petrova GV. Malignant neoplasms in Russia in 2015 (morbiliti and mortality) [In Russian]. Moscow: P.A. Hertsen Moscow Oncology Research Center; 2017.
  5. Bratchenko IA, Alonova MV, Myakinin OO, Moryatov AA, Kozlov SV, Zakharov VP. Hyperspectral visualization of skin pathologies in visible region. Computer Optics 2016; 40(2): 240-248. DOI: 10.18287/2412-6179-2016-40-2-240-248.
  6. Gross PE, Strasser-Weippl K, Lee-Bychkovsky BL, et al. Challenges to effective cancer control in China, India, and Russia. Lancet Oncol 2014; 15: 489-538. DOI: 10.1016/S1470-2045(14)70029-4.
  7. Majoie C. Perineural tumor extension of facial malignant melanoma: CT and MRI. J Comput Assist Tomo 1991; 15: 570-574.
  8. Argenziano G, Soyer HP. Dermoscopy of pigmented skin lesions – a valuable tool for early diagnosis of melanoma. Lancet Oncol 2001; 2(7): 443-449. DOI: 10.1016/S1470-2045(00)00422-8.
  9. Bratchenko IA, Artemyev DN, Myakinin OO, et al. Combined Raman and autofluorescence ex vivo diagnostics of skin cancer in near-infrared and visible regions. J Biomed Opt 2017; 22(2): 027005. DOI: 10.1117/1.JBO.22.2.027005.
  10. Lim L, Nichols B, Migden M, et al. Clinical study of noninvasive in vivo melanoma and nonmelanoma skin cancers using multimodal spectral diagnosis. J Biomed Opt 2014; 19(11): 117003. DOI: 10.1117/1.JBO.19.11.117003.
  11. Calin MA, Sorin V, Savastru D, Dragos M. Hyperspectral imaging in the medical field: Present and future. Appl Spectrosc Reviews 2014; 49: 435-447. DOI: 10.1080/05704928.2013.838.
  12. Calin A, Coman T, Parasca SV, et al. Hyperspectral imaging-based wound analysis using mixture-tuned matched filtering classification method. J Biomed Opt 2015; 20(4): 046004. DOI: 10.1117/1.JBO.20.4.046004.
  13. Nagaoka T, Nakamura A, Okutani H, et al. A possible melanoma discrimination index based on hyperspectral data: A pilot study. Skin Research and Technology 2012; 18: 301-310. DOI: 10.1111/j.1600-0846.2011.00571.x.
  14. Nagaoka T, Nakamura A, Okutani H, et al. Hyperspectroscopic screening of melanoma on acral volar skin. Skin Research and Technology 2013; 19: e290-e296. DOI: 10.1111/j.1600-0846.2012.00642.x.
  15. Abbasi NR, Shaw HM, Rigel DS, et al. Early diagnosis of cutaneous melanoma: revisiting the ABCD criteria. J Am Med Assoc 2004; 292(22): 2771-2776. DOI: 10.1001/jama.292.22.2771
  16. Zakharov VP, Bratchenko IA, Artemyev DN, et al. Comparative analysis of combined spectral and optical tomography methods for detection of skin and lung cancers. J Biomed Opt 2015; 20(2): 025003. DOI: 10.1117/1.JBO.20.2.025003.
  17. Argenziano G, Fabbrocini G, Carli P, et al. Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions. Arch Dermatol 1998; 134: 1563-1570. DOI: 10.1001/archderm.134.12.1563.
  18. Machihin A, Batshev V, Pozhar V. Aberration analysis of AOTF-based spectral imaging systems. J Opt Soc Am A 2017; 34(7): 1109-1113. DOI: 10.1364/JOSAA.34.001109.
  19. Zherdeva LA, Bratchenko IA, Myakinin OO, et al. In vivo hyperspectral imaging and differentiation of skin cancer. Proc SPIE 2016; 10024: 100244G. DOI: 10.1117/12.2246433.
  20. Lihachev A, Derjabo A, Ferulova I, et al. Autofluorescence imaging of basal cell carcinoma by smartphone RGB camera. J Biomed Opt 2015; 20(12): 120502. DOI: 10.1117/1.JBO.20.12.120502.
  21. Lihacova L, Bolocko K, Lihachev A. Semi-automated non-invasive diagnostics method for melanoma differentiation from nevi and pigmented basal cell carcinomas. Proc SPIE 2017; 10592: 1059206. DOI: 10.1117/12.2295773.
  22. Neittaanmäki N, Salmivuori M, Pölönen I, et al. Hyperspectral imaging in detecting dermal invasion in lentigo maligna melanoma. Br J Dermatol 2017; 177: 1742-1744. DOI: 10.1111/bjd.15267.
  23. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542: 115-118. DOI: 10.1038/nature21056.
  24. Li Y, Shen L. Skin lesion analysis towards melanoma detection using deep learning network. Sensors 2018; 18: 556. DOI: 10.3390/s18020556.

© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: ko@smr.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20