RUS  ENG
Full version
JOURNALS // Optics and Spectroscopy // Archive

Optics and Spectroscopy, 2019 Volume 126, Issue 5, Pages 584–595 (Mi os713)

This article is cited in 9 papers

The 22nd Annual Conference Saratov Fall Meeting 2018 (SFM'18): VI International Symposium ''Optics and Biophotonics'' and XXII International School for Junior Scientists and Students on Optics, Laser Physics & Biophotonics
Biophotonics

Differentiation of pigmented skin neoplasms based on digital processing of optical images

E. N. Rimskayaa, A. O. Schadkoa, I. A. Apollonovaa, A. P. Nikolaeva, A. N. Brikoa, I. A. Deshina, P. U. Bereshnoyb, K. G. Kudrinc, K. I. Zaitsevade, V. V. Tuchinf, I. V. Reshetovd

a Bauman Moscow State Technical University
b Plekhanov Russian State University of Economics, Moscow
c Institute for Advanced Studies of the Federal Medical Biological Agency of Russia, Moscow
d I. M. Sechenov First Moscow State Medical University
e Prokhorov General Physics Institute of the Russian Academy of Sciences, Moscow
f Saratov State University

Abstract: A method for differentiation of pigmented skin neoplasms based on digital processing of optical images has been proposed. The optical images are detected using a digital camera and an etalon for its color and spatial calibration. The method implies segmentation and automatic differentiation of neoplasms based on 5 parameters –the diameter, area, color, shape and smoothness of margins of the neoplasm. It reveals clinical features of the neoplasm, required for diagnosis, and allows for calculating a probability of the neoplasms malignizaion. The proposed method has been verified using 360 imaging of pigmented neoplasms of the skin in vivo; among them: ordinary, dysplastic nevi of the skin and skin melanoma. The observed sensitivity and specificity of the proposed technique are 97% and 95%, correspondingly.

Received: 21.11.2018
Revised: 09.01.2019
Accepted: 31.01.2019

DOI: 10.21883/OS.2019.05.47657.6-19


 English version:
Optics and Spectroscopy, 2019, 126:5, 503–513

Bibliographic databases:


© Steklov Math. Inst. of RAS, 2026