Development of a comprehensive method for the dermatoscopic images analysis of the facial skin with acne

Keywords: acne, post-acne, facial skin, dermatology, dermatoscopic imaging, color image segmentation, multi-Otsu method

Abstract

Background: One of the most common inflammatory chronic and recurrent skin diseases is acne (“acne vulgaris”), which appears itself as open or closed comedones and inflammatory skin lesions in the form of papules, pustules, nodes, etc. It has been established that acne is one of the most common dermatoses, since, according to modern data, it affects about 9.4% of the population. During adolescence, up to 90% of people suffer, and in adulthood — about 20% with varying degrees of severity. Currently, there are many approaches to treating this disease through various cosmetic treatments such as phototherapy, ultrasonic skin cleansing, Mesotherapy, chemical peels, and medication. Therefore, the development of methods and means of differential diagnosis of acne is one of the urgent tasks in the field of biomedical engineering, dermatology, and clinical medicine, since this allows timely identification of the localization of the disease, its causes, and prescribing appropriate treatment. However, the solution to the problem of monitoring the dynamics of external manifestations of the disease is possible only with the use of combined mathematical methods for image analysis.

Objectives: To develop a comprehensive method for analyzing dermatoscopic images for monitoring the external manifestations of acne disease during treatment and isolating the affected areas of the facial skin.

Materials and Methods: Dermatological preclinical researches of the skin were conducted in the laboratory of 3D-biomedical technologies of the Department of Biomedical Engineering of the Kharkiv National University of Radio Electronics, using a digital videodermatoscope BIO Bm6+ in daylight and a portable skin analyzer Skin Scope F-102 in the ultraviolet range. Clinical researches were conducted based on the Department of Pediatric Propaedeutics #2 of the Kharkiv National Medical University. The development of a software tool for image analysis was conducted out in Python programming using the libraries OpenCV, Scikit-image, Numpy, PIL, Mathplotlib. Determination of the affected skin areas and calculation of the parameters of inflammation were carried out using multi-Otsu methods and morphological segmentation of digital dermatoscopic images.

Results: During the research, automated software was developed that allows to analyze in dynamics the nature of inflammatory processes and the area of facial skin lesions, as well as to carry out a differential diagnosis of acne disease. The proposed method for the analysis of dermatoscopic images makes it possible to perform color segmentation and obtain a map of the gradations of skin inflammations to control the dynamics during the prescribed treatment.

Conclusions: The comprehensive method of analysis of dermatoscopic images of the skin of the face makes it possible to effectively control the condition of the skin of the face from acne during treatment, while analyzing the degree of inflammatory processes and the area of lesions, where, using the developed software, in an automated mode, red gradations are calculated to detect the boundaries of inflammation, geometric parameters and percentage of lesions in relation to healthy facial skin.

Downloads

Download data is not yet available.

Author Biographies

A. A. Trubitcin, Kharkiv National University of Radio Electronics, 14, Nauky Ave., Kharkiv, 61166, Ukraine

Assistant of the Department of Biomedical Engineering, Kharkiv National University of Radio Electronics

O. G. Avrunin, Kharkiv National University of Radio Electronics, 14, Nauky Ave., Kharkiv, 61166, Ukraine

Head of the Department of Biomedical Engineering, Member of EMC, Member of STC Presidium, Deputy Head of Section 4 of STC, Deputy Head of Specialized Scientific Council, Member of the Audit Commission of the NURE Alumni Association, Sports Medicine and Physical Rehabilitation Educational and Scientific Laboratory Scientific adviser, Doctor of technical sciences, Professor

References

Suva МА, Patel АМ, Sharma N, Bhattacharya C, Mangi RK. A brief Review on Acne Vulgaris: Pathugenesis, Diahnosis and Treatment. Research & Reviews: Journal of Pharmacology. 2014;4(3):1–12.

Akman A, Durusoy C, Senturk M, Koc CK, Soyturk D, Alpsoy E. Treatment of acne with intermittent and conventional isotretinoin: a randomized, controlled multicenter study. Arch Dermatol Res. 2007;299(10):467–73. https://doi.org/10.1007/s00403-007-0777-2

Capitanio B, Sinagra JL, Bordignon V, Cordiali Fei P, Picardo M, Zouboulis CC. Underestimated clinical features of postadolescent acne. J Am Acad Dermatol. 2010;63(5):782–8. https://doi.org/10.1016/j.jaad.2009.11.021

Bowe WP, Shalita AR. Effective over-the-counter acne treatments. Semin Cutan Med Surg. 2008;27(3):170–6. https://doi.org/10.1016/j.sder.2008.07.004

Tan AU, Schlosser BJ, Paller AS. A review of diagnosis and treatment of acne in adult female patients. Int J Womens Dermatol. 2018 Jun;4(2):56–71. https://doi.org/10.1016/j.ijwd.2017.10.006

Avrunin O, Trubitcin A, Klymenko V. The method for predictive assessment of the condition of patients with atopic dermatitis at different stages of the disease. Innovative Technologies and Scientific Solutions for Industries. 2021;2(16):63–71. https://doi.org/10.30837/ITSSI.2021.16.063

Avrunin O, Trubitcin A, Isaeva O, Klymenko V. Possibilities for assessing the effectiveness of treatment of atopic dermatitis based on analysis of color characteristics of video dermatoscopic images. Innovative Technologies and Scientific Solutions for Industries. 2020;2(12):127–33. https://doi.org/10.30837/2522-9818.2020.12.127

Krylova EV, Krylov AV. Assessment of the functional state of the skin using autofluorescence dermoscopy. Scientific notes of St. acad. I.P. Pavlova. 2013;20(1):62–5. https://doi.org/10.30837/ITSSI.2021.16.063

Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern. 1979;9(1):62–6. https://doi.org/10.1109/TSMC.1979.4310076

Barron JT. A generalization of Otsu's method and minimum error thresholding. In: Vedaldi A, Bischof H, Brox T, Frahm JM, editors. Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12350. Springer, Cham. https://doi.org/10.1007/978-3-030-58558-7_27

Ping-Sung Liao, Tse-Sheng Chen, Pau-Choo Chung. A Fast Algorithm for Multilevel Thresholding. J. Inf. Sci. Eng. 2001;17(5):713–27. https://doi.org/10.6688/JISE.2001.17.5.1

Liu W, Shi H, He X, Pan S, Ye Z, Wang Y. An application of optimized Otsu multi-threshold segmentation based on fireworks algorithm in cement SEM image. Journal of Algorithms & Computational Technology. 2019;13:1–12. https://doi.org/10.1177/1748301818797025

Igarashi T, Nishino K, Nayar SK. The Appearance of Human Skin: A Survey. Foundation and Trends in Computer Graphics and Vision. 2007;3(1):1-95. https://doi.org/10.1561/0600000013

Bhandari AK, Kumar A, Singh GK. Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur’s, Otsu and Tsallis functions. Expert Systems with Applications. 2015;42(3):1573–1601. https://doi.org/10.1016/j.eswa.2014.09.049

Ramadevi Y, Sridevi T, Poornima B, Kalyani B. Segmentation and Object Recognition using edge detection techniques. International Journal of Computer Science & Information Technology (IJCSIT). 2010;2(6):153–61. https://doi.org/10.5121/ijcsit.2010.2614

Mutneja D, Mutneja V. Methods of Image Edge Detection: A Review. J Electr Electron Syst. 2015;4(2):150. https://doi.org/10.4172/2332-0796.1000150

Yuncong Feng, Haiying Zhao, Xiongfei Li, Xiaoli Zhang, Hongpeng Li. A multi-scale 3D Otsu thresholding algorithm for medical image segmentation. Digital Signal Processing. 2017;60:186–99. https://doi.org/10.1016/j.dsp.2016.08.003

Sarkar S, Das S, Chaudhuri SS. A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution. Pattern Recognition Letters. 2015;54:27–35. https://doi.org/10.1016/j.patrec.2014.11.009

Tymkovych M, Gryshkov O, Avrunin O, Selivanova K, Nosova Y, Mutsenko V, et all. Application of SOFA Framework for Physics-Based Simulation of Deformable Human Anatomy of Nasal Cavity. In: Jarm T, Cvetkoska A, Mahnič-Kalamiza S, Miklavcic D. (eds) 8th European Medical and Biological Engineering Conference. EMBEC 2020. IFMBE Proceedings, vol. 80. Springer, Cham. https://doi.org/10.1007/978-3-030-64610-3_14

Heijmans HJAM. Mathematical morphology: A modern approach in image processing based on algebra and geometry. SIAM Review. 1995;37(1):1-36. https://doi.org/10.1137/1037001

Tymkovych M, Gryshkov O, Selivanova K, Mutsenko V, Avrunin O, Glasmacher B. Application of artificial neural networks for analysis of ice recrystallization process for cryopreservation. In: Jarm, T., Cvetkoska, A., Mahnič-Kalamiza, S., Miklavcic, D. (eds) 8th European Medical and Biological Engineering Conference. EMBEC 2020. IFMBE Proceedings, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-64610-3_13

Li X, Zhou Z, Keller P, Zeng H, Liu T, Peng H. Interactive exemplar-based segmentation toolkit for biomedical image analysis. IEEE 12th International Symposium on Biomedical Imaging (ISBI); 2015; Brooklyn. p. 168–71. https://doi.org/10.1109/ISBI.2015.7163842

Avrunin OG, Tymkovych MY, Moskovko SP, Romanyuk SO, Kotyra A, Smailova S. Using a priori data for segmentation anatomical structures of the brain. Przeglad Elektrotechniczny. 2017;93(5):102–5. https://doi.org/10.15199/48.2017.05.20

Avrunin O, Tymkovych M, Drauil J. Automatized technique for three-dimensional reconstruction of cranial implant based on symmetry, Proceedings of the Information Technologies in Innovation Business Conference (ITIB). 2015. p. 39–42. https://doi.org/10.15199/48.2017.05.20

Selivanova KG, Avrunin OG, Tymkovych MY, Manhora TV, Oleh S, Bezverkhyi OS, et al. 3D visualization of human body internal structures surface during stereo-endoscopic operations using computer vision techniques. Przeglad Elektrotechniczny. 2021;97(9):30–3. https://doi.org/10.15199/48.2021.09.06

Published
2022-06-01
Cited
How to Cite
Selivanova, K. G., Trubitcin, A. A., & Avrunin, O. G. (2022). Development of a comprehensive method for the dermatoscopic images analysis of the facial skin with acne. Biophysical Bulletin, (46), 34-45. https://doi.org/10.26565/2075-3810-2021-46-03
Section
Biomedical engineering