Quantitative Analysis of Eukaryotic Protist Cells Using Image Processing with Epson Perfection V750 Pro & MATLAB

Yuzo Iano¹ , Daniel Katz Bonello² , Umberto Bonello Neto³

Section:Research Paper, Product Type: Journal-Paper
Vol.10 , Issue.3 , pp.9-20, Jun-2023

Online published on Jun 30, 2023

Copyright © Yuzo Iano, Daniel Katz Bonello, Umberto Bonello Neto . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

Abstract :
Biomedical engineering is the link between engineering health sciences, and society. In this way, the biomedical engineer works in the development maintenance and assembly of equipment and programs to carry out diagnoses and treatments carried out by health professionals, such as: physicians, biomedical engineers, and dentists. In addition, can also manage the area of equipment purchases and develop scientific research on biomedical materials and instruments. Among the places where this professional works are: hospitals, medical clinics, health centers, pharmaceutical and clinical analysis laboratories, companies specialized in hospital maintenance and research centers. Therefore, the aim of this article is to propose a quantitative analysis of eukaryotic protist cells using image processing with a microscopy – Epson Perfection V750 and MATLAB-R2020 image analysis code. Thus, as results, were obtained a dataset of 14 eukaryotic detected cells samples using the MATLAB-R2020, and then a measurement pattern was utilized to quantify these 14 patterns, according the following premises: the sample number (from “sample 1” to “sample 14”); number of detected circles in eukaryotic cells images; sensitivity position of detection; edge threshold position; utilized method (phase code); object polarity (bright); and radius range (from 10µm to 30µm). As final results, the maximum numbers of detected circles were 287, and the minimum number of detected circles was 1 detection: utilizing a threshold of 0.3. Now utilizing a threshold form 0.2 to 0.94 (percentage difference of 370%), the maximum number of detected circles were 218 elements and the minimum number of detected circles were 7 elements (percentage difference about 3,000%). In conclusion, this experiment has obtained a better detection of circular elements in eukaryotic protist cells when the sensitivity position is seted at 0.99 and the edge threshold position is about 0.3: within phase code method, bright object polarity and radius range between 10µm and 30µm. These values were obtained using MATLAB-R2020.

Key-Words / Index Term :
Eukaryotic cells, MATLAB, image processing, pattern recognition, quantitative analysis, spectrometry.

References :
[1]. SBEB (Sociedade Brasileira de Engenharia Biomédica). "Engenharia Biomédica: o elo entre engenharia, ciências da saúde e sociedade". Rio de Janeiro – Brazil, 2017, pp.01-03, 2017.
[2]. LAM, W. “Perspectivas da biotecnologia em 2022: Após um ano difícil, a biotecnologia tem o potencial de despertar em 2022," Franklin Equity Group. São Paulo – Brazil, pp.03-06, 2021.
[3]. BIOPHARMA TREND. “AI Drug Discovery: Key Trends and Developments in Pharmaceutical Industry”. Brentford – England. pp.05-07, 2023.
[4]. MORDOR INTELLIGENCE. “Mercado de descoberta de medicamentos – crescimento, tendências, impactos do COVID-19 e previsões (2023 – 2028)”. Telangana – Índia, pp.01-06, 2023.
[5]. QIAO, L. et. al. “A circuit for secretion-coupled cellular autonomy in multicellular eukaryotic cells,” Molecular Systems Biology, Vol. 19, Issue 12, pp.1-27, 2023.
[6]. BARREIRO, E.J. et. al. “Biodiversidade: fonte potencial para a descoberta de fármacos,” Quim. Nova, Vol. 32, No. 3, pp.679-688, 2009.
[7]. BHAVANI, M. et. al. “Streamlined Classification of Microscopic Blood Cell Images,” International Journal of Intelligent Systems and Applications in Engineering, Vol. 11, No. 1, pp. 57-62, 2023.
[8]. MATSUURA, K. et. al. “Blood Cell Separation Using Polypropylene-Based Microfluidic Devices Based on Deterministic Lateral Displacement,” Micromachines, Vol. 14, No. 238, pp.1-13, 2023.
[9]. MERZOUG, A. et. al. “Lesions Detection of Multiple Sclerosis in 3D Brian MR Images by Using Artificial Immune Systems,” International Journal of Cognitive Informatics and Natural Intelligence and Support Vector Machines, Vol. 15, No. 2, pp.1-14, 2021.
[10]. SARKAR, C. et. al. “Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development,” International Journal of Molecular Sciences, Vol. 24, No. 2026, pp.1-40, 2023.
[11]. SOUDAH, T. et. al. “Desorption electrospray ionization mass spectrometry imaging in discovery and development of novel therapies,” WILEY – Mass Spec, Vol. 42, No. 1, pp.751-778, 2021.
[12]. RASHID, P.Q. “Sars COV-2 CT-Scan Image Classification Using Graph Convolutional Networks,” International ?stabul Modern Scientific Research Congress – IV, pp.160-165, 2022.
[13]. HASAN, M. et. al. “Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment,” MDPI Sensors, Vol. 23, No. 527, pp.1-35, 2023.
[14]. MIRSHAFIEI, M. et. al. “Application of MIA-QSAR in Designing New Protein P38 MAP Kinase Compounds Using a Genetic Algorithm,” Periodica Polytechnica Chemical Engineering, Vol. 67, No. 1, pp.141-160, 2023.
[15]. LOPES, S. “Bio: volume único – 1. ed.,” Saraiva. São Paulo – Brazil, pp.38-126, 2004.
[16]. IANO, Y. et. al. “Analysis of Results of Some Techniques for the Recognition of Circular Shapes in the Steel Bar Counting System Using Image Processing,” Proceedings of the 6th Brazilian Technology Symposium (BTSym’20), Vol. 6, No. 1, pp.1-11, 2021.
[17]. HART, P.E. et. al. “How the Hough transform was invented [DSP History],” IEEE Signal Process. Mag, Vol. 26, No. 1, pp. 18–22, 2009.
[18]. SHAPIRO, L. et. al. “Computer Vision,” Prentice Hall. United States, pp.608-615, 2001.
[19]. X. CHEN, L. LU, AND Y. GAO, “A new concentric circle detection method based on Hough transform,” ICCSE 2012 - Proc. 2012 7th Int. Conf. Comput. Sci. Educ., no. Iccse, pp. 753–758, 2012.
[20]. CELL IMAGE LIBRARY. “CIL Image Viewer – Open source,” UCSD. United States, pp.01-14, 2023.
[21]. P. SHARNAGAT, K.P. JAISWAL. “Assessment of Enteropathogens from Fresh Vegetables Associated Serious Risks to Human Health,” International Journal of Scientific Research in Biological Sciences, Vol. 10, No. 1, pp.12-17, 2023.
[22]. FERRARI, I.V. et. al. “Molecular Docking studies against Glioblastoma Proteins,” International Journal of Scientific Research in Biological Sciences, Vol. 10, No. 2, pp.19-23, 2023.