Development and analysis of an algorithm for detecting multiple instances of an object in microscopic images using numerical methods

Cover Page

Cite item

Full Text

Abstract

This paper presents a method for object detection in microscopy images, focusing on particle detection. The main objective of the research is to develop an algorithm capable of efficiently detecting multiple instances of objects in various scenarios, while maintaining specificity for structures of interest. The algorithm is based on using extremal regions as candidates for detection, followed by evaluating these regions with trained parameters. A key element of the algorithm is its built-in non-overlapping constraint, which enables effective handling of particle clustering. Experimental results on various microscopy datasets confirm the method's robustness to changes in image intensity, particle density, and size. The proposed algorithm serves as a valuable tool in the development of object detection methods for microscopy images and can be applied in both scientific and medical research.

About the authors

Sergey Yu. Ganigin

Samara State Technical University

ORCID iD: 0000-0001-5778-6516
SPIN-code: 5725-6961
Russia, 443100, Samara St. Molodogvardeyskaya, 244

Andrey N. Davydov

Samara State Technical University

ORCID iD: 0000-0002-7061-5460
SPIN-code: 7434-7987
Scopus Author ID: 7201949562
ResearcherId: D-7828-2014
Russia, 443100, Samara St. Molodogvardeyskaya, 244

Alexander S. Nechaev

Samara State Technical University

ORCID iD: 0000-0002-0939-8292
SPIN-code: 4564-7570
Scopus Author ID: 57216884784
Russia, 443100, Samara St. Molodogvardeyskaya, 244

Victoria Vitalievna Kiyashchenko

Samara State Technical University

ORCID iD: 0000-0001-9710-2860
SPIN-code: 6752-8232
Russia, 443100, Samara St. Molodogvardeyskaya, 244

References

  1. Puchkov E. Image analysis in microbiology: A review // Journal of Computer and Communications. 2016. Vol. 4, iss. 15. P. 8–32. https://doi.org/10.4236/jcc.2016.415002
  2. Spahn C., Gomez-de-Mariscal E., Laine R. F., Pereira P. M., Chamier L., Conduit M., Pinho M. G., Jacquemet G., Holden S., Heilemann M., Henriques R. DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches // Communications Biology. 2022. Iss. 5. Art. 688. https://doi.org/10.1038/s42003-022-03634-z
  3. Su P.-T., Liao C.-T., Roan J.-R., Wang S.-H., Chiou A., Syu W.-J. Bacterial colony from two-dimensional division to three-dimensional development // PloS ONE. 2023. Vol. 7, iss. 11. Art. e48098. https://doi.org/10.1371/journal.pone.0048098
  4. Замятин Д. А., Поротников А. В., Щапова Ю. В., Вотяков С. Л. JPD-анализ микроскопических изображений в исследовании структурно-химической неоднородности зерен природного циркона // Минералы: строение, свойства, методы исследования. 2014. Вып. 6. С. 26–27. EDN: YMSLCU
  5. Полищук С. В., Смехун Я. А. Анализ элекронно-микроскопических изображений с использованием спектральных характеристик // Международный научно-исследовательский журнал. 2014. Вып. 4–1 (23). С. 65–67. EDN: SCQPCB
  6. Беляева Л. А., Шурыгина О. В., Юхимец С. Н., Петрова А. А., Миронов С. Ю., Ратенкова Н. В., Кулакова О. В., Бовтунова С. С. Автоматизированный и ручной анализ спермы: сравнительная характеристика // Морфологические ведомости. 2022. Т. 30, вып. 4. С. 9–15. https://doi.org/10.20340/mv-mn.2022.30(4).704, EDN: JGHSAW
  7. Wei X., Liu Yo., Song Q., Zou J., Wen Zh., Li J., Jie D. Microscopic hyperspectral imaging and an improved detection model based detection of Mycogone perniciosa chlamydospore in soil // European Journal of Agronomy. 2024. Vol. 152. Art. 127007. https://doi.org/10.1016/j.eja.2023.127007
  8. Khan S., Sajjad M., Abbas N., Escorcia-Gutierrez J., Gamarra M., Khan M. Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network // Computers in Biology and Medicine. 2024. Vol. 174. Art. 108146. https://doi.org/10.1016/j.compbiomed.2024.108146
  9. Han Zh., Huang H., Lu D., Fan Q., Ma Ch., Chen X., Gu Q., Chen Q. One-stage and lightweight CNN detection approach with attention: Application to WBC detection of microscopic images // Computers in Biology and Medicine. 2023. Vol. 154. Art. 106606. https://doi.org/10.1016/j.compbiomed.2023.106606
  10. Ван Ц. И., Воронов В. И. Анализ результатов компьютерной томографии головного мозга с помощью сверточной нейронной сети // DSPA: Вопросы применения цифровой обработки сигналов. 2020. Т. 10, вып. 1. С. 32–40. EDN: OFBSSY
  11. Sun M., Wang W., Zhu X., Liu J. Reparameterizing and dynamically quantizing image features for image generation // Pattern Recognition. 2024. Vol. 146. Art. 109962. https://doi.org/10.1016/j.patcog.2023.109962
  12. Pawlowski J., Majchrowska S., Zhu X., Golan T. Generation of microbial colonies dataset with deep learning style transfer // Scientific Reports. 2022. Vol. 12. Art. 5212. https://doi.org/10.1038/s41598-022-09264-z
  13. Panic B., Borovinsek M., Vesenjak M., Oman S., Nagode M. A guide to unsupervised image segmentation of mCT-scanned cellular metals with mixture modelling and Markov random fields // Materials and Design. 2024. Vol. 239. Art. 112750. https://doi.org/10.1016/j.matdes.2024.112750
  14. Franti P., Mariescu-Istodor R. Soft precision and recall // Pattern Recognition Letters. 2023. Vol. 167. P. 115–121. https://doi.org/10.1016/j.patrec.2023.02.005

Supplementary files

Supplementary Files
Action
1. JATS XML
Download


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

Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).