Some Problematic Aspects of Coliform Bacteria Clustering on Medical Images in the Task of Identifying Possible Diseases
Article Sidebar
Coliform Bacteria,
Medical Images,
Image Processing
Main Article Content
Abstract
Medical image analysis methods are one of the sources for obtaining additional information about the investigated phenomena. We are looking at images of coliform bacteria. Analysis of these images allows you to determine the possibility of developing certain diseases. To do this, it is necessary to cluster the set of bacteria and count the bacteria. The paper highlights the features of clustering for coliform bacteria. Clustering results for real data are presented.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
Babker, A. M., & Lyashenko, V. (2020). Wavelet Analysis as Research Tool Image Cytological Preparations. J Clin Exp Pathol, 10, 382.
Cernicchiaro, N., Oliveira, A. R., Hoehn, A., Cull, C. A., Noll, L. W., Shridhar, P. B., ... & Sanderson, M. W. (2019). Quantification of bacteria indicative of fecal and environmental contamination from hides to carcasses. Foodborne Pathogens and Disease, 16(12), 844-855.
Goel, N., Yadav, A., & Singh, B. M. (2016). Medical image processing: a review. In 2016 Second International Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity (CIPECH), 57-62.
Hiremath, M. (2019). Segmentation and Recognition of E. coli Bacteria Cell in Digital Microscopic Images Based on Enhanced Particle Filtering Framework. In Emerging Research in Computing, Information, Communication and Applications, 503-512.
Jeon, D. J., Ligaray, M., Kim, M., Kim, G., Lee, G., Pachepsky, Y. A., ... & Cho, K. H. (2019). Evaluating the influence of climate change on the fate and transport of fecal coliform bacteria using the modified SWAT model. Science of the Total Environment, 658, 753-762.
Karthick, S., & Maniraj, S. (2019). Different Medical Image Registration Techniques: A Comparative Analysis. Current Medical Imaging, 15(10), 911-921.
Kis, B., Unay, M., Ekimci, G. D., Ercan, U. K., & Akan, A. (2019). Counting Bacteria Colonies Based on Image Processing Methods. In 2019 Medical Technologies Congress (TIPTEKNO), 1-4.
Lyashenko, V. V., Babker, A. M., & Kobylin, O. A. (2016). The methodology of wavelet analysis as a tool for cytology preparations image processing. Cukurova Medical Journal, 41(3), 453-463.
Lyashenko, V., Babker, A. M. A. A., & Kobylin, O. (2016). Using the methodology of wavelet analysis for processing images of cytology preparations. National Journal of Medical Research, 6(1). 98-102.
Lyashenko, V., Sinelnikova, T., Zeleniy, O., & Babker, A. M. A. (2020). Specifics of Using Image Processing Techniques for Blood Smear Analysis. Journal La Medihealtico, 1(6), 1-6.
Merjulah, R., & Chandra, J. (2017). Segmentation technique for medical image processing: A survey. In 2017 International Conference on Inventive Computing and Informatics (ICICI), 1055-1061.
Orobinskyi, P., & Lyashenko, V. (2020). The Task of Identifying the Various Objects’ Boundary Points of the Medical Image as the Optimal Path Search on the Graph. International Journal of Emerging Trends in Engineering Research, 8(3), 824-829.
Orobinskyi, P., Deineko, Z., & Lyashenko, V. (2020). Comparative Characteristics of Filtration Methods in the Processing of Medical Images. American Journal of Engineering Research, 9(4), 20-25.
Orobinskyi, P., Petrenko, D., & Lyashenko, V. (2019). Novel Approach to Computer-Aided Detection of Lung Nodules of Difficult Location with Use of Multifactorial Models and Deep Neural Networks. In 2019 IEEE 15th International Conference on the Experience of Designing and Application of CAD Systems (CADSM),1-5.
Połap, D., & Woźniak, M. (2019). Bacteria shape classification by the use of region covariance and convolutional neural network. In 2019 International Joint Conference on Neural Networks (IJCNN), 1-7.
Rabotiahov, A., Kobylin, O., Dudar, Z., & Lyashenko, V. (2018). Bionic image segmentation of cytology samples method. In 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET), 665-670.
Razzak, M. I., Naz, S., & Zaib, A. (2018). Deep learning for medical image processing: Overview, challenges and the future. In Classification in BioApps, 323-350.
Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual review of biomedical engineering, 19, 221-248.
Wang, H., Koydemir, H. C., Qiu, Y., Bai, B., Zhang, Y., Jin, Y., ... & Ozcan, A. (2020). Early-detection and classification of live bacteria using time-lapse coherent imaging and deep learning. arXiv preprint arXiv:2001.10695.
Wang, J., & Deng, Z. (2019). Modeling and predicting fecal coliform bacteria levels in oyster harvest waters along Louisiana Gulf coast. Ecological Indicators, 101, 212-220.