Training Introduction to Digital Image Processing Techniques in The Medical Field

Rika Rosnelly, Linda Wahyuni, Hardianto Hardianto, Elsa Aditya

Abstract


Malaria is a health problem in Asia, especially in Indonesia. Malaria is an infectious disease, through the bite of a female Anopheles mosquito. Data sourced from the Ministry of Health of the Republic of Indonesia, there are 261,671 cases of malaria in Indonesia, one hundred of which claimed human lives. There are 28% in Indonesia living in malaria endemic areas for low, medium and highlands. Medical examination takes a long time, one example for examination of malaria parasites is with a microscope. The examination microscope has a hundred fields of view. Digital image processing is an image manipulation process to get better image results. The stages begin with ROI (Region of Interest), image improvement, segmentation with Otsu thresholding, extraction of shape and texture features and classification using LVQ. Digital image processing can help in the medical field, especially for disease identification.

Keywords


Digital Image Processing; Medical; Malaria;

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References


Zhang, J., Li, C., Rahaman, M. M., Yao, Y., Ma, P., Zhang, J., ... & Grzegorzek, M. (2021). A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches. Artificial Intelligence Review, 1-70.

Khalifa, N. E., Loey, M., & Mirjalili, S. (2021). A comprehensive survey of recent trends in deep learning for digital images augmentation. Artificial Intelligence Review, 1-27.

Pimple, K. M., Likhitkar, P. P., & Pande, S. (2022). Convolutional Neural Networks for Malaria Image Classification. In Proceedings of Data Analytics and Management (pp. 459-470). Springer, Singapore.

Khatkar, M., Atal, D. K., & Singh, S. (2021, August). Identification of Malaria Parasite Using Soft Computing Techniques. In 2021 Asian Conference on Innovation in Technology (ASIANCON) (pp. 1-7). IEEE.

Jusman, Y., Pusparini, A., Chamim, A. N. N., & Kanafiah, S. N. A. M. (2021, February). Comparison of Malaria Parasite Image Segmentation Algorithm Using Thresholding and Watershed Method. In Journal of Physics: Conference Series (Vol. 1783, No. 1, p. 012092). IOP Publishing.

Apriliani, A., Siregar, P. A., & Tarigan, A. A. (2021). Analysis of Risk Factors Malaria Incidence in Indonesia (data analysis of riskesdas 2018). International Archives of Medical Sciences and Public Health, 2(1).

Akindele, S. T., Bilesanmi–Awoderu, J. B., Otuewu, O. O., & Adetunji, A. M (2021). Comparative Analysis of Malaria Diagnosis Using Microscopy and Rapid Diagnostic Test (RDT) in Ijebu-Igbo North Local Government, Southwest Nigeria.

Manning, K., Zhai, X., & Yu, W. (2022). Image analysis and machine learning-based malaria assessment system. Digital Communications and Networks, 8(2), 132-142.

Kittichai, V., Kaewthamasorn, M., Thanee, S., Jomtarak, R., Klanboot, K., Naing, K. M. & Boonsang, S. (2021). Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks. Scientific reports, 11(1), 1-10.

Jabbar, M. A., & Radhi, A. M. (2022). Diagnosis of Malaria Infected Blood Cell Digital Images using Deep Convolutional Neural Networks. Iraqi Journal of Science, 380-396.

Abdurahman, F., & Fante, K. A. (2022). Tile-based microscopic image processing for malaria screening using deep learning approach.

Singh, M., Khurana, R., Jain, P., & Verma, A. Malaria Cell Detection Using Machine Learning.

Bashir, A., Mustafa, Z. A., Abdelhameid, I., & Ibrahem, R. (2017, January). Detection of malaria parasites using digital image processing. In 2017 International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE) (pp. 1-5). IEEE.




DOI: http://dx.doi.org/10.30700/jm.v3i1.1282

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