skip to main content

A Review on Digital Microscopic Images for Plasmodium Parasite Detection

A Review on Digital Microscopic Images for Plasmodium Parasite Detection

Sapto Nisworo  -  Department of Electrical Engineering, Universitas Tidar, Indonesia
*Alifia Revan Prananda  -  Department of Information Technology, Universitas Tidar, Indonesia
Open Access Copyright (c) 2023 TEKNIK

Citation Format:
Abstract
Indonesia is one of the regions that contribute to the increasing number of malaria cases. In 2019, more than 250 million malaria cases were found in Indonesia. This phenomenon is caused by several factors, including the examination procedure. In Indonesia, the digital microscopic examination has become the gold standard procedure in detecting and diagnosing malaria, whereas this procedure requires considerable expertise. Hence, the rapid examination is difficult to ensure. In order to overcome this problem, several methods of malaria detection have been proposed with a different approach. Image processing and computer vision techniques have become a powerful approach in the development of early detection systems called computer-aided detection (CADe) and computer-aided diagnosis (CADx). Several previous findings reported their contributions in detecting Plasmodium parasites using image processing and computer vision. Recently, artificial intelligence, including machine learning and deep learning, also offered outstanding results in detecting the Plasmodium parasite. This paper aims to present a scientific review of recent image processing and computer vision applications for the development of CADe or CADx in order to assist the doctor in doing rapid detection and diagnosis.
Fulltext View|Download
Keywords: CAD; early detection; malaria; medical image analysis; Plasmodium parasite

Article Metrics:

  1. Bates, I., Bekoe, V., & Asamoa-Adu, A. (2004). Improving the accuracy of malaria-related laboratory tests in Ghana. Malaria Journal, 3, 38. https://doi.org/10.1186/1475-2875-3-38
  2. Bias, S., Reni, S. K., & Kale, P. (2018). Mobile Hardware Based Implementation of a Novel, Efficient, Fuzzy Logic Inspired Edge Detection Technique for Analysis of Malaria Infected Microscopic Thin Blood Images. Procedia Computer Science, 141, 374–381. https://doi.org/10.1016/j.procs.2018.10.187
  3. BioGPS. (n.d.). Malaria Dataset. Retrieved September 11, 2019, from BioGPS Dataset Library website: http://biogps.org/dataset/tag/malaria/
  4. Coleman, R. E., Maneechai, N., Rachaphaew, N., Kumpitak, C., Miller, R. S., Soyseng, V., … Sattabongkot, J. (2002). Comparison of field and expert laboratory microscopy for active surveillance for asymptomatic Plasmodium falciparum and Plasmodium vivax in western Thailand. The American Journal of Tropical Medicine and Hygiene, 67(2), 141–144. https://doi.org/10.4269/ajtmh.2002.67.141
  5. Dave, I. R., & Upla, K. P. (2017). Computer aided diagnosis of Malaria disease for thin and thick blood smear microscopic images. 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN), 561–565. https://doi.org/10.1109/SPIN.2017.8050013
  6. Devi, S. S., Singha, J., Sharma, M., & Laskar, R. H. (2017). Erythrocyte segmentation for quantification in microscopic images of thin blood smears. Journal of Intelligent & Fuzzy Systems, 32, 2847–2856. https://doi.org/10.3233/JIFS-169227
  7. Dharun. (n.d.). Malaria BBBC. Retrieved September 11, 2019, from Kaggle Dataset website: https://www.kaggle.com/kmader/malaria-bounding-boxes#002f20ad-2ace-499c-9335-c9080bc3e6b5.png
  8. G, D. M. (2019). A Novel Algorithm for Segmentation of Parasites in Thin Blood Smears From Microscopy Using Type II Fuzzy Sets and Inverse Gaussian Gradient. International Journal of Computer Vision and Image Processing, 9, 1–22. https://doi.org/10.4018/IJCVIP.2019070101
  9. Gatc, J., & Maspiyanti, F. (2018). Plasmodium Parasite Detection on Thin Blood Smear Image using Double Thresholding and BLOB Analysis. 2018 International Conference on Applied Engineering (ICAE), 1–6. https://doi.org/10.1109/INCAE.2018.8579407
  10. GEO DataSets. (n.d.). Plasmodium Dataset. Retrieved September 11, 2019, from Gene Expression Omnibus (GEO) Datasets website: https://www.ncbi.nlm.nih.gov/gds
  11. González-Betancourt, A., Rodríguez-Ribalta, P., Meneses-Marcel, A., Sifontes-Rodríguez, S., Lorenzo-
  12. Ginori, J. V., & Orozco-Morales, R. (2017). Automated marker identification using the Radon transform for watershed segmentation. IET Image Processing, 11(3), 183–189. https://doi.org/https://doi.org/10.1049/iet-ipr.2016.0525
  13. Haryanto, S. E. V, Mashor, M. Y., Nasir, A. S. A., & Jaafar, H. (2017). A fast and accurate detection of Schizont plasmodium falciparum using channel color space segmentation method. 2017 5th International Conference on Cyber and IT Service Management (CITSM), 1–4. https://doi.org/10.1109/CITSM.2017.8089290
  14. Hegde, R., Prasad, K., Hebbar, H., & Singh, B. M. (2018). Development of a Robust Algorithm for Detection of Nuclei and Classification of White Blood Cells in Peripheral Blood Smear Images. Journal of Medical Systems, 42. https://doi.org/10.1007/s10916-018-0962-1
  15. Jalari, S., & Reddy, B. E. (2017). A Novel Two-Stage Thresholding Method for Segmentation of Malaria Parasites in Microscopic Blood Images. British Journal of Healthcare and Medical Research, 4(2), 31. https://doi.org/10.14738/jbemi.42.2986
  16. Kettelhut, M. M., Chiodini, P. L., Edwards, H., & Moody, A. (2003). External quality assessment schemes raise standards: evidence from the UKNEQAS parasitology subschemes. Journal of Clinical Pathology, 56(12), 927–932. https://doi.org/10.1136/jcp.56.12.927
  17. Kumawat, N. (n.d.). Cell Image for Malaria Detection. Retrieved September 11, 2019, from Kaggle Dataset website: https://www.kaggle.com/thenandkishorkumawat/cell imagesfordetectingmalaria
  18. Loddo, A., Di Ruberto, C., & Kocher, M. (2018). Recent Advances of Malaria Parasites Detection Systems Based on Mathematical Morphology. Sensors (Basel, Switzerland), 18(2). https://doi.org/10.3390/s18020513
  19. Mader, K. (n.d.). Malaria Bounding Boxes. Retrieved September 11, 2019, from Kaggle Dataset website: https://www.kaggle.com/datasets/kmader/malaria-bounding-boxes
  20. National Library of Medicine. (n.d.). Malaria Dataset. https://doi.org/10.1001/jama.1976.03260320018013
  21. Nugroho, H. A., Darojatun, A., Ardiyanto, I., & Buana, R. (2018). Classification of Plasmodium Malariae dan Plasmodium Ovale in Microscopic Thin Blood Smear Digital Images. International Journal on Advanced Science, Engineering and Information Technology, 8, 2301–2307. https://doi.org/10.18517/ijaseit.8.6.6514
  22. Nugroho, H. A., Dendi Maysanjaya, I. M., Setiawan, N. A., Murhandarwati, E. E. H., & Oktoeberza, W. K. Z. (2019). Feature analysis for stage identification of Plasmodium vivax based on digital microscopic image. Indonesian Journal of Electrical Engineering and Computer Science, 13(2), 721–728. https://doi.org/10.11591/ijeecs.v13.i2.pp721-728
  23. Nugroho, H. A., Satria Wibawa, M., Setiawan, N. A., Murhandarwati, E., & Buana, R. (2019). Identification of Plasmodium falciparum and Plasmodium vivax on digital image of thin blood films. Indonesian Journal of Electrical Engineering and Computer Science, 13, 933–944. https://doi.org/10.11591/ijeecs.v13.i3.pp933-944
  24. Oliveira, A. D., Prats, C., Espasa, M., Zarzuela Serrat, F., Montañola Sales, C., Silgado, A., … Albuquerque, J. (2017). The Malaria System MicroApp: A New, Mobile Device-Based Tool for Malaria Diagnosis. JMIR Research Protocols, 6(4), e70. https://doi.org/10.2196/resprot.6758
  25. Pattanaik, P. A., Swarnkar, T., & Sheet, D. (2017). Object detection technique for malaria parasite in thin blood smear images. 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2120–2123. https://doi.org/10.1109/BIBM.2017.8217986
  26. Romero, M., Sanabria, M., Bautista, L., & Mendoza, A. (2016). Algorithm for detection of overlapped red blood cells in microscopic images of blood smears. Dyna (Medellin, Colombia), 83, 188–195. https://doi.org/10.15446/dyna.v83n198.47177
  27. Rosado, L., Correia da Costa, J. M., Elias, D., & Cardoso, J. (2016). Automated Detection of Malaria Parasites on Thick Blood Smears via Mobile Devices. Procedia Computer Science, 90, 138–144. https://doi.org/10.1016/j.procs.2016.07.024
  28. Rosado, L., da Costa, J. M. C., Elias, D., & Cardoso, J. S. (2017). Mobile-based analysis of malaria-infected thin blood smears: Automated species and life cycle stage determination. Sensors (Switzerland), 17(10), 1–22. https://doi.org/10.3390/s17102167
  29. Somasekar, J., & Eswara Reddy, B. (2015). Segmentation of erythrocytes infected with malaria parasites for the diagnosis using microscopy imaging. Computers & Electrical Engineering, 45, 336–351. https://doi.org/https://doi.org/10.1016/j.compeleceng.2015.04.009
  30. Tek, F. B., Dempster, A. G., & Kale, İ. (2010). Parasite detection and identification for automated thin blood film malaria diagnosis. Computer Vision and Image Understanding, 114(1), 21–32. https://doi.org/https://doi.org/10.1016/j.cviu.2009.08.003

Last update:

No citation recorded.

Last update: 2024-04-15 15:09:54

No citation recorded.