White blood cell segmentation by using vector color approach
DOI:
https://doi.org/10.58524/app.sci.def.v1i1.158Keywords:
Smear image, White blood cell, Image segmentation, VectorAbstract
White blood cells play an important role in protecting the body from various types of diseases, therefore blood tests are done to determine a person’s disease. Diagnosis of blood diseases is done by analyzing and counting the white blood cells in the blood. Optimal calculations and analysis of white blood cell are important, so research on white blood cell detection is a rapidly growing topic. Many detection methods are proposed using the existing segmentation techniques. In this paper, the detection method is proposed using vector operation approach instead general segmentation technique. From the experiments performed, it is shown that the proposed method can detect white blood cells on smear images. Further, this method extracts the nucleus from white blood cells well.
References
Abdurrazzaq, A., Junoh, A. K., Yahya, Z., & Mohd, I. (2021). New white blood cell detection technique by using singular value decomposition concept. Multimedia Tools and Applications, 80(3), 4627–4638. https://doi.org/10.1007/s11042-020-09946-8
Anilkumar, K. K., Manoj, V. J., & Sagi, T. M. (2020). A survey on image segmentation of blood and bone marrow smear images with emphasis to automated detection of Leukemia. Biocybernetics and Biomedical Engineering, 40(4), 1406–1420. https://doi.org/10.1016/j.bbe.2020.08.010
Bain, B. J. (2017). Structure and function of red and white blood cells. Medicine, 45(4), 187–193. https://doi.org/10.1016/j.mpmed.2017.01.011
Buxhofer‐Ausch, V., Wolf, D., Sormann, S., Forjan, E., Schimetta, W., Gisslinger, B., Heibl, S., Krauth, M. T., Thiele, J., Ruckser, R., & Gisslinger, H. (2021). Impact of platelets on major thrombosis in patients with a normal white blood cell count in essential thrombocythemia. European Journal of Haematology, 106(1), 58–63. https://doi.org/10.1111/ejh.13516
Hardiyanti, C., Santosa, P. E., Muhtarudin, M., Suharyati, S., & Tantalo, S. (2023). Gambaran darah (sel darah merah, hemoglobin, dan pcv) pada ayam kampung jantan dengan pemberian kombinasi vitamin e, selenium, dan zinc melalui air minum. Jurnal Riset Dan Inovasi Peternakan (Journal of Research and Innovation of Animals), 7(1), 9–18. https://doi.org/10.23960/jrip.2023.7.1.9-18
Liang, G., Hong, H., Xie, W., & Zheng, L. (2018). Combining convolutional neural network with recursive neural network for blood cell image classification. IEEE Access, 6, 36188–36197. https://doi.org/10.1109/ACCESS.2018.2846685
Mayrose, H., Sampathila, N., Bairy, G. M., Belurkar, S., Saravu, K., Basu, A., & Khan, S. (2021). Intelligent algorithm for detection of dengue using mobilenetv2-based deep features with lymphocyte nucleus. Expert Systems, 40(4). https://doi.org/10.1111/exsy.12904
Nazlibilek, S., Karacor, D., Ercan, T., Sazli, M. H., Kalender, O., & Ege, Y. (2014). Automatic segmentation, counting, size determination and classification of white blood cells. Measurement, 55, 58–65. https://doi.org/10.1016/j.measurement.2014.04.008
Negm, A. S., Hassan, O. A., & Kandil, A. H. (2018). A decision support system for Acute Leukaemia classification based on digital microscopic images. Alexandria Engineering Journal, 57(4), 2319–2332. https://doi.org/10.1016/j.aej.2017.08.025
Saleem, S., Amin, J., Sharif, M., Mallah, G. A., Kadry, S., & Gandomi, A. H. (2022). Leukemia segmentation and classification: A comprehensive survey. Computers in Biology and Medicine, 150, 106028. https://doi.org/10.1016/j.compbiomed.2022.106028
Settouti, N., Saidi, M., Bechar, M. E. A., el Habib Daho, M., & Chikh, M. A. (2020). An instance and variable selection approach in pixel-based classification for automatic white blood cells segmentation. Pattern Analysis and Applications, 23(4), 1709–1726. https://doi.org/10.1007/s10044-020-00873-w
Shahin, A. I., Guo, Y., Amin, K. M., & Sharawi, A. A. (2019). White blood cells identification system based on convolutional deep neural learning networks. Computer Methods and Programs in Biomedicine, 168, 69–80. https://doi.org/10.1016/j.cmpb.2017.11.015
Thomas, N., & Sreejith, V. (2018). A Review on White Blood Cells Segmentation. IOP Conference Series: Materials Science and Engineering, 396, 012038. https://doi.org/10.1088/1757-899X/396/1/012038
Xin, Z. (2018). Data for: Fast and robust segmentation of cell images by self-supervised learning.
Yao, X., Sun, K., Bu, X., Zhao, C., & Jin, Y. (2021). Classification of white blood cells using weighted optimized deformable convolutional neural networks. Artificial Cells, Nanomedicine, and Biotechnology, 49(1), 147–155. https://doi.org/10.1080/21691401.2021.1879823
Z., M., S., T., & B., A. (2017). Neural Network Classification of white blood cell using microscopic images. international journal of advanced computer science and applications, 8(5), 99-104. https://doi.org/10.14569/IJACSA.2017.080513
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Copyright (c) 2023 Achmad Abdurrazzaq, Ahmad Kadri bin Junoh, Ilyasa Pahlevi Reza Yulianto
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