A SURVEY ON VARIOUS APPROACHES FOR SEGMENTATION AND CLASSIFICATION OF WHITE BLOOD CELLS
DOI:
https://doi.org/10.55766/sujst-2023-01-e01917Keywords:
Segmentation, Classification, Leukocytes, White Blood Cells, Deep LearningAbstract
Traditionally, White Blood Cell identification is performed by experienced pathologists manually. Peripheral blood smear analysis is a laboratory procedure that helps in diagnosis of various pathological disorders such as malaria, anemia, leukemia, etc. This process is performed at a microscopic level and therefore is required to be precise. Manually performing microscopic evaluation is a challenging task. This task can be automated with the help of computer-aided systems. This work discusses various methods being used in a variety of cellular image segmentation and classification tasks with the field of the survey being concentrated on leukocyte segmentation and classification.
References
Abdullah, E.L.E.N. and Turan, M.K. (2019). Classifying white blood cells using machine learning algorithms. International Journal of Engineering Research and Development, 11(1):141-152.
Adewoyin, A. S. (2014). Peripheral blood film-a review. Annals of Ibadan postgraduate medicine, 12(2):71-79. Available: https://www.histopathology.guru/peripheral-smear-examination/. Accessed date: Jul 12, 2021.
Adollah, R., Mashor, M. Y., Nasir, N. M., Rosline, H., Mahsin, H., and Adilah, H. (2008). Blood cell image segmentation: a review. In: Proceedings of 4th Kuala Lumpur international conference on biomedical engineering, Berlin, Heidelberg, p. 141-144.
Al-Hafiz, F., Al-Megren, S., and Kurdi, H. (2018). Red blood cell segmentation by thresholding and Canny detector. Procedia Computer Science, 141:327-334.
Al-Muhairy, J. and Al-Assaf, Y. (2005). Automatic white blood cell segmentation based on image processing. Proceedings of 16th IFAC World Congress, Jul 3-8, 2005; Prague, Czech Republic.
Alomari, Y.M., Sheikh Abdullah, S.N.H., Zaharatul Azma, R., and Omar, K. (2014). Automatic detection and quantification of WBCs and RBCs using iterative structured circle detection algorithm. Computational and mathematical methods in medicine, 2014.
Ananthi, V.P. and Balasubramaniam, P. (2016). A new thresholding technique based on fuzzy set as an application to leukocyte nucleus segmentation. Computer methods and programs in biomedicine, 134:165-177.
Angkoso, C.V., Purnama, I.K.E., and Purnomo, M.H. (2018). Automatic White Blood Cell Segmentation Based on Colour Segmentation and Active Contour Model. In: Proceedings of IEEE International Conference on Intelligent Autonomous Systems (ICoIAS), Singapore, p. 72-76.
Bidart, R., Gangeh, M. J., Peikari, M., Salama, S., Nofech-Mozes, S., Nofech, S., and Ghodsi, A. (2019). Fully Convolutional Networks in Localization and Classification of Cell Nuclei.
Branislav, H., Harabiš, V., and Králík, M. (2018). White blood cell segmentation using fully convolutional neural networks. Elektrorevue, 20(5):133-140.
Cao, H., Liu, H., and Song, E. (2018). A novel algorithm for segmentation of leukocytes in peripheral blood. Biomedical Signal Processing and Control, 45:10-21.
Ghane, N., Vard, A., Talebi, A., and Nematollahy, P. (2017). Segmentation of white blood cells from microscopic images using a novel combination of K-means clustering and modified watershed algorithm. Journal of medical signals and sensors, 7(2):92.
Grimmeiss Grahm, S. and Nilsson, D. (2019). Segmentation of White Blood Cells Using Deep Learning. Master’s Theses in Mathematical Sciences.
Hegde, R.B., Prasad, K., Hebbar, H., and Singh, B.M.K. (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(6):1-8.
Hegde, R.B., Prasad, K., Hebbar, H., and Singh, B.M.K. (2019a). Development of a robust algorithm for detection of nuclei of white blood cells in peripheral blood smear images. Multimedia Tools and Applications, 78(13):17,879-17,898.
Hegde, R.B., Prasad, K., Hebbar, H., and Singh, B.M.K. (2019b). Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images. Biocybernetics and Biomedical Engineering, 39(2):382-392.
Hegde, R.B., Prasad, K., Hebbar, H., and Singh, B.M.K. (2019c). Feature extraction using traditional image processing and convolutional neural network methods to classify white blood cells: a study. Australasian physical and engineering sciences in medicine, 42(2):627-638.
Kowal, M., Żejmo, M., Skobel, M., Korbicz, J., and Monczak, R. (2020). Cell nuclei segmentation in cytological images using convolutional neural network and seeded watershed algorithm. Journal of digital imaging, 33(1):231-242.
Kutlu, H., Avci, E., and Özyurt, F. (2020). White blood cells detection and classification based on regional convolutional neural networks. Medical hypotheses, 135:109,472.
Labati, R.D., Piuri, V., and Scotti, F. (2011). All-IDB: The acute lymphoblastic Leukemia image database for image processing. Proceedings of IEEE International Conference on Image Processing, Brussels, Belgium, p. 2,045-2,048.
Li, Y., Zhu, R., Mi, L., Cao, Y., and Yao, D. (2016). Segmentation of white blood cell from acute lymphoblastic Leukemia images using dual-threshold method. Computational and mathematical methods in medicine, 2016.
Mahanta, L.B., Bora, K., Kalita, S.J., and Yogi, P. (2019). Automated Counting of Platelets and White Blood Cells from Blood Smear Images. Proceedings of International Conference on Pattern Recognition and Machine Intelligence, Tezpur, India, p. 13-20.
Mahmood, N.H., Lim, P.C., Mazalan, S.M., and Razak, M.A.A. (2013). Blood cells extraction using colour based segmentation technique. International journal of life sciences biotechnology and pharma research, 2(2):2,250-3,137.
Dridi, M., El Bedoui, K., Barhoumi, W., and Maktouf, C. (2018). Automated detection and counting of malaria cells in thin blood smears. In: Traitement et Analyse de l'Information Méthodes et Applications, Tunisia.
Moallem, G., Poostchi, M., Yu, H., Silamut, K., Palaniappan, N., Antani, S., and Thoma, G. (2017). Detecting and Segmenting White Blood Cells in Microscopy Images of Thin Blood Smears. In: Proceedings of IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Washington, DC, USA, p.1-8.
Naylor, P., Laé, M., Reyal, F., and Walter, T. (2017). Nuclei segmentation in histopathology images using deep neural networks. In: Proceedings of IEEE International symposium on biomedical imaging (ISBI), Melbourne, VIC, Australia, p. 933-936.
Prinyakupt, J. and Pluempitiwiriyawej, C. (2015). Segmentation of white blood cells and comparison of cell morphology by linear and naïve Bayes classifiers. Biomedical engineering online, 14(1):1-19.
PuttamadeGowda. J. and Prasanna Kumar S.C., (2019). Segmentation of White Blood Cells using Integrated Process of Improved Spectral Angle Mapper, Gram-Schmidt Orthogonalization with KMeans Clustering. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8:2,278-3,075.
Rajendran, S. and Kumar, E.S. (2019). Leukocytes Classification and Segmentation in Microscopic Blood Smear Image. In: Proceedings of IEEE International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kerala, India, p. 1,064-1,068.
Ravikumar, S. (2016). Image segmentation and classification of white blood cells with the extreme learning machine and the fast relevance vector machine. Artificial cells, nanomedicine and biotechnology, 44(3):985-989.
Rezatofighi, S.H. and Soltanian-Zadeh, H. (2011). Automatic recognition of five types of white blood cells in peripheral blood. Computerized Medical Imaging and Graphics, 35(4):333-343.
Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: convolutional networks for biomedical image segmentation. In: Proceedings of Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany.
Roy, R. and Sasi, S. (2018). Classification of WBC using deep learning for diagnosing diseases. In: Proceedings of IEEE International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, India, p. 1,634-1,638.
Safuan, S.N.M., Tomari, R., Zakaria, W.N.W., and Othman, N. (2017, September). White blood cell counting analysis of blood smear images using various segmentation strategies. Proceedings of AIP Conference, p. 020018.
Sahlol, A.T., Kollmannsberger, P., and Ewees, A.A. (2020). Efficient classification of white blood cell Leukemia with improved swarm optimization of deep features. Scientific reports, 10(1):1-11.
Sapna, S. and Renuka, A. (2017). Techniques for segmentation and classification of leukocytes in blood smear images-a review. In: Proceedings of IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Coimbatore, India, p. 1-5.
Sapna, S. and Renuka, A. (2020). Computer-aided system for Leukocyte nucleus segmentation and Leukocyte classification based on nucleus characteristics. International Journal of Computers and Applications, 42(6):622-633.
Sharif, M., Amin, J., Siddiqa, A., Khan, H.U., Malik, M.S.A., Anjum, M.A., and Kadry, S. (2020). Recognition of different types of leukocytes using YOLOv2 and optimized bag-of-features. IEEE Access, 8:167,448-167,459.
Sharma, N. and Kinra, N. (2014). Detection and Counting The No Of White Blood Cells In Blood Sample Images By Colour Based K-Means Clustring. Int J Electr Electron Eng, 1(3).
Su, M. C., Cheng, C.Y., and Wang, P.C. (2014). A neural-network-based approach to white blood cell classification. The scientific world journal, 2014.
Tobias, R.R., De Jesus, L.C., Mital, M.E., Lauguico, S., Guillermo, M., Vicerra, R.R., and Dadios, E. (2020). Faster R-CNN model with momentum optimizer for RBC and WBC variants classification. In: Proceedings of IEEE Global Conference on Life Sciences and Technologies (LifeTech), Kyoto, Japan, p. 235-239.
Togacar, M., Ergen, B., and Sertkaya, M.E. (2019). Subclass separation of white blood cell images using convolutional neural network models. Elektronikair Elektrotechnika, 25(5):63-68.
Togacar, M.J., Quiñones, V.V., Ballado, A., Cruz, J.D., and Caya, M.V. (2018). White blood cell classification and counting using convolutional neural network. In: Proceedings of IEEE International conference on control and robotics engineering (ICCRE), Nagoya, Japan, p. 259-263.
Umamaheshwari, D. and Geetha, S. (2018). Segmentation and classification of acute lymphoblastic leukemia cells tooled with digital image processing and ML techniques. In: Proceedings of IEEE International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, p. 1,336-1,341.
Weng, Y., Zhou, T., Li, Y., and Qiu, X. (2019). Nas-unet: Neural architecture search for medical image segmentation. IEEE Access, 7:44,247-44,257.
Wijesinghe, C.B., Wickramarachchi, D.N., Kalupahana, I.N., Lokesha, R., Silva, I.D., and Nanayakkara, N.D. (2020). Fully Automated Detection and Classification of White Blood Cells. In: Proceedings of IEEE Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Montreal, QC, Canada, p. 1,816-1,819.
Yildirim, M. and Cinar, A.C. (2019). Classification of White Blood Cells by Deep Learning Methods for Diagnosing Disease. Rev. D’Intelligence Artif., 33(5):335-340.
Zeng, Z., Xie, W., Zhang, Y., and Lu, Y. (2019). RIC-Unet: An improved neural network based on Unet for nuclei segmentation in histology images. IEEE Access, 7:21,420-21,428.
Zhao, J., Zhang, M., Zhou, Z., Chu, J., and Cao, F. (2017). Automatic detection and classification of leukocytes using convolutional neural networks. Medical and biological engineering and computing, 55(8):1,287-1,301.
Zheng, X., Wang, Y., Wang, G., and Liu, J. (2018). Fast and robust segmentation of white blood cell images by self-supervised learning. Micron, 107:55-71.
Downloads
Published
Versions
- 2023-07-27 (2)
- 2023-07-26 (1)
