Thresholding-based White Blood Cells Segmentation from Microscopic Blood Images
DOI:
https://doi.org/10.21928/uhdjst.v4n1y2020.pp9-17Keywords:
Medical image processing, Segmentation techniques, Thresholding, White blood cellsAbstract
Digital image processing has a significant role in different research areas, including medical image processing, object detection, biometrics, information hiding, and image compression. Image segmentation, which is one of the most important steps in processing medical image, makes the objects inside images more meaningful. For example, from microscopic images, blood cancer can be identified which is known as leukemia; for this purpose at first, the white blood cells (WBCs) need to be segmented. This paper focuses on developing a segmentation technique for segmenting WBCs from microscopic blood images based on thresholding segmentation technique and it compares with the most commonly used segmentation technique which is known as color-k-means clustering. The comparison is done based on three well-known measurements, used for evaluating segmentation techniques which are probability random index, variance of information, and global consistency error. Experimental results demonstrate that the proposed thresholding-based segmentation technique provides better results compared to color-k-means clustering technique for segmenting WBCs as well as the time consumption of the proposed technique is less than the color-k-means which are 70.8144 ms and 204.7188 ms, respectively.
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[3] H. B. Kekre, B. Archana and H. R. Galiyal. “Segmentation of blast using vector quantization technique”. International Journal of Computer Applications, vol. 72, pp. 20-23, 2013.
[4] M. A. Bennet, G. Diana, U. Pooja and N. Ramya. “Texture metric driven acute lymphoid leukemia classification using artificial neural networks”. International Journal of Recent Technology and Engineering, vol. 7, no. 6S3, pp. 152-156, 2019.
[5] K. A. ElDahshan, M. I. Youssef, E. H. Masameer and M. A. Mustafa. “An efficient implementation of acute lymphoblastic leukemia images segmwntation on FPGA”. Advances in Image and Vedio Prpcessing, vol. 3, no. 3, pp. 8-17, 2015.
[6] V. Venmathi, K. N. Shobana, A. Kumar and D. G. Waran. “Leukemia detection using image processing”. International Journal for Scientific Research and Development, vol. 5, no. 1, pp. 804-808, 2017.
[7] S. C. Neoh, W. Srisukkham, L. Zhang, S. Todryk, B. Greystoke, C. P. Lim, M. A. Hossain and N. Aslam. “An intelligent decision support system for leukaemia diagnosis using microscopic blood images”. Scientific Reports, vol. 5, p. 14938, 2015.
[8] F. Sadeghian, Z. Seman, A. R. Ramli, B. H. A. Kahar and M. Saripan. “A framework for white blood cell segmentation in microscopic blood images using digital image processing”. Biological Procedures Online, vol. 11, pp. 196-206, 2009.
[9] N. I. C. Marzukia, N. H. Mahmoodb and M. A. A. Razakb. “Segmentation of white blood cell nucleus using active contour”. Jurnal Teknologi, vol. 74, pp. 115-118, 2015.
[10] H. T. Madhloom, S. A. Kareem and H. Ariffin. “Computer-aided acute leukemia blast cells segmentation in peripheral blood images”. Journal of Vibroengineering, vol. 17, pp. 4517-4532, 2015.
[11] N. M. Sobhy, N. M. Salem and M. El Dosoky. “A comparative study of white blood cells segmentation using otsu threshold and watershed transformation”. Journal of Biomedical Engineering and Medical Imaging, vol. 3, no. 3, pp. 15-24, 2016.
[12] J. P. Gowda and S. C. P. Kumar. “Segmentation of white blood cell using K-means and gram-schmidt orthogonalization”. Indian Journal of Science and Technology, vol. 10, pp. 1-6, 2017.
[13] K. N. Sukhia, M. M. Riaz, A. Ghafoor and N. Iltaf. “Overlapping white blood cells detection based on watershed transform and circle fitting”. Radioengineering, vol. 24, pp. 1177-1181, 2017.
[14] S. Shafique and S. Tehsin. “Computer-aided diagnosis of acute lymphoblastic leukaemia”. Computational and Mathematical Methodsin Medicine, vol. 2018, p. 6125289, 2018.
[15] S. Yuheng and Y. Hao. “Image segmentation algorithms overview”. arXiv Preprint, vol. 2017, pp. 1-7, 2017.
[16] K. Bhargavi and S. Jyothi. “A survey on threshold based segmentation technique in image processing”. International Journal of Innovative Research and Development, vol. 3, pp. 234- 239, 2014.
[17] D. Kaur and Y. Kaur. “Various image segmentation techniques: A review”. International Journal of Computer Science and Mobile Computing, vol. 3, no. 5, pp. 809-814, 2014.
[18] S. Ravi and A. M. Khan. “Morphological Operations for Image Processing: Understanding and its Applications”. In: NCVSComs-13 Conference Proceedings, 2013.
[19] S. Singh and S. K. Grewal. “Role of mathematical morphology in digital image processing: A review”. International Journal of Scientific Engineering and Research, vol. 2, no. 4, 2014.
[20] A. E. Huque. “Shape Analysis and Measurement for the HeLa Cell Classification of Cultured Cells in High Throughput Screening”. University of Skövde, Skövde, Sweden, 2006.
[21] R. D. Labati, V. Piuri and F. Scotti. “ALL-IDB: The Acute Lymphoblastic Leukemia Image Database for Image Processing”. In: 18th IEEE International Conference on Image Processing, 2011.
[22] S. Kumar, S. Mishra, P. Asthana and Pragya. “Automated Detection of Acute Leukemia Using k-mean Clustering Algorithm”. In: Advances in Computer and Computational Sciences, Proceedings of ICCCCS, pp. 655-671, 2018.
[23] S. Mishra, L. Sharma, B. Majhi and P. Kumar Sa. “Microscopic Image Classification Using DCT for the Detection of Acute Lymphoblastic Leukemia (ALL)”. Proceedings of International Conference on Computer Vision and Image Processing, pp. 171- 180, 2017.
[24] P. S. Kumar and S. Vasuki. “Automated diagnosis of acute lymphocytic leukemia and acute myeloid leukemia using multi-SV”. Journal of Biomedical Imaging and Bioengineering, vol. 1, pp. 20-24, 2017.
[25] B. J. Ferdosi, S. Nowshin, F. A. Sabera and Habiba. “White Blood Cell Detection and Segmentation from Fluorescent Images with an Improved Algorithm using K-means Clustering and Morphological Operators”. In: 4th International Conference on Electrical Engineering and Information and Communication Technology (iCEEiCT), 2018.
[26] O. Sarrafzadeh and A. M. Dehnavi. “Nucleus and cytoplasm segmentation in microscopic images using K-means clustering and region growing”. Advanced Biomedical Research, vol. 4, p. 174. 2015.
[27] R. Kumar and A. M. Arthanariee. “Performance evaluation and comparative analysis of proposed image segmentation algorithm”. Indian Journal of Science and Technology, vol. 7, pp. 39-47, 2014.
[28] R. Sardana. “Comparitive analysis of image segmentation techniques”. International Journal of Advanced Research in Computer Engineering and Technology, vol. 2, no. 9, pp. 2615-2619, 2013.
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Published
2020-02-13
How to Cite
Mohammed, Z. F., & Abdulla, A. A. (2020). Thresholding-based White Blood Cells Segmentation from Microscopic Blood Images. UHD Journal of Science and Technology, 4(1), 9–17. https://doi.org/10.21928/uhdjst.v4n1y2020.pp9-17
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