Segmentation of White Blood Cells and Lymphoblast Cells Using Moving K-Means

Ika Candradewi(1*), Reno Ghaffur Bagasjvara(2)

(1) Universitas Gadjah Mada
(2) PT EPSON INDONESIA, Bekasi, Jawa barat
(*) Corresponding Author


One of the diagnosis procedures for acute lymphoblastic leukemia is screening for blood cells by expert operator using microscope. This process is relatively long and will slow healing process of this disease which need fast treatment. Another way to screen this disease is by using digital image processing technique in microscopic image of blood smears to detect lymphoblast cells and types of white blood cells. One of essential step in digital image processing is segmentation because this process influences the subsequent process of detecting and classifying Acute Lymphoblastic Leukemia disease. This research performed segmentation of white blood cells using moving k-means algorithm. Some process are done to remove noise such as red blood cells and reduce detection errors such as white blood cells and/or lymphoblastic cell  that’s appear overlap. Postprocessing are performed to improve segmentation quality and to separate connected white blood cell. The dataset in this study has been validated with expert clinical pathologists from Sardjito Regional General Hospital, Yogyakarta, Indonesia. This research produces systems performance with results in sensitivity of 85.6%, precision 82.3%, Fscore of 83,9% and accuracy of 72.3%. Based on the results of the testing process with a much larger number of datasets on the side of the variations level of cell segmentation difficulties both in terms of illumination and overlapping cell, the method proposed in this study was able to detect or segment overlapping white blood cells better.


Acute Lymphoblastic Leukemia, White Blood Cell Segmentation. Moving K-Means, Watershed Transformation

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[1] M. M. Amin, S. Kermani, A. Talebi, and M. G. Oghli, “Recognition of Acute Lymphoblastic Leukemia Cells in Microscopic Images Using K ‑ Means Clustering and Support Vector Machine Classifier,” J. Med. Signals Sens., vol. 5, no. 1, pp. 49–58, Jna-Mar 2015 [Online]. Available:

/?report=printable. [Accessed: 3-Sep-2017]

[2] D. Goutam, “Classification of acute myelogenous leukemia in Blood Microscopic Images using Supervised Classifier,” in 2015, Int. Conf. on Engineering and Technology (ICETECH). pp. 1-5, 2015 [online]. Available:

5021. [Accessed: 31-Agust-2017]

[3] F. Scotti, “Automatic Morphological Analysis for Acute Leukemia Identification in Peripheral Blood Microscope Images,” in 2015, IEEE Int. Conf. on Computational Intelligence for Measurement Systems and Applications, pp. 96–101, 2005 [online]. Available: [Accessed: 20-Agust-2017]

[4] M. N. Khasanah, A. Harjoko, and I. Candradewi, “Klasifikasi Sel Darah Putih Berdasarkan Ciri Warna dan Bentuk dengan Metode K-Nearest Neighbor (K-NN),” IJEIS (Indonesian. J. Electron. Instrum. Syst., vol. 6, no. 2, pp. 151–162, Oct 2016. [online]. Available: [Accessed: 20-Sep-2017]

[5] B. Caraka, B. A. A. Sumbodo, and I. Candradewi, “Klasifikasi Sel Darah Putih Menggunakan Metode Support Vector Machine (SVM) Berbasis Pengolahan Citra Digital,” IJEIS (Indonesian. J. Electron. Instrum. Syst., vol. 7, no. 1, pp. 25–36, April 2017 [online]. Available: [Accessed: 20-Sep-2017]

[6] F. I. Sholeh, I. Candradewi, H. Abdul, A. Jabbar, and D. A. Setiawan, “Segmentasi Sel Blast pada Otomatisasi Sistem Deteksi Leukimia Limpositik Akut ( ALL )” in 2014 Prosiding – Seminar Nasional Ilmu Komputer, pp. 1-6, 2014.

[7] N. H. Harun, A. S. A. Nasir, M. Y. Mashor, and R. Hassan, “Unsupervised Segmentation Technique for Acute Leukemia Cells Using Clustering Algorithms,” Int. J. Comput. Electr. Autom. Control Inf. Eng., vol. 9, no. 1, pp. 253–259, 2015. [online]. Available: [Accessed: 2-Sep-2017]

[8] M. M. Amin, S. Kermani, A. Talebi, and M. G. Oghli, “Recognition of Acute Lymphoblastic Leukemia Cells in Microscopic Images Using K ‑ Means Clustering and Support Vector Machine Classifier,” J. Med. Signals Sens., vol. 5, no. 1, pp. 49–58, 2015. online]. Available: [Accessed: 10-Sep-2017]

[9] C. Raje and J. Rangole, “Detection of Leukemia in Microscopic Images Using Image Processing,” in Int. Conf. on Communication and Signal Processing, pp. 255-259, 2014 [online]. Available: [Accessed: 20-Agust-2017]

[10] Bagasjvara, R.G. "Klasifikasi Jenis Sel Darah Putih dan Sel Acute Lymphoblastic Leukimia Menggunakan Pengolahan Citra Digital dengan Metode Multilayer Perceptron", Thesis, FMIPA Universitas Gadjah Mada, 2017.


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