A Comparative Study of Digital Image Segmentation Algorithms for Acute Myeloid Leukemia M1 White Blood Cells Images

Nurcahya Pradana Taufik Prakisya, Andika Setiawan

Abstract

Various types of algorithms have been widely used for image segmentation in digital image processing. Every algorithm has features that make it unique to be applied to specific cases. One of the applications of image segmentation is to detect white blood cells. Certain objects such as blood cells must be able to be well segmented because their existence is very crucial to support the accuracy of disease detection related to haematology or the branch of medical science that studies the morphology of blood and blood-forming tissues. Three image segmentation algorithms were compared through this study: Seed Region Growing, Otsu Thresholding and Active Contour Without Edge. Comparative analysis of the three algorithms was done by counting the number of white blood cell objects that were successfully segmented with the actual number of cells that were counted manually. A total of 30 images of blood smears were taken from people suffering from acute myeloid leukemia M1. The average accuracy values from each algorithm were used to determine which image segmentation algorithm is the most suitable for application in the case of white blood cells segmentation. The results showed that Active Contour Without Edge is the most appropriate among the other algorithms

Keywords

Acute Myeloid Leukemia, Active Contour Without Edge, Otsu Thresholding, Seed Region Growing

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References

Bell, A., & Sallah, S. (n.d.). Bell & Sallah, 2005.pdf (7th ed.). Abbott. A Promise for Life.

Chan, T. F., Yezrielev Sandberg, B., & Vese, L. A. (2000). Active contours without edges for vector-valued images. Journal of Visual Communication and Image Representation, 11(2), 130–141. https://doi.org/10.1006/jvci.1999.0442

Chen, X., Pan, J., Wang, S., Hong, S., Hong, S., & He, S. (2019). The epidemiological trend of acute myeloid leukemia in childhood: A population-based analysis. Journal of Cancer, 10(20), 4824–4835. https://doi.org/10.7150/jca.32326

Devi, S. S., Roy, A., Sharma, M., & Laskar, R. H. (2016). kNN Classification Based Erythrocyte Separation in Microscopic Images of Thin Blood Smear. Proceedings - International Conference on Computational Intelligence and Networks, 2016-Janua, 69–72. https://doi.org/10.1109/CINE.2016.19

Gonzalez, R. C., & Woods, R. E. (2008). Digital Image Processing Third Edition (Third Edit). Prentice Hall, Pearson.

Harjoko, A., Ratnaningsih, T., Suryani, E., Wiharto, Palgunadi, S., & Prakisya, N. P. T. (2018). Classification of acute myeloid leukemia subtypes M1, M2 and M3 using active contour without edge segmentation and momentum backpropagation artificial neural network. MATEC Web of Conferences, 154. https://doi.org/10.1051/matecconf/201815401041

Mulkal. (n.d.). Implementasi Algoritma Region Growing untuk Segmentasi Retakan Bidang Batuan. Implementasi Algoritma Region Growing Untuk Segmentasi Retakan Bidang Batuan, (7).

Prakisya, N. P. T., Esti, S., & Wiharto. (2013). Pemanfaatan Seed Region Growing Segmentation dan Momentum Backpropagation Neural Network untuk Klasifikasi Jenis Sel Darah Putih. (May), 1–11.

Rajpurohit, S., Patil, S., Choudhary, N., Gavasane, S., & Kosamkar, P. (2018). Identification of Acute Lymphoblastic Leukemia in Microscopic Blood Image Using Image Processing and Machine Learning Algorithms. 2018 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2018, (Cll), 2359–2363. https://doi.org/10.1109/ICACCI.2018.8554576

Sachin, P., & Kumar, R. Y. (2017). Detection and Classification of Blood Cancer from Microscopic Cell Images Using SVM KNN and NN Classifier. International Journal of Advance Research, 3(6), 315–324. Retrieved from www.ijariit.com

Salat, J., & Achmady, S. (2018). Minimalisasi Distorsi Dari Segmentasi Citra Metode Otsu Menggunakan Fuzzy Clustering. ILKOM Jurnal Ilmiah, 10(1), 80–85. https://doi.org/10.33096/ilkom.v10i1.234.80-85

Setiawan, A., Harjoko, A., Ratnaningsih, T., Suryani, E., Wiharto, & Palgunadi, S. (2018). Classification of cell types in Acute Myeloid Leukemia (AML) of M4, M5 and M7 subtypes with support vector machine classifier. 2018 International Conference on Information and Communications Technology, ICOIACT 2018, 2018-Janua(Cml), 45–49. https://doi.org/10.1109/ICOIACT.2018.8350822

Shah, S., Abaza, A., Ross, A., & Ammar, H. (2006). AUTOMATIC TOOTH SEGMENTATION USING ACTIVE CONTOUR WITHOUT EDGES. 2006 Biometrics Symposium, 0–5.

Shaikh Mohammed Bilal, N., & Deshpande, S. (2018). Computer aided leukemia detection using digital image processing techniques. RTEICT 2017 - 2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, Proceedings, 2018-Janua(1), 344–348. https://doi.org/10.1109/RTEICT.2017.8256613

Suryani, E., Wiharto, Palgunadi, S., & Prakisya, N. P. T. (2017). Classification of Acute Myelogenous Leukemia (AML M2 and AML M3) using Momentum Back Propagation from Watershed Distance Transform Segmented Images. Journal of Physics: Conference Series, 801(1). https://doi.org/10.1088/1742-6596/801/1/012044

Suryani, E., Wiharto, W., & Wahyudiani, K. N. (2016). Identifikasi Anemia Thalasemia Betha (?) Mayor Berdasarkan Morfologi Sel Darah Merah. Scientific Journal of Informatics, 2(1), 15–27. https://doi.org/10.15294/sji.v2i1.4525

Tong, J., Shi, H., Wu, C., Jiang, H., & Yang, T. (2018). Skewness correction and quality evaluation of plug seedling images based on Canny operator and Hough transform. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2018.10.035

Vaishnnave, M. P., Suganya Devi, K., Srinivasan, P., & Arutperumjothi, G. (2019). Detection and classification of groundnut leaf diseases using KNN classifier. 2019 IEEE International Conference on System, Computation, Automation and Networking, ICSCAN 2019, 1–5. https://doi.org/10.1109/ICSCAN.2019.8878733