K-Means Clustering Based on Otsu Thresholding For Nucleus of White Blood Cells Segmentation

  • Wiga Maulana Baihaqi STMIK Amikom Purwokerto
  • Chyntia Raras Ajeng Widiawati Universitas Amikom Purwokerto
  • Tegar Insani Universitas Amikom Purwokerto
Keywords: segmentation, white blood cells, k-means clustering, otsu thresholding, morphological operations

Abstract

White blood cells function as the human immune system, and help defend the body against viruses. In clinical practice, identification and counting of white blood cells in blood smears is often used to diagnose many diseases such as infection, inflammation, malignancy, leukemia. In the past, examination of blood smears was very complex, manual tasks were tedious and time-consuming. This research proposes the k-means clustering algorithm to separate white blood cells from other parts. However, k-means clustering has a weakness that is when determining the initial prototype values, so the otsu thresholding method is used to determine the threshold by utilizing global values, then proceed with morphological operations to refine the segmentation image. The results of segmentation are measured by the Positive Predeictive Value (PPV) and Negative Positive Value (NPV) parameters. The results obtained prove that the use of otsu thresholding and morphological operations significantly increase the value of PPV compared to the value of PPV that does not use otsu thresholding. Whereas the NPV value increased but not significantly.

Downloads

Download data is not yet available.

References

A. R. Andrade, L. H. S. Vogado, R. de M. S. Veras, R. R. V. Silva, F. H. D. Araujo, and F. N. S. Medeiros, “Recent computational methods for white blood cell nuclei segmentation: A comparative study,” Comput. Methods Programs Biomed., vol. 173, no. 2019, pp. 1–14, 2019.

F. Xing and L. Yang, “Robust nucleus/cell detection and segmentation in digital pathology and microscopy images: A comprehensive review,” IEEE Rev. Biomed. Eng., vol. 9, pp. 234–263, 2016.

H. Kutlu, E. Avci, and F. Özyurt, “White blood cells detection and classification based on regional convolutional neural networks,” Med. Hypotheses, vol. 135, no. 2020, pp. 1–11, 2019.

S. Tantikitti, S. Tumswadi, and W. Premchaiswadi, “Image Processing for Detection of Dengue Virus Based on WBC Classification and Decision Tree,” in International Conference on ICT and Knowledge Engineering, 2015, pp. 84–89.

R. B. Hegde, K. Prasad, H. Hebbar, and B. M. K. Singh, “Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images,” Biocybern. Biomed. Eng., vol. 39, no. 2, pp. 382–392, 2019.

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, 2017.

H. Kong, M. Gurcan, and K. Belkacem-Boussaid, “Partitioning Histopathological Images: An Integrated Framework for Supervised Color-texture Segmentation and Cell Splitting,” IEEE Trans. Med. Imaging, vol. 30, no. 9, pp. 1661–1677, 2011.

S. Laddha, “Analysis of White Blood Cell Segmentation Techniques and Classification Using Deep Convolutional Neural Network for Leukemia Detection,” Helix, vol. 8, no. 6, pp. 4519–4524, 2018.

X. Hou et al., “A BandMax and spectral angle mapper based alogrithm for white blood cell segmentation,” in Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, vol. 10420, no. 104202B, pp. 104202B-1-104202B–5.

C. Di Ruberto, A. Loddo, and L. Putzu, “A leucocytes count system from blood smear images,” Mach. Vis. Appl., vol. 27, no. 8, pp. 1151–1160, 2016.

Y. Song, W. Cai, H. Huang, Y. Wang, D. D. Feng, and M. Chen, “Region-based progressive localization of cell nuclei in microscopic images with data adaptive modeling,” BMC Bioinformatics, vol. 14, no. 1, p. 173, 2013.

R. R. Waliyansyah, “Identifikasi Jenis Biji Kedelai ( Glycine Max L ) Menggunakan Gray Identification of Glycine Max L Seeds Using Gray Level Coocurance Matrix ( Glcm ) and K-Means Clustering,” J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 1, pp. 17–26, 2020.

H. Kong, M. Gurcan, and K. Belkacem-Boussaid, “Partitioning histopathological images: an integrated framework for supervised color-texture segmentation and cell splitting,” IEEE Trans. Med. Imaging, vol. 30, no. 9, pp. 1661–1677, Sep. 2011.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015, pp. 234–241.

C., Zhang, X., Xiao, X., Li, Chen, J., Ying, W., Zhen, J., Chang, C., Zheng, Z., Liu, “White blood cell segmentation by color-space-based k-means clustering,” Sensors (Switzerland), vol. 14, no. 9, pp. 16128–16147, 2014.

N. Theera-Umpon, “White blood cell segmentation and classification in microscopic bone marrow images,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 3614 LNAI, pp. 787–796, 2005.

S. Arslan, E. Ozyurek, and C. Gunduz-Demir, “A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images,” Cytometry, vol. 85, pp. 480–490, 2014.

L. B. Dorini, R. Minetto, and N. J. Leite, “White blood cell segmentation using morphological operators and scale-space analysis,” in XX Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2007), 2007, pp. 294–304.

M. Farhan, O. Yli-Harja, and A. Niemistö, “A novel method for splitting clumps of convex objects incorporating image intensity and using rectangular window-based concavity point-pair search,” Pattern Recognit., vol. 46, no. 3, pp. 741–751, 2013.

M. G. Abdul-Haleem, “White blood cells nuclei localization using modified K-means clustering algorithm and seed filling technique,” Iraqi J. Sci., vol. 60, no. 4, pp. 875–890, 2019.

A. Harto and C. Fatichah, “Segmentasi Dan Pemisahan Sel Darah Putih Bersentuhan Menggunakan K-Means Dan Hierarchical Clustering Analysis Pada Citra Leukemia Myeloid Akut,” JUTI J. Ilm. Teknol. Inf., vol. 15, no. 2, p. 162, 2017.

I. Herawati, Faridah, B. Achmad, and R. J. Yanti, “The effect of contrast enhancement method for k-means clustering segmentation of white blood cell cytoplasm image,” J. Eng. Sci. Technol., vol. 15, no. 1, pp. 227–248, 2020.

N. Thomas and V. Sreejith, “A Review on White Blood Cells Segmentation,” IOP Conf. Ser. Mater. Sci. Eng., vol. 396, no. 2018, pp. 1–5, 2018.

Sutrisno, A. A. Supianto, and I. Cholissodin, “Implementasi Teknik Watershed Dan Morfologi Pada Citra Satelit Untuk Segmentasi Area Universitas Brawijaya,” J. Teknol. Inf. dan Ilmu Komput., vol. 1, no. 1, p. 5, 2014.

M. Sokolova and G. Lapalme, “A systematic analysis of performance measures for classification tasks,” Inf. Process. Manag., vol. 45, no. 4, pp. 427–437, 2009.

M. Fatourechi, R. K. Ward, S. G. Mason, J. Huggins, A. Schlögl, and G. E. Birch, “Comparison of evaluation metrics in classification applications with imbalanced datasets,” in Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008, 2008, pp. 777–782

Published
2020-10-30
How to Cite
Baihaqi, W. M., Chyntia Raras Ajeng Widiawati, & Tegar Insani. (2020). K-Means Clustering Based on Otsu Thresholding For Nucleus of White Blood Cells Segmentation. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(5), 907-914. https://doi.org/10.29207/resti.v4i5.2309
Section
Artikel Teknologi Informasi