SEGMENTASI DAN PEMISAHAN SEL DARAH PUTIH BERSENTUHAN MENGGUNAKAN K-MEANS DAN HIERARCHICAL CLUSTERING ANALYSIS PADA CITRA LEUKEMIA MYELOID AKUT

Authors

  • Aryo Harto Teknik Informatika, Institut Teknologi Sepuluh Nopember
  • Chastine Fatichah Teknik Informatika, Institut Teknologi Sepuluh Nopember
Views: 784 Downloads: 874

DOI:

https://doi.org/10.12962/j24068535.v15i2.a599

Abstract

The success of identification and classification on diagnosing acute myeloid leukemia (AML) diseases based on image processing relies heavily on segmentation result. Segmentation on peripheral blood smear images aims to separate the leukocytes region with others region. To increase the segmentation accuracy on AML images, a few things regarding lighting condition, contrast, staining variations and the existence of touching cells must be overcome. In this study a method for leukocytes segmentation and separate the touching cell on AML images using cluster analysis with K-Means and hierarchical clustering analysis (HCA) is proposed. K-Means method is used to analyze the cluster for AML images segmentation. The AML image datasets with various staining variations is segmented using K-Means method.  The existence of touching cells is separated using HCA method which produce a stable clusters result. Segmentation and cell separation will be processed on local region or sub-image which is obtained from AML images cropping. From the evaluation results in 40 images of AML dataset, the proposed method is capable to properly segment the white blood cells region and separating the touching cell into a single cells. The average value of the segmentation results is 0.977 for precision, 0.885 for recall and 0.928 for Zijdenbos similarity index (ZSI) in white blood cell region. While in nucleus region the average value is 0.975 for precision, 0.924 for recall and 0.948 for ZSI. On cell counting, the error rate is also low which about 7.68%.

Downloads

Download data is not yet available.

References

[1] H.T. Madhloom, S.A. Kareem, H. Ariffin, A.A. Zaidan, H.O. Alanazi and B.B. Zaidan, 2010. An Automated White Blood Cell Nucleus Localization and Segmentation using Image Arithmetic and Automatic Threshold. Journal of Applied Sciences, 10: 959-966.

[2] H. Döhner, D.J. Weisdorf, C.D. Bloomfield (17 September 2015). "Acute Myeloid Leukemia". The New England Journal of Medicine. 373 (12): 1136–52.

[3] A. Jemal, A. Thomas, T. Murray, M. Thun (2002). "Cancer statistics, 2002". CA Cancer J Clin. 52 (1): 23–47.

[4] Theera-Umpon, N. Wang, L. & Jin, Y. (Eds.) White Blood Cell Segmentation and Classification in Microscopic Bone Marrow Images Fuzzy Systems and Knowledge Discovery: Second International Conference, FSKD 2005, Changsha, China, August 27-29, 2005, Proceedings, Part II, Springer Berlin Heidelberg, 2005, 787-796.

[5] S. H. Rezatofighi, H. Soltanian-Zadeh, Automatic recognition of five types of white blood cells in peripheral blood, Computerized Medical Imaging and Graphics, Volume 35, Issue 4, June 2011, Pages 333-343, ISSN 0895-6111.

[6] R.A. Saputra, C. Fatichah, N. Suciati. Penggabungan Fitur Tekstur yang Invariant terhadap Iluminasi dan Fitur Bentuk untuk Deteksi Acute Lymphoblastic Leukemia. JBI: Jurnal Buana Informatika, vol. 7, No. 1, 2016.

[7] C. Fatichah, M.L. Tangel, M.R. Widyanto, F. Dong and K. Hirota. Interest-Based Ordering for Fuzzy Morphology on White Blood Cell Image Segmentation. JACII: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.16, No.1, pp. 76-86, 2012.

[8] V. Piuri and F. Scotti, "Morphological classification of blood leucocytes by microscope images," 2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA, 2004, pp. 103-108.

[9] F. Scotti, "Robust Segmentation and Measurements Techniques of White Cells in Blood Microscope Images," 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings, Sorrento, 2006, pp. 43-48.

[10] E.-y. Wang, Z. Gou, A.-m. Miao, S.-q. Peng, Z.-y. Niu and X.-l. Shi. Recognition of Blood Cell Images Based on Color Fuzzy Clustering Fuzzy. Information and Engineering,Volume 2, Springer Berlin Heidelberg, 2009, pp. 69-75.

[11] D.C. Huang, K.D. Hung, Y.K. Chan, A computer assisted method for leukocyte nucleus segmentation and recognition in blood smear images, Journal of Systems and Software, Volume 85, Issue 9, September 2012, Pages 2104-2118, ISSN 0164-1212.

[12] H.T. Madhloom, S.A. Kareem and H. Ariffin. An Image Processing Application for the Localization and Segmentation of Lymphoblast Cell Using Peripheral Blood Images, Journal of Medical Systems, Vol. 36, pp. 2149-2158, 2012.

[13] S. Mohapatra, D. Patra and S. Satpathy. An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images, Neural Computing and Applications, 24, 1887-1904, 2014.

[14] L. Putzu, G. Caocci, C. Di Ruberto. Leucocyte classification for leukaemia detection using image processing techniques, Artificial Intelligence in Medicine, Volume 62, Issue 3, November 2014, Pages 179-191, ISSN 0933-3657.

[15] X. Yang, H. Li and X. Zhou, "Nuclei Segmentation Using Marker-Controlled Watershed, Tracking Using Mean-Shift, and Kalman Filter in Time-Lapse Microscopy," in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 53, no. 11, pp. 2405-2414, Nov. 2006.

[16] J. Cheng and J. C. Rajapakse. "Segmentation of Clustered Nuclei With Shape Markers and Marking Function," in IEEE Transactions on Biomedical Engineering, vol. 56, no. 3, pp. 741-748, March 2009.

[17] C. Jung, C. Kim, S. W. Chae and S. Oh. "Unsupervised Segmentation of Overlapped Nuclei Using Bayesian Classification," in IEEE Transactions on Biomedical Engineering, vol. 57, no. 12, pp. 2825-2832, Dec. 2010.

[18] F. Effendy, “Segmentasi sel darah merah bertumpuk berdasarkan fitur geometri pada perhitungan sel darah merah”, tesis magister, Teknik Informatika, ITS, Surabaya, Indonesia, 2013.

[19] E.P. Mandyarta, C. Fatichah. Three-level Local Thresholding Berbasis Metode Otsu untuk Segmentasi Leukosit pada Citra Leukemia Limfoblastik Akut. JBI: Jurnal Buana Informatika, vol. 7, No. 1, 2016.

[20] E. Prasetyo, “Pengolahan Citra Digital dan Aplikasinya Menggunakan Matlab”. Yogyakarta, Indonesia, 2011.

Downloads

Published

2017-07-01

Issue

Vol 15, No. 2, Juli 2017

Section

Articles

How to Cite

[1]
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, vol. 15, no. 2, pp. 140–151, Jul. 2017, doi: 10.12962/j24068535.v15i2.a599.

License

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in JUTI unless they receive approval for doing so from the Editor-in-Chief.

JUTI open access articles are distributed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.