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IJMLC 2019 Vol.9(3): 351-356 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.3.809

Characteristics Identification of Myeloblast Cell Using K-Means Clustering for Uncontrolled Images

Retno Supriyanti, Ahmad Rifai, Yogi Ramadhani, and Wahyu Siswandari

Abstract—Blood cells are one of the most important parts in humans. One type of blood cells that play an important role in a leukemia diagnosis is leukocyte cells. There are some types of leukocyte i.e. myeloblast, lymphoblast, monoblasts and erythroblasts. One method of measuring leukocyte cell abnormalities is by examination of the morphology of leukocyte cells covering the area, circumference and diameter of leukocyte cells. In this research will be identified morphology of myeloblast cells by using K-means clustering method. The observed control variable is a characteristic of myeloblast cell that includes diameter, contour, and uniformity of object, amount, and cell density. While the observed data is uncontrolled image (noise) with RGB color format. The experiment showed promising results for further development.

Index Terms—Leukocyte cell, leukemia, morphology, K-means clustering, uncontrolled image.

Retno Supriyanti, Ahmad Rifai, and Yogi Ramadhani are with Electrical Engineering Dept, Jenderal Soedirman University. Kampus Blater, Jl. Mayjend Sungkono KM 5, Blater, Purbalingga, Jawa Tengah, Indonesia (e-mail : retno_supriyanti@unsoed.ac.id).
Wahyu Siswandari is with Medical Department, Jenderal Soedirman University, Jl. Gumbreg, Purwokerto, Jawa Tengah, Indonesia.

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Cite: Retno Supriyanti, Ahmad Rifai, Yogi Ramadhani, and Wahyu Siswandari, "Characteristics Identification of Myeloblast Cell Using K-Means Clustering for Uncontrolled Images," International Journal of Machine Learning and Computing vol. 9, no. 3, pp. 351-356, 2019.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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