Identification of acute lymphoblastic leukemia subtypes in touching cells based on enhanced edge detection

Nenden Siti Fatonah*, Handayani Tjandrasa, Chastine Fatichah

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


Acute lymphoblastic leukemia (ALL) is a type of blood cancer that begins with immature lymphocytes in bone marrow. The challenge in automatic identification of ALL when there are touching cells in image. The previous studies related to the separation of touching cells are still constrained by oversegmentation, undersegmentation, and inaccurate in cell separation. To overcome this problem, this research proposed an algorithm for cell separation based on enhanced edge detection. The objective of this study is identification of L1, L2, and L3, in touching cell based on edge detection and edge linking. Edge detection was performed on grayscale images and Hue images for the area of White Blood Cell segmentation results then the image results were combined. The classification of Acute Lymphoblastic Leukemia (ALL) cells was carried out using the geometry and texture features of cells and nucleus with Support Vector Machine (SVM) as the classifier. The dataset for the training process amounted to 668 ALL single cell images and for the testing process was 301 multi-cell ALL images. The testing results of the identification of ALL subtypes showed that the proposed cell separation method gave accuracy of 75.42%.

Original languageEnglish
Pages (from-to)204-215
Number of pages12
JournalInternational Journal of Intelligent Engineering and Systems
Issue number4
Publication statusPublished - 2020


  • Acute lymphoblastic leukemia
  • Edge detection
  • Splitting of touching cells
  • Support vector machine
  • Watershed
  • White blood cell


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