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Acute lymphoblastic leukemia segmentation using local pixel information

Al-jaboriy, Saif S. and Sjarif, Nilam Nur Amir and Chuprat, Suriayati and Abduallah, Wafaa Mustafa (2019) Acute lymphoblastic leukemia segmentation using local pixel information. Pattern Recognition Letters, 125 . pp. 85-90. ISSN 0167-8655

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Official URL: http://dx.doi.org/10.1016/j.patrec.2019.03.024


The severity of acute lymphoblastic leukemia depends on the percentages of blast cells (abnormal white blood cells) in bone marrow or peripheral blood. The manual microscopic examination of bone marrow is less accurate, time-consuming, and susceptible to errors, thus making it difficult for lab workers to accurately recognize the characteristics of blast cells. Researchers have adopted different computational methods to identify the nature of blast cells; however, these methods are incapable of accurately segmenting leukocyte cells due to some major disadvantages, such as lack of contrast between objects and background, sensitivity to gray-scale, sensitivity to noise in images, and large computational size. Therefore, it is indispensable to develop a new and improved technique for leukocyte cell segmentation. In the present research, an automatic leukocyte cell segmentation process was introduced that is based on machine learning approach and image processing technique. Further, the characteristics of blast cells were extracted using 4-moment statistical features and artificial neural networks (ANNs). It was found that the proposed method yielded a blasts cell segmentation accuracy of 97% under different lighting conditions.

Item Type:Article
Uncontrolled Keywords:artificial neural networks, machine learning technique, microscopy images, segmentation
Subjects:Q Science > Q Science (General)
T Technology > T Technology (General)
Divisions:Razak School of Engineering and Advanced Technology
ID Code:87745
Deposited By: Yanti Mohd Shah
Deposited On:30 Nov 2020 21:15
Last Modified:30 Nov 2020 21:15

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