Fusion noise-removal technique with modified dark-contrast algorithm for robust segmentation of acute leukemia cell images

(1) * Nor Hazlyna Harun Mail (School of Computing, College of Arts and Science, Universiti Utara Malaysia, Malaysia)
(2) Juhaida Abu Bakar Mail (School of Computing, College of Arts and Science, Universiti Utara Malaysia, Malaysia)
(3) Hamirulaini’ Hambali Mail (School of Computing, College of Arts and Science, Universiti Utara Malaysia, Malaysia)
(4) Nurnadia Mohd Khair Mail (Faculty of Engineering Technology, Universiti Malaysia Perlis, Malaysia)
(5) Mohd. Yusoff Mashor Mail (School of Mechatronics Engineering, Universiti Malaysia Perlis, Malaysia)
(6) Roseline Hassan Mail (Department of Hematology, Universiti Sains Malaysia, Malaysia)
*corresponding author


Segmentation is the major area of interest in the field of image processing stage. In an automatic diagnosis of acute leukemia disease, the crucial process is to achieve the accurate segmentation of acute leukemia blood image. Generally, there are three requirements of image segmentation for medical purposes, namely; accuracy, robustness and effectiveness which have received considerable critical attention. As such, we propose a new (modified) dark contrast enhancement technique to enhance and automatically segment the acute leukemic cells. Subsequently, we used a fusion 7 × 7 median filter as well as the seeded region growing area extraction (SRGAE) algorithm to minimise the salt-and-pepper noise, apart from preserving the post-segmentation edge. As per the outcomes, the accuracy, sensitivity, and specificity of this method were 91.02%, 83.68%, and 91.57% respectively.


Acute leukemia; Median filter; Seeded region growing area; Extraction; Modified dark contrast enhancement




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