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Nor Hazlyna Harun
School of Computing, College of Arts and Science, Universiti Utara Malaysia
Computer Science & IT

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Fusion noise-removal technique with modified dark-contrast algorithm for robust segmentation of acute leukemia cell images Nor Hazlyna Harun; Juhaida Abu Bakar; Hamirulaini’ Hambali; Nurnadia Mohd Khair; Mohd. Yusoff Mashor; Roseline Hassan
International Journal of Advances in Intelligent Informatics Vol 4, No 3 (2018): November 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v4i3.276

Abstract

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.
Co-Authors Hamirulaini’ Hambali Juhaida Abu Bakar Mohd. Yusoff Mashor Nurnadia Mohd Khair Roseline Hassan