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, 2018)
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.