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

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...

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Bibliographic Details
Published inInternational journal of advances in intelligent informatics Vol. 4; no. 3; pp. 202 - 211
Main Authors Harun, Nor Hazlyna, Abu Bakar, Juhaida, Hambali, Hamirulaini’, Khair, Nurnadia M, Mashor, M.Y., Hassan, R.
Format Journal Article
LanguageEnglish
Published Yogyakarta Universitas Ahmad Dahlan 01.11.2018
Subjects
Accuracy
Acute leukemia
Algorithms
Automation
Blood
Bone marrow
Disease
Engineering
Erythrocytes
Extraction
Image contrast
Image processing
Image segmentation
Leukemia
Median filter
Medical imaging
Modified dark contrast enhancement
Noise
Seeded region growing area
Ultrasonic imaging
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Summary: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.
ISSN:2442-6571
2442-6571
DOI:10.26555/ijain.v4i3.276