Study on Capabilities of Different Segmentation Algorithms in Detecting and Reducing Brain Tumor Size in Magnetic Resonance Imaging for Effective Telemedicine Services

Jide Julius Popoola, Thompson Emeka Godson, Yekeen Olajide Olasoji and Michael Rotimi Adu
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Jide Julius Popoola: Department of Electrical and Electronics Engineering, Federal University of Technology, Akure, Nigeria
Yekeen Olajide Olasoji: Department of Electrical and Electronics Engineering, Federal University of Technology, Akure, Nigeria
Michael Rotimi Adu: Department of Electrical and Electronics Engineering, Federal University of Technology, Akure, Nigeria

European Journal of Engineering and Technology Research, 2019, vol. 4, issue 2, 23-29

Abstract: Over the past decade, different image segmentation algorithms have been developed and employed in segmenting or analyzing brain magnetic resonance imaging (MRI) scans in the clinical applications for the detection of brain tumor. However, accurate detection, compression and transmission of brain tumor data remain parts of the challenging tasks militating against brain tumor telemedicine services due to the complex nature of brain tumor MRI scans. In overcoming this challenge, five different brain tumor segmentation algorithms were developed for this study. The algorithms were developed using MATLAB scripts. The developed algorithms were evaluated using patients’ data retrieved from Mayfield website. The result of the comparative performance compression rate efficiency evaluation test carried out shows that the developed hybrid threshold-watershed segmentation algorithm outperforms others in terms of compression efficiency. The result implies that the usage of the developed hybrid threshold-watershed segmentation algorithm in transmitting brain tumor patients’ data transmission over wireless communication system will require limited bandwidth resource.

Keywords: Tumor; Brain Tumor; Telemedicine; Medical Imaging Techniques; Segmentation Algorithms (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejeng0:v:4:y:2019:i:2:id:61142

DOI: 10.24018/ejeng.2019.4.2.1142

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