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International Journal of Electrical and Computer Engineering Systems, Vol. 10. No. 2., 2019.

Preliminary communication

https://doi.org/10.32985/ijeces.10.2.1

Multilevel Thresholding of Brain Tumor MRI Images: Patch-Levy Bees Algorithm versus Harmony Search Algorithm

Farah Aqilah Bohani ; The National University of Malaysia,Faculty Information Science and Technology, Centre for Artificial Intelligence Technology
Ashwaq Qasem ; The National University of MalaysiaFaculty of Information Science and Technology, Centre for Artificial Intelligence and Technology
Siti Norul Huda Sheikh Abdullah ; The National University of MalaysiaFaculty of Information Science and Technology, Center for Cyber Security
Khairuddin Omar ; The National University of MalaysiaFaculty of Information Science and Technology, Centre for Artificial Intelligence and Technology
Shahnorbanun Sahran ; The National University of MalaysiaFaculty of Information Science and Technology, Centre for Artificial Intelligence and Technology
Rizuana Iqbal Hussain ; The National University of MalaysiaTuanku Muhriz Hospital Counselor, Department of Radiology
Syaza Sharis ; The National University of MalaysiaTuanku Muhriz Hospital Counselor, Department of Radiology

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Abstract

Image segmentation of brain magnetic resonance imaging (MRI) plays a crucial role among radiologists in terms of diagnosing brain disease. Parts of the brain such as white matter, gray matter and cerebrospinal fluids (CFS), have to be clearly determined by the radiologist during the process of brain abnormalities detection. Manual segmentation is grueling and may be prone to error, which can in turn affect the result of the diagnosis. Nature-inspired metaheuristic algorithms such as Harmony Search (HS), which was successfully applied in multilevel thresholding for brain tumor segmentation instead of the Patch-Levy Bees algorithm (PLBA). Even though the PLBA is one powerful multilevel thresholding, it has not been applied to brain tumor segmentation. This paper focuses on a comparative study of the PLBA and HS for brain tumor segmentation. The test dataset consisting of nine images was collected from the Tuanku Muhriz UKM Hospital (HCTM). As for the result, it shows that the PLBA has significantly outperformed HS. The performance of both algorithms is evaluated in terms of solution quality and stability.

Keywords

Brain MRI; HS; multilevel thresholding; Otsu; PLBA; segmentation

Hrčak ID:

234710

URI

https://hrcak.srce.hr/234710

Publication date:

15.1.2020.

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