hrcak mascot   Srce   HID

Prethodno priopćenje
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

Puni tekst: engleski, pdf (2 MB) str. 45-57 preuzimanja: 110* citiraj
APA 6th Edition
Aqilah Bohani, F., Qasem, A., Norul Huda Sheikh Abdullah, S., Omar, K., Sahran, S., Iqbal Hussain, R. i Sharis, S. (2019). Multilevel Thresholding of Brain Tumor MRI Images: Patch-Levy Bees Algorithm versus Harmony Search Algorithm. International journal of electrical and computer engineering systems, 10. (2.), 45-57. https://doi.org/10.32985/ijeces.10.2.1
MLA 8th Edition
Aqilah Bohani, Farah, et al. "Multilevel Thresholding of Brain Tumor MRI Images: Patch-Levy Bees Algorithm versus Harmony Search Algorithm." International journal of electrical and computer engineering systems, vol. 10., br. 2., 2019, str. 45-57. https://doi.org/10.32985/ijeces.10.2.1. Citirano 28.02.2021.
Chicago 17th Edition
Aqilah Bohani, Farah, Ashwaq Qasem, Siti Norul Huda Sheikh Abdullah, Khairuddin Omar, Shahnorbanun Sahran, Rizuana Iqbal Hussain i Syaza Sharis. "Multilevel Thresholding of Brain Tumor MRI Images: Patch-Levy Bees Algorithm versus Harmony Search Algorithm." International journal of electrical and computer engineering systems 10., br. 2. (2019): 45-57. https://doi.org/10.32985/ijeces.10.2.1
Harvard
Aqilah Bohani, F., et al. (2019). 'Multilevel Thresholding of Brain Tumor MRI Images: Patch-Levy Bees Algorithm versus Harmony Search Algorithm', International journal of electrical and computer engineering systems, 10.(2.), str. 45-57. https://doi.org/10.32985/ijeces.10.2.1
Vancouver
Aqilah Bohani F, Qasem A, Norul Huda Sheikh Abdullah S, Omar K, Sahran S, Iqbal Hussain R i sur. Multilevel Thresholding of Brain Tumor MRI Images: Patch-Levy Bees Algorithm versus Harmony Search Algorithm. International journal of electrical and computer engineering systems [Internet]. 2019 [pristupljeno 28.02.2021.];10.(2.):45-57. https://doi.org/10.32985/ijeces.10.2.1
IEEE
F. Aqilah Bohani, et al., "Multilevel Thresholding of Brain Tumor MRI Images: Patch-Levy Bees Algorithm versus Harmony Search Algorithm", International journal of electrical and computer engineering systems, vol.10., br. 2., str. 45-57, 2019. [Online]. https://doi.org/10.32985/ijeces.10.2.1

Sažetak
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.

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

Hrčak ID: 234710

URI
https://hrcak.srce.hr/234710

Posjeta: 192 *