Segmentation and Estimation of Brain Tumor Volume in Magnetic Resonance Images Based on T2-Weighted using Hidden Markov Random Field Algorithm

Authors

  • Hayder Saad Abdulbaqi School of Physics, Universiti Sains Malaysia, Penang, Malaysia
  • Mohd Zubir Mat Jafri Department of Physics, College of Education Al-Qadisiya, Al-Qadisiya, Iraq
  • Kussay N. Mutter School of Physics, Universiti Sains Malaysia, Penang, Malaysia
  • Ahmad Fairuz Omar School of Physics, Universiti Sains Malaysia, Penang, Malaysia
  • Iskandar Shahrim Mustafa School of Physics, Universiti Sains Malaysia, Penang, Malaysia
  • Loay Kadom Abood Department of Computer Science, College of Science, University of Baghdad Baghdad, Iraq.

Abstract

A brain tumor is an abnormal growth of tissue in the brain. The segmentation of brain tumors, which has been manually achieved from magnetic resonance images (MRI) is a decisive and time-consuming task. Treatment, diagnosis, signs and symptoms of the brain tumors mainly depend on the tumor size, position, and growth pattern. The accuracy and timeliness of detecting a brain tumor are vital factors to achieve the success in diagnosis and treatment of brain tumor. Therefore, segmentation and estimation of volume of brain tumor have been deemed a challenge mission in medical image processing. This paper aims to present a new approach, to improve the segmentation of brain tumors form T2-weighted MRI images using hidden Markov random fields (HMRF) and threshold method. We calculate the volume of the tumor using a new approach based on 2D images measurements and voxel space. The accuracy of segmentation is computed by using the ROC method. In order to validate the proposed approach a comparison is achieved with a manual method using Mango software. This comparison reveals that the noise or impurities in measurement of tumor volume are less in the proposed approach than in Mango software

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Published

2016-06-01

How to Cite

Abdulbaqi, H. S., Mat Jafri, M. Z., Mutter, K. N., Omar, A. F., Mustafa, I. S., & Abood, L. K. (2016). Segmentation and Estimation of Brain Tumor Volume in Magnetic Resonance Images Based on T2-Weighted using Hidden Markov Random Field Algorithm. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(3), 9–13. Retrieved from https://jtec.utem.edu.my/jtec/article/view/995

Issue

Vol. 8 No. 3: Emerging Technologies in Communication and Computer Engineering for Smart City Applications I

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Articles

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