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Proceedings Paper

Measuring the volume of brain tumour and determining its location in T2-weighted MRI images using hidden Markov random field: expectation maximization algorithm
Author(s): Mohd. Zubir Mat Jafri; Hayder Saad Abdulbaqi; Kussay N. Mutter; Iskandar Shahrim Mustapha; Ahmad Fairuz Omar
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Paper Abstract

A brain tumour is an abnormal growth of tissue in the brain. Most tumour volume measurement processes are carried out manually by the radiographer and radiologist without relying on any auto program. This manual method is a timeconsuming task and may give inaccurate results. Treatment, diagnosis, signs and symptoms of the brain tumours mainly depend on the tumour volume and its location. In this paper, an approach is proposed to improve volume measurement of brain tumors as well as using a new method to determine the brain tumour location. The current study presents a hybrid method that includes two methods. One method is hidden Markov random field - expectation maximization (HMRFEM), which employs a positive initial classification of the image. The other method employs the threshold, which enables the final segmentation. In this method, the tumour volume is calculated using voxel dimension measurements. The brain tumour location was determined accurately in T2- weighted MRI image using a new algorithm. According to the results, this process was proven to be more useful compared to the manual method. Thus, it provides the possibility of calculating the volume and determining location of a brain tumour.

Paper Details

Date Published: 26 June 2017
PDF: 5 pages
Proc. SPIE 10335, Digital Optical Technologies 2017, 103351V (26 June 2017); doi: 10.1117/12.2270132
Show Author Affiliations
Mohd. Zubir Mat Jafri, Univ. Sains Malaysia (Malaysia)
Hayder Saad Abdulbaqi, Univ. Sains Malaysia (Malaysia)
College of Education Al-Qadisiya (Iraq)
Kussay N. Mutter, Univ. Sains Malaysia (Malaysia)
Iskandar Shahrim Mustapha, Univ. Sains Malaysia (Malaysia)
Ahmad Fairuz Omar, Univ. Sains Malaysia (Malaysia)

Published in SPIE Proceedings Vol. 10335:
Digital Optical Technologies 2017
Bernard C. Kress; Peter Schelkens, Editor(s)

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