HMRF Model For Brain Tumour Segmentation To Estimate The Volume Of MRI And CT Scan Images
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Date
2018-03
Authors
Abdulbaqi, Hayder Saad
Journal Title
Journal ISSN
Volume Title
Publisher
Universiti Sains Malaysia
Abstract
Magnetic resonance imaging (MRI) and computed tomography (CT) are two of the most important imaging technologies that enable the doctors to gain a reliable segmentation and estimation of brain tumours. The current study aims to develop a diagnostic method for medical images (MRI and CT) to achieve an accurate and precise segmentation and estimation of brain tumours. The proposed method in the study was applied on datasets had been collected from the Cancer Imaging Archive (TCIA) and Iraqi hospitals. The proposed method based on adapting and developing hidden Markov random field (HMRF) model and threshold method to carry out the segmentation of the brain tumours. In this study, an automatic trace method had been developed based on the voxel dimension value to execute the estimation of brain tumour volume. Furthermore, the study provided a method for rendering the brain tumour in 3D visualisation by using isosurface extraction techniques. In this study, a new algorithm has been developed to detect the location of the brain tumour using statistical information. In addition, the present study expands the number of CT scan slices using the mean between two successive slices based on the proposed method. The validation and the evaluation of all the work stages were performed using qualitative and quantitative methods. The ground truth of a brain tumour, which was traced manually by radiologists, is used for the evaluation of the segmentation results. It recorded satisfactory results using different measures where their means are high with Dice = 0.9393, JAC = 0.8908, TPR = 0.9142, TNR = 0.9669, FPR = 0.0313, and FNR = 0.0834. The estimation of the brain tumour volume was validated qualitatively using a real volume, which was obtained using the water displacement
method (Archimedes’ method).
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Keywords
Magnetic resonance imaging and computed tomography , a reliable segmentation and estimation of brain tumours