Malaysian Journal of Computer Science (ISSN 0127-9084)
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Title:Brain Magnetic Resonance Image Lateral Ventricles Deformation Analysis and Tumor Prediction
Auhtor(s): Kai Xiao,Sooi Hock Ho,Aboul Ella Hassanien,
Journal:Malaysian Journal of Computer Science (ISSN 0127-9084)
Volume:20, No 2
Year:2007
Keywords:Magnetic Resonance Imaging, Lateral Ventricles, Deformation Analysis, Thin Plate Spline, Morphing, Radon Transform, Skeleton, Radial Basis Function
Abstract:Brain tumor detection is still a challenge in the field of brain compute-aided diagnosis. In the brain Magnetic Resonance Images (MRI), the correlation between lateral ventricles deformations and tumor existence has been found useful in brain tumor detection and prediction. To retrieve the lateral ventricles deformation data for further statistical analysis and processing, a new method has been proposed in this paper to analyze the deformation of ventricles. Firstly, in this method, the boundaries of the lateral ventricles are segmented, pixels on the boundary are sampled, and a nonlinear interpolation method based on Thin Plate Spline (TPS) is conducted to create a more accurate template image for each specific case, followed by the application and performance comparison between TPS with Radial Basis Function Neural Networks (RBF-NN) and Radon Transform (RT) on the extracted Skeleton of the boundary of the ventricles for locating the optimal orientation of the image through iterative image rotation. The reorienting facilitates the final step of deformation analysis whereby the reoriented ventricles are analyzed based on the displacement values obtained from the TPS of the sampled template and the diagnostic lateral ventricle. By comparing with several real cases, our experimental results suggest that this method is effective and relevant in ventricles deformation analysis and prediction of tumor location. The performance comparison results also suggest that using RT on Skeleton is an efficient method in locating the optimal orientation where the results show that the computing speed is at least more than 100 times faster than using TPS and RBF-NN.
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