Abstract:
In this thesis, we propose a new technique for noise filtering in (MRI) Magnetic Resonance Imaging. In medical image processing, medical image are corrupted by different type of noises. It is very important to obtain precise images to facilitate accurate observations for the given application. Magnetic Resonance Imaging scans are the diagnostic tool of choice in medical field. De-noising is always a challenging problem in magnetic resonance imaging and important for clinical diagnosis and computerized analysis, such as tissue classification and segmentation. It is well known that the noise in magnetic resonance imaging has a rician distribution. Unlike additive Gaussian noise, rician noise is signal dependent, and separating signal from noise is a difficult task. Because of this reason noise removal techniques have been customarily applied to improve MR image quality.
In this thesis firstly, a study of MR image denoising filters was made, these filters have been implemented using MATLAB for reduction rician noise. The quality of the output images is measured by the statistical quantity measures: mean square error (MES), signal to noise ratio (SNR), image quality measure (UQI) and method noise. Secondly, technique was introduced to reduce rician noise in magnetic resonance images (MRI) this done by wavelet transform decomposition and sub-bands mixing (inverse wavelet transform) to obtained the proposed technique image. The proposed technique has been implemented using MATLAB program, applied to synthetics and real MR images, the MSE, SNR, UQI and method noise are taken as performance measures. Experimental results are compared with the results of denoising filters that explain firstly at different noise levels and the proposed technique showing superior performance in most causes was analyzed.
