Abstract
In recent years, multi-scale transform application in image processing especially for magnetic resonance (MR) images has been raised. Wavelet transform is introduced as a useful tool in image processing and it is capable of effectively removing noise from magnetic resonance images. The main problem with wavelet transform is that it is not able to distinguish one dimensional (1D) or higher dimentional discontinuities in images. In other words, since the wavelet transform is two dimensional (2D), it is considered as a separable transform, it is solely able to identify pointwise discontinuity in images. A proposed solution for this issue is an inseparable transform which is named curvelet. Time frequency transform based noise elimination methods, usually rely on thresholding. There are two important factors involved in thresholding: (1) a method to determine the threshold limit, (2) the implementation of the threshold. In curvelet method, by setting a hard threshold at low levels of noise the obtained similarity index is 0.9254. The proposed method for noise elimination and edge detection in this paper is applying curvelet transform in combination with wavelet transform, which on average leads to 10% improvement compared with wavelet method. The results show the efficiency of this method in different parts of image processing on simulated and actual MR images.
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