Medicine image denoising using wavelet transform

Abstract

Medical image processing has been investigated for more than three decades. It is clear that medical imaging will still play a very dominant role in clinical research as well as in the daily routine practice in the coming decade. For a number of reasons the images obtained by the medical instruments itself, such as CT, MRI are insufficient for the efficient performance of a surgical intervention and various image processing techniques are necessary in order to make the most important features more easily visible. Owing to its rapidly increasing popularity over last few decades, the wavelet transform has become quite a standard tool in numerous image research and application domains. Wavelet thresholding has been a popular technique for image denoising. The basic principle of wavelet thresholding is to identify and zero out wavelet coefficients of a signal which are likely to contain mostly noise. By preserving the most significant coefficients, wavelet thresholding preserves important highpass features of a signal such as discontinuities. Here we used this technology in medicine image denoising and resulted in quite satisfying result. The goal of the medical image denoising in a broad sense is the research, implementation, and validation of image processing approaches. Research is carried out among others medical application areas.