Medical Images Denoising Based On Sparse Decomposition

With the development of medicine and computer technology, medical imaging technology is playing an increasingly important role in clinical application. Noises are inevitably introduced to medical CT images because of various factors in medical imaging. Noises in medical images will greatly degrade the quality of images and bring difficulties to clinical diagnosis.The traditional denoising methods often need to know the statistical characteristics of the noise,In the paper, based on the sparse decomposition of matching pursuit algorithm, an adaptive block sparse denoising method and a hard threshold block sparse denoising method is proposed. The paper has done the following specifically work:Firstly, using match pursuit algorithm to achieve sparse decomposition of signals, including the decomposition of one-dimensional signal and two-dimensional image signals. the asymmetric atom dictionaries are selected as over-complete atoms of image sparse decomposition. In order to reduce the computational complexity of the matching pursuit method, genetic algorithm and block-based method are introduced. The experiments show that block-based and genetic algorithm can reconstruct original images effectively, reduces the computational complexity greatly.Secondly, the block-based method is applied to medical image denoising. Image coherent ratio threshold is introduced as the end conditions of denoising. Different image coherent ratio thresholds are fixed. The paper has done some researches about hard-threshold on the basis of block-based. The experimental results show that with selecting the appropriate number of reconstruction atoms, hard-threshold has a certain denoising performance under the condition of not knowing noises in medical images.