A total variation method for MR renography denoising based on mathematical morphology reconstruction

作者：Yu Chun yan; Li Ying^*; Liu Ai lian; Liu Bing xin; Chen Peng

来源：Journal of Convergence Information Technology, 2012, 7(14): 494-502.

DOI：10.4156/jcit.vol7.issue14.57

• Application research
• De-noising
• De-nosing
• Image de-nosing
• Image denoisng
• Image preprocessing
• Iterative regularization
• Medical analysis
• Morphological reconstruction
• Morphology reconstruction
• MRI Image
• Peak signal to noise ratio
• Total variation

Some detailed information of Magnetic resonance (MR) renography may be submerged due to the presence of noise. It is important for MRI image to be denoised before being used for medical analysis and application research field. The classic total variation (TV) model for image denosing is prone to lose rich textures and it is sensitive to noise. In this paper, a modified TV denosing model for MRI images is proposed. To achieve smoothing and enhancing the contrast in image preprocessing, the method modified TV iterative regularization with the gradient of morphological reconstruction diffusion image. The results of experiments performed on real medical MRI kidney images show that the new TV denosing model can eliminate noises effectively, and enhance edges clearly. Furthermore it has improvement in peak signal to noise ratio (PSNR) over some other standard Partial Differential Equation (PDE) models.

• 出版日期2012