Publication Type:

Journal Article


Procedia Computer Science, Volume 58, p.603 - 613 (2015)



Iterative PCA.


Nowadays most of the clinical applications uses Magnetic Resonance Images(MRI) for diagnosing neurological abnormalities. During MR image acquisition the emitted energy is converted to image by using some mathematical models, and this may cause addition of noise. Therefore we need to denoise the image. Currently most of the clinical application uses Diffusion Tensor-MR Images for tracking neural fibres by extracting features from the images. Noise in DT-MR Images make fibre tracking and disease diagnosing tougher. So our work aims to denoise the Diffusion Tensor MR images with better visual quality. In this paper, we propose a denoising technique that uses Structural Similarity Index Matrix (SSIM) for grouping similar patches and performs Iterative Principal Component Analysis on each group. By performing the weighted average on Principal Component, we have obtained the denoised DT-MR Image. For getting better visual quality of the denoised images we employ Iterative Principal component Analysis technique.

Cite this Research Publication

S. U. Priya and Jyothisha J. Nair, “Denoising of DT-MR Images with an Iterative PCA”, Procedia Computer Science, vol. 58, pp. 603 - 613, 2015.