Rician noise attenuation in the wavelet packet transformed domain for brain MRI

Authors:
Gabriela Pérez;Aura Conci;Ana Belén Moreno;Juan Antonio Hernandez-Tamames
Affiliations:
Universidad Técnica de Ambato, Ciudadela Universitaria, Campus Huachi, Edificio Zeta, Ambato, Ecuador and Departamento de Ciencias de la Computación, Universidad Rey Juan Carlos, Madrid, ...;Universidade Federal Fluminense, Niterói, Brazil;Departamento de Ciencias de la Computación, Universidad Rey Juan Carlos, Madrid, Spain;Alzheimer Center, Reina Sofía Foundation, Madrid, Spain
Venue:
Integrated Computer-Aided Engineering
Year:
2014

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Abstract

Preprocessing stage for denoising is a crucial task in image analysis in general, and in computer-aided diagnosis using medical images in particular. Standard acquisition of Magnetic Resonance Images MRI presents statistical Rician noise which degrades the performance of the image analysis. This paper presents a new technique to reduce Rician noise of brain MRI. The new method for noise filtering is achieved in the discrete Wavelet Packets Transform WPT domain. Four methodologies for thresholding the detail coefficients in the same 2D WPT domain have been experimented considering two scenarios with and without a previous adaptive Wiener filtering in the spatial domain. Best quantitative and qualitative results have been obtained by the new method presented in this work specifically tailored for brain MRI, which is adaptive to each subband and dependent on the data. It has been compared with other traditional methods considering the Signal to Noise Ratio SNR, Normalized Cross Correlation NCC and execution time ∼ 0.1 s/slice. A complete dataset of structural T1-w brain MRI of the BrainWeb database has been used for experiments. An important aspect is that these experiments with synthetic images proved that the common prior adaptive Wiener filtering often used by many authors is a dispensable procedure.