An extended non‐local means algorithm: Application to brain MRI |
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| Authors: | Muhammad Aksam Iftikhar Abdul Jalil Saima Rathore Ahmad Ali Mutawarra Hussain |
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| Affiliation: | 1. Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad, Pakistan;2. Department of Computer Science, COMSAT institute of information technology, Lahore, Pakistan;3. Department of Computer Science and Information Technology, University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir |
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| Abstract: | Improved adaptive nonlocal means (IANLM) is a variant of classical nonlocal means (NLM) denoising method based on adaptation of its search window size. In this article, an extended nonlocal means (XNLM) algorithm is proposed by adapting IANLM to Rician noise in images obtained by magnetic resonance (MR) imaging modality. Moreover, for improved denoising, a wavelet coefficient mixing procedure is used in XNLM to mix wavelet sub‐bands of two IANLM‐filtered images, which are obtained using different parameters of IANLM. Finally, XNLM includes a novel parameter‐free pixel preselection procedure for improving computational efficiency of the algorithm. The proposed algorithm is validated on T1‐weighted, T2‐weighted and Proton Density (PD) weighted simulated brain MR images (MRI) at several noise levels. Optimal values of different parameters of XNLM are obtained for each type of MRI sequence, and different variants are investigated to reveal the benefits of different extensions presented in this work. The proposed XNLM algorithm outperforms several contemporary denoising algorithms on all the tested MRI sequences, and preserves important pathological information more effectively. Quantitative and visual results show that XNLM outperforms several existing denoising techniques, preserves important pathological information more effectively, and is computationallyefficient. |
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| Keywords: | nonlocal means denoising brain MRI Rician noise wavelet |
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