MRI denoising using nonlocal neutrosophic set approach of Wiener filtering

Abstract

In this paper, a new filtering method is presented to remove the Rician noise from magnetic resonance images (MRI) acquired using single coil MRI acquisition system. This filter is based on nonlocal neutrosophic set (NLNS) approach of Wiener filtering. A neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. Now, we apply the neutrosophic set into image domain and define some concepts and operators for image denoising. First, the nonlocal mean is applied to the noisy MRI. The resultant image is transformed into NS domain, described using three membership sets: true (T), indeterminacy (I) and false (F). The entropy of the neutrosophic set is defined and employed to measure the indeterminacy. The -Wiener filtering operation is used on T and F to decrease the set indeterminacy and to remove the noise. The experiments have been conducted on simulated MR images from Brainweb database and clinical MR images. The results show that the NLNS Wiener filter produces better denoising results in terms of qualitative and quantitative measures compared with other denoising methods, such as classical Wiener filter, the anisotropic diffusion filter, the total variation minimization and the nonlocal means filter. The visual and the diagnostic quality of the denoised image are well preserved.

Simplified neutrosophic linguistic normalized weighted bonferroni mean operator and its application to multi-criteria decision-making problems

“…Rivieccio [36] pointed out that a NS is a set where each element of the universe has a truth-membership, indeterminacymembership and falsity-membership, respectively, and it lies in the non-standard unit interval ]0 − , 1 + [. In recent years, the NS theory has found practical applications in various fields, such as semantic web services [37], mineral prospectivity prediction [38], image processing [39][40][41], granular computing [42], medical diagnosis [43] and information fusion [44]. Since it is hard to apply NSs in real scientific and engineering situations, single-valued neutrosophic sets (SVNSs) [45] and interval neutrosophic sets (INSs) [37] were introduced, which are two particular instances of NSs.…”

Segmentation of SAR images using improved artificial bee colony algorithm and neutrosophic set

“…The new algorithm uses the I-ABC algorithm and the neutrosophic set. In recent years, neutrosophic set has been applied to different computer vision problems such as image thresholding, image segmentation, image denoising and classification applications [28][29][30]. The neutrosophic set can suppress the edge fuzzy and provide accurate edge location.…”

Nonlocal linear minimum mean square error methods for denoising MRI

“…Since the MR data intrinsically contain many similar samples (patches) that can be used to improve the estimation results, Golshen et al [34] addressed this difficulty by developing an SNR adapted nonlocal LMMSE method that takes the advantage of the high degree of redundancy in the contents of MR images and using a similarity measure based on the local statistical moments of the image. In [35], a filter based on nonlocal neutrosophic set (NLNS) approach was proposed. Filter bank based nonlocal means [36], iterative bilateral filtering [37], MR denoising in the wavelet packet transformed domain [38] are the other latest trends of the research area.…”