https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01235868/document

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI

A review on CT image noise and its denoising

Neutrosophic set, initiated by Smarandache, is a novel tool to deal with vagueness considering the truth-membership T, indeterminacy-membership I and falsity-membership F satisfying the condition $$0\le T+I+F\le 3$$. It can be used to characterize the uncertain information more sufficiently and accurately than intuitionistic fuzzy set. Neutrosophic set has attracted great attention of many scholars that have been extended to new types and these extensions have been used in many areas such as aggregation operators, decision making, image processing, information measures, graph and algebraic structures. Because of such a growth, we present an overview on neutrosophic set with the aim of offering a clear perspective on the different concepts, tools and trends related to their extensions. A total of 137 neutrosophic set publication records from Web of Science are analyzed. Many interesting results with regard to the annual trends, the top players in terms of country level as well as institutional level, the publishing journals, the highly cited papers, and the research landscape are yielded and explained in-depth. The results indicate that some developing economics (such as China, India, Turkey) are quite active in neutrosophic set research. Moreover, the co-authorship analysis of the country and institution, the co-citation analysis of the journal, reference and author, and the co-occurrence analysis of the keywords are presented by VOSviewer software.

A bibliometric analysis of neutrosophic set: two decades review from 1998 to 2017

https://hal.archives-ouvertes.fr/hal-01116697/document

MRI noise estimation and denoising using non-local PCA

Accurate and fully automatic brain tumor grading from volumetric 3D magnetic resonance imaging (MRI) is an essential procedure in the field of medical imaging analysis for full assistance of neuroradiology during clinical diagnosis. We propose, in this paper, an efficient and fully automatic deep multi-scale three-dimensional convolutional neural network (3D CNN) architecture for glioma brain tumor classification into low-grade gliomas (LGG) and high-grade gliomas (HGG) using the whole volumetric T1-Gado MRI sequence. Based on a 3D convolutional layer and a deep network, via small kernels, the proposed architecture has the potential to merge both the local and global contextual information with reduced weights. To overcome the data heterogeneity, we proposed a preprocessing technique based on intensity normalization and adaptive contrast enhancement of MRI data. Furthermore, for an effective training of such a deep 3D network, we used a data augmentation technique. The paper studied the impact of the proposed preprocessing and data augmentation on classification accuracy.Quantitative evaluations, over the well-known benchmark (Brats-2018), attest that the proposed architecture generates the most discriminative feature map to distinguish between LG and HG gliomas compared with 2D CNN variant. The proposed approach offers promising results outperforming the recently supervised and unsupervised state-of-the-art approaches by achieving an overall accuracy of 96.49% using the validation dataset. The obtained experimental results confirm that adequate MRI's preprocessing and data augmentation could lead to an accurate classification when exploiting CNN-based approaches.

Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification

A survey on the magnetic resonance image denoising methods

MRI denoising using nonlocal neutrosophic set approach of Wiener filtering

In this paper, a new filtering method based on neutrosophic set (NS) approach of wiener filter is presented to remove Rician noise from magnetic resonance image. A neutrosophic set, 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. The 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 ω-wiener filtering operation is used on T and F to decrease the set indeterminacy and remove noise. The experiments have conducted on simulated Magnetic Resonance images (MRI) from Brainweb database and clinical MR images corrupted by Rician noise. The results show that the NS wiener filter produces better denoising results in terms of visual perception, qualitative and quantitative measures compared with other denoising methods, such as classical wiener filter, the anisotropic diffusion filter, the total variation minimization scheme and non local means filter.

A New Neutrosophic Approach of Wiener Filtering for MRI Denoising

In this paper, a new filtering method is presents to remove Rician noise from magnetic resonance image. This filter is based on Neutrosophic set (NS) 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 s e t into image domain and define some concepts and operators for image denoising. The 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 ω-wiener filtering operation is used on T and F to decrease the set indeterminacy and remove noise. The experiments have conducted on simulated MR images from Brainweb database with Rician noise added. The visual and the diagnostic quality of the denoised image are well preserved. The performance of this filter is compared with anisotropic diffusion filter (ADF) and unbiased non local mean filter (UNLM).

/pdf/mri-denoising-based-on-neutrosophic-wiener-filtering-1c9eef3d3q.pdf

MRI denoising based on neutrosophic wiener filtering

In this paper, a new filtering method to remove Rician noise from magnetic resonance image is presented. This filter is based on Neutrosophic set (NS) approach of median filtering. A Neutrosophic set, a part of neutrosophy theory, studies the origin, nature and scope of neutralities, as well as their interactions with different ideational spectra. Here the MRI image is transformed into NS domain, which is described using three membership sets: T, I, F. The entropy of the neutrosophic set is defined and employed to evaluate the indeterminacy. The γ-median filtering operation is used on T and F to decrease the set indeterminacy and remove noise. We have conducted experiments on real MR image with Rician noise added. The performance of this filter is compared with the median filtering and the classical non local mean (NLM) de-noising approaches. This filter outperforms the median filter for different levels of noise. For low signal to noise ratio (SNR), this filter provides high peak signal to noise ratio (PSNR) than the NLM approach.

J. Mohan

Papers

A survey on the magnetic resonance image denoising methods

MRI denoising using nonlocal neutrosophic set approach of Wiener filtering

A New Neutrosophic Approach of Wiener Filtering for MRI Denoising

MRI denoising based on neutrosophic wiener filtering

A Neutrosophic approach of MRI denoising