scholarly journals A MRI Denoising Method Based on 3D Nonlocal Means and Multidimensional PCA

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Liu Chang ◽  
Gao ChaoBang ◽  
Yu Xi
Keyword(s):  
Structural Information ◽  
Evaluation Criteria ◽  
Desirable Property ◽  
3D Mri ◽  
Two Stage ◽  
Denoising Method ◽  
Nonlocal Means ◽  
Neighborhood Information ◽  
Visual Results ◽  
Scientific Fields

Recently nonlocal means (NLM) and its variants have been applied in the various scientific fields extensively due to its simplicity and desirable property to conserve the neighborhood information. The two-stage MRI denoising algorithm proposed in this paper is based on 3D optimized blockwise version of NLM and multidimensional PCA (MPCA). The proposed algorithm takes full use of the block representation advantageous of NLM3D to restore the noisy slice from different neighboring slices and employs MPCA as a postprocessing step to remove noise further while preserving the structural information of 3D MRI. The experiments have demonstrated that the proposed method has achieved better visual results and evaluation criteria than 3D-ADF, NLM3D, and OMNLM_LAPCA.

scholarly journals DOPSIE: Deep-Order Proximity and Structural Information Embedding

2019 ◽  
Vol 1 (2) ◽  
pp. 684-697
Author(s):  
Mario Manzo ◽  
Alessandro Rozza
Keyword(s):  
Vector Space ◽  
Structural Information ◽  
Graph Embedding ◽  
Depth Information ◽  
Classification Problems ◽  
Characteristic Path Length ◽  
Topological Information ◽  
Neighborhood Information ◽  
Information Embedding ◽  
Optimal Network

Graph-embedding algorithms map a graph into a vector space with the aim of preserving its structure and its intrinsic properties. Unfortunately, many of them are not able to encode the neighborhood information of the nodes well, especially from a topological prospective. To address this limitation, we propose a novel graph-embedding method called Deep-Order Proximity and Structural Information Embedding (DOPSIE). It provides topology and depth information at the same time through the analysis of the graph structure. Topological information is provided through clustering coefficients (CCs), which is connected to other structural properties, such as transitivity, density, characteristic path length, and efficiency, useful for representation in the vector space. The combination of individual node properties and neighborhood information constitutes an optimal network representation. Our experimental results show that DOPSIE outperforms state-of-the-art embedding methodologies in different classification problems.

UNCORRELATED PROJECTION DISCRIMINANT ANALYSIS AND ITS APPLICATION TO FACE IMAGE FEATURE EXTRACTION

2003 ◽  
Vol 17 (08) ◽  
pp. 1325-1347 ◽  
Author(s):  
JIAN YANG ◽  
JING-YU YANG ◽  
ALEJANDRO F. FRANGI ◽  
DAVID ZHANG
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Face Image ◽  
Desirable Property ◽  
Experimental Results ◽  
Image Feature ◽  
Analysis Method ◽  
Two Stage ◽  
Image Feature Extraction ◽  
Projection Analysis

In this paper, a novel image projection analysis method (UIPDA) is first developed for image feature extraction. In contrast to Liu's projection discriminant method, UIPDA has the desirable property that the projected feature vectors are mutually uncorrelated. Also, a new LDA technique called EULDA is presented for further feature extraction. The proposed methods are tested on the ORL and the NUST603 face databases. The experimental results demonstrate that: (i) UIPDA is superior to Liu's projection discriminant method and more efficient than Eigenfaces and Fisherfaces; (ii) EULDA outperforms the existing PCA plus LDA strategy; (iii) UIPDA plus EULDA is a very effective two-stage strategy for image feature extraction.

A two-stage fuzzy multi-objective framework for segmentation of 3D MRI brain image data

2017 ◽  
Vol 60 ◽  
pp. 312-327 ◽  
Author(s):  
Sayan Kahali ◽  
Sudip Kumar Adhikari ◽  
Jamuna Kanta Sing
Keyword(s):  
Image Data ◽  
3D Mri ◽  
Two Stage ◽  
Brain Image ◽  
Multi Objective ◽  
Mri Brain ◽  
Mri Brain Image

scholarly journals Improving Spatial Adaptivity of Nonlocal Means in Low-Dosed CT Imaging Using Pointwise Fractal Dimension

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xiuqing Zheng ◽  
Zhiwu Liao ◽  
Shaoxiang Hu ◽  
Ming Li ◽  
Jiliu Zhou
Keyword(s):  
Fractal Dimension ◽  
Signal To Noise Ratio ◽  
Denoising Method ◽  
Nonlocal Means ◽  
Anatomical Features ◽  
Fractal Image ◽  
Box Counting Dimension ◽  
Local Comparison ◽  
The Difference ◽  
Spatial Adaptivity

NLMs is a state-of-art image denoising method; however, it sometimes oversmoothes anatomical features in low-dose CT (LDCT) imaging. In this paper, we propose a simple way to improve the spatial adaptivity (SA) of NLMs using pointwise fractal dimension (PWFD). Unlike existing fractal image dimensions that are computed on the whole images or blocks of images, the new PWFD, named pointwise box-counting dimension (PWBCD), is computed for each image pixel. PWBCD uses a fixed size local window centered at the considered image pixel to fit the different local structures of images. Then based on PWBCD, a new method that uses PWBCD to improve SA of NLMs directly is proposed. That is, PWBCD is combined with the weight of the difference between local comparison windows for NLMs. Smoothing results for test images and real sinograms show that PWBCD-NLMs with well-chosen parameters can preserve anatomical features better while suppressing the noises efficiently. In addition, PWBCD-NLMs also has better performance both in visual quality and peak signal to noise ratio (PSNR) than NLMs in LDCT imaging.

scholarly journals A two-stage ECG signal denoising method based on deep convolutional network

2020 ◽  
Author(s):  
Lishen Qiu ◽  
Wenqiang Cai ◽  
Jie Yu ◽  
Jun Zhong ◽  
Yan Wang ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Stress Test ◽  
Noise Removal ◽  
Signal Denoising ◽  
Ecg Signal ◽  
Two Stage ◽  
Convolutional Network ◽  
Denoising Method ◽  
One Dimensional ◽  
Ecg Signals

AbstractElectrocardiogram (ECG) is an effective and non-invasive indicator for the detection and prevention of arrhythmia. ECG signals are susceptible to noise contamination, which can lead to errors in ECG interpretation. Therefore, ECG pretreatment is important for accurate analysis. In this paper, a method of noise reduction based on deep learning is proposed. The method is divided into two stages, and two corresponding models are formed. In the first stage, a one-dimensional U-net model is designed for ECG signal denoising to eliminate noise as much as possible. The one-dimensional DR-net model in the second stage is used to reconstruct the ECG signal and to correct the waveform distortion caused by noise removal in the first stage. In this paper, the U-net and the DR-net are constructed by the convolution method to achieve end-to-end mapping from noisy ECG signals to clean ECG signals. The ECG data used in this paper are from CPSC2018, and the noise signal is from MIT-BIH Noise Stress Test Database (NSTDB). In the experiment, the improvement in the signal-to-noise ratio SNRimp, the root mean square error decrease RMSEde, and the correlation coefficient P, are used to evaluate the performance of the network. This two-stage method is compared with FCN and U-net alone. The experimental results show that the two-stage noise reduction method can eliminate complex noise in the ECG signal while retaining the characteristic shape of the ECG signal. According to the results, we believe that the proposed method has a good application prospect in clinical practice.

scholarly journals Economic Security as a Scientific Category: Theoretical and Methodological Aspect of Research

2014 ◽  
Vol 39 (2) ◽  
Author(s):  
Sergey Efimovich Metelev
Keyword(s):  
Russian Federation ◽  
Evaluation Criteria ◽  
Economic Security ◽  
Methodological Aspect ◽  
Security Evaluation ◽  
The Russian Federation ◽  
Methodological Approaches ◽  
Methodological Position ◽  
Local Levels ◽  
Scientific Fields

SUMMARYThis article is devoted to the problems of providing economic security in the Russian Federation. Methodological approaches to research of economic security characteristics as an economic category are considered in this article as independent scientific fields. The special attention is paid to the analysis of scientific definitions of economic security within each of methodological approaches. The definition “economic security” is concretized on the basis of differentiation this definition from a methodological position of the research. The analysis of economic security evaluation criteria is carried out, and also measures to its providing for Russia are systematized at the federal, the regional and the local levels.

Continuous Line Drawings and Designs

2014 ◽  
Vol 5 (2) ◽  
pp. 16-39 ◽  
Author(s):  
Hua Li ◽  
David Mould
Keyword(s):  
Cost Function ◽  
Structural Information ◽  
Line Drawing ◽  
Continuous Line ◽  
Two Stage ◽  
Propagation Process ◽  
Line Drawings ◽  
Key Features ◽  
Single Boundary ◽  
Automatic Algorithm

Continuous Line Drawing (CLD) is a drawing style where a picture consists of a single closed non-intersecting line. This paper presents an automatic algorithm for constructing CLDs, with tone and structural information obtained from input images. The connectivity of the line is maintained through a tree generated by path finding with consideration of the key features for a given image. A branching tree structure is grown incrementally by selecting pixels by a cost function, relating to both the tone map and an importance map. After labeling each branch, an artificial wall is then constructed through a two-stage labeling propagation process to produce a single boundary, interpreted as the final CLD. The presented CLD method is effective and automatic, and provides some opportunities for variations. The paper also shows how to design CLDs from scratch using three steps: building base structures, forming shapes by thickening, and extracting CLDs by tracing the boundary of the shapes.

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