The SURE-LET Approach for MR Brain Image Denoising Using Different Shrinkage Rules

2012 ◽  
pp. 227-235 ◽  
Author(s):  
D. Selvathi ◽  
S. Thamarai Selvi ◽  
C. Loorthu Sahaya Malar
Keyword(s):  
Image Denoising ◽  
Mean Squared Error ◽  
Diffusion Tensor ◽  
Curvelet Transform ◽  
Research Area ◽  
Magnetic Resonance Images ◽  
Image Domain ◽  
Active Research ◽  
Unbiased Risk ◽  
Wavelet Image

SURE-LET Approach is used for reducing or removing noise in brain Magnetic Resonance Images (MRI). Removing or reducing noise is an active research area in image processing. Rician noise is the dominant noise in MRIs. Due to this type of noise, the abnormal tissue (cancerous tissue) may be misclassified as normal tissue and introduces bias into MRI measurements that can have significant impact on the shapes and orientations of tensors in diffusion tensor MRIs. SURE is a new approach to Orthonormal wavelet image denoising. It is an image-domain minimization of an estimate of the mean squared error—Stein’s unbiased risk estimates (SURE). Here, the denoising process can be expressed as a linear combination of elementary denoising processes-linear expansion of thresholds (LET). Different Shrinkage functions such as Soft and Hard and Shrinkage rules and Universal and BayesShrink are used to remove noise and the performance of these results are compared. The algorithm is applied on brain MRIs with different noisy conditions by varying standard deviation of noise. The performance of this approach is compared with performance of the Curvelet transform.

The SURE-LET Approach for MR Brain Image Denoising Using Different Shrinkage Rules

2010 ◽  
Vol 5 (2) ◽  
pp. 73-81 ◽  
Author(s):  
D. Selvathi ◽  
S. Thamarai Selvi ◽  
C. Loorthu Sahaya Malar
Keyword(s):  
Image Denoising ◽  
Mean Squared Error ◽  
Curvelet Transform ◽  
Research Area ◽  
Magnetic Resonance Images ◽  
Image Domain ◽  
New Approach ◽  
Active Research ◽  
Unbiased Risk ◽  
Wavelet Image

SURE-LET Approach is used for reducing or removing noise in brain Magnetic Resonance Images (MRI). Removing or reducing noise is an active research area in image processing. Rician noise is the dominant noise in MRIs. Due to this type of noise, the abnormal tissue (cancerous tissue) may be misclassified as normal tissue and introduces bias into MRI measurements that can have signi?cant impact on the shapes and orientations of tensors in di?usion tensor MRIs. SURE is a new approach to Orthonormal wavelet image denoising. It is an image-domain minimization of an estimate of the mean squared error—Stein’s unbiased risk estimates (SURE). Here, the denoising process can be expressed as a linear combination of elementary denoising processes-linear expansion of thresholds (LET). Different Shrinkage functions such as Soft and Hard and Shrinkage rules and Universal and BayesShrink are used to remove noise and the performance of these results are compared. The algorithm is applied on brain MRIs with different noisy conditions by varying standard deviation of noise. The performance of this approach is compared with performance of the Curvelet transform.

scholarly journals Impact of Deep Learning in Medical Imaging: a Systematic New Proposed Model

2019 ◽  
Vol 8 (3S2) ◽  
pp. 112-118
Keyword(s):  
Deep Learning ◽  
Medical Imaging ◽  
Learning Algorithms ◽  
Brain Lesion ◽  
Research Area ◽  
Magnetic Resonance Images ◽  
Machine Learning Algorithms ◽  
Proposed Model ◽  
Active Research ◽  
Active Research Area

For years’ radiologist and clinician continues to employs various approaches, machine learning algorithms included to detect, diagnose, and prevent diseases using medical imaging. Recent advances in deep learning made medical imaging analysis and processing an active research area, various algorithms for segmentation, detection, and classification have been proposed. In this survey, we describe the trends of deep learning algorithms use in medical imaging, their architecture, hardware, and software used are all discussed. We concluded with the proposed model for brain lesion segmentation and classification using Magnetic Resonance Images (MRI).

Mathematical Model of Human Operator Using Fractional Calculus for Human-in-the-Loop Control

2015 ◽  
Author(s):  
Jiacai Huang ◽  
YangQuan Chen ◽  
Zhuo Li
Keyword(s):  
Mathematical Models ◽  
Mean Squared Error ◽  
Research Area ◽  
Human Operator ◽  
Loop Control ◽  
Human In The Loop ◽  
Squared Error ◽  
Active Research ◽  
Operator Behavior ◽  
Active Research Area

Mathematical models of human operator play a very important role in the Human-in-the-Loop manual control system. For several decades, modeling human operator’s dynamic has been an active research area. The traditional classical human operator models are usually developed using the Quasi-linear transfer function method, the optimal control theory method, and so on. The human operator models established by the above methods have deficiencies such as complicated and over parameterized, even for basic control elements. In this paper, based on the characteristics of human brain and behaviour, two kinds of fractional order mathematical models for describing human operator behavior are proposed. Through validation and comparison by the actual data, the best_fit model with smallest root mean squared error (RMSE) is obtained, which has simple structure with only few parameters, and each parameter has definite physical meaning.

Document Image Quality Assessment with Relaying Reference to Determine Minimum Readable Resolution for Compression

2020 ◽  
Vol 2020 (9) ◽  
pp. 323-1-323-8
Author(s):  
Litao Hu ◽  
Zhenhua Hu ◽  
Peter Bauer ◽  
Todd J. Harris ◽  
Jan P. Allebach
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Research Area ◽  
Input Image ◽  
Quality Score ◽  
Document Image ◽  
Digital Cameras ◽  
Active Research ◽  
Traditional Approaches

Image quality assessment has been a very active research area in the field of image processing, and there have been numerous methods proposed. However, most of the existing methods focus on digital images that only or mainly contain pictures or photos taken by digital cameras. Traditional approaches evaluate an input image as a whole and try to estimate a quality score for the image, in order to give viewers an idea of how “good” the image looks. In this paper, we mainly focus on the quality evaluation of contents of symbols like texts, bar-codes, QR-codes, lines, and hand-writings in target images. Estimating a quality score for this kind of information can be based on whether or not it is readable by a human, or recognizable by a decoder. Moreover, we mainly study the viewing quality of the scanned document of a printed image. For this purpose, we propose a novel image quality assessment algorithm that is able to determine the readability of a scanned document or regions in a scanned document. Experimental results on some testing images demonstrate the effectiveness of our method.

Medical Image Denoising With Various Noise Estimators Using Curvelet Transform

2014 ◽  
Vol 1 (2) ◽  
pp. 30-40
Author(s):  
Abha Choubey ◽  
◽  
Dr.G.R. Sinha ◽  
S. K Naik ◽  
◽  
...  
Keyword(s):  
Image Denoising ◽  
Medical Image ◽  
Curvelet Transform

Image denoising based on fast discrete curvelet transform

2009 ◽  
Vol 28 (12) ◽  
pp. 3138-3140
Author(s):  
Gao-qiu FANG ◽  
Zheng-yong WANG ◽  
Xiao-hong WU
Keyword(s):  
Image Denoising ◽  
Curvelet Transform ◽  
Fast Discrete Curvelet Transform

A Person-Centered Design Framework for Serious Games for Dementia

2020 ◽  
Vol 64 (1) ◽  
pp. 18-22
Author(s):  
Bella Yigong Zhang ◽  
Mark Chignell
Keyword(s):  
Theoretical Basis ◽  
Serious Games ◽  
Aging Population ◽  
Research Area ◽  
Design Framework ◽  
Strategic Design ◽  
Health And Wellbeing ◽  
Initial Design ◽  
Active Research ◽  
Active Research Area

With the rapidly aging population and the rising number of people living with dementia (PLWD), there is an urgent need for programming and activities that can promote the health and wellbeing of PLWD. Due to staffing and budgetary constraints, there is considerable interest in using technology to support this effort. Serious games for dementia have become a very active research area. However, much of the work is being done without a strong theoretical basis. We incorporate a Montessori approach with highly tactile interactions. We have developed a person-centered design framework for serious games for dementia with initial design recommendations. This framework has the potential to facilitate future strategic design and development in the field of serious games for dementia.

scholarly journals An Overview on the Rheology, Mechanical Properties, Durability, 3D Printing, and Microstructural Performance of Nanomaterials in Cementitious Composites

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2950
Author(s):  
Hongwei Song ◽  
Xinle Li
Keyword(s):  
Mechanical Properties ◽  
3D Printing ◽  
Three Dimensional ◽  
Cementitious Materials ◽  
Research Area ◽  
Cementitious Composites ◽  
Split Tensile Strength ◽  
Contour Crafting ◽  
Wide Range ◽  
Active Research

The most active research area is nanotechnology in cementitious composites, which has a wide range of applications and has achieved popularity over the last three decades. Nanoparticles (NPs) have emerged as possible materials to be used in the field of civil engineering. Previous research has concentrated on evaluating the effect of different NPs in cementitious materials to alter material characteristics. In order to provide a broad understanding of how nanomaterials (NMs) can be used, this paper critically evaluates previous research on the influence of rheology, mechanical properties, durability, 3D printing, and microstructural performance on cementitious materials. The flow properties of fresh cementitious composites can be measured using rheology and slump. Mechanical properties such as compressive, flexural, and split tensile strength reveal hardened properties. The necessary tests for determining a NM’s durability in concrete are shrinkage, pore structure and porosity, and permeability. The advent of modern 3D printing technologies is suitable for structural printing, such as contour crafting and binder jetting. Three-dimensional (3D) printing has opened up new avenues for the building and construction industry to become more digital. Regardless of the material science, a range of problems must be tackled, including developing smart cementitious composites suitable for 3D structural printing. According to the scanning electron microscopy results, the addition of NMs to cementitious materials results in a denser and improved microstructure with more hydration products. This paper provides valuable information and details about the rheology, mechanical properties, durability, 3D printing, and microstructural performance of cementitious materials with NMs and encourages further research.

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