scholarly journals Influence of Rotation Increments on Imaging Performance for a Rotatory Dual-Head PET System

2017 ◽  
Vol 2017 ◽  
pp. 1-11
Author(s):  
Fanzhen Meng ◽  
Xu Cao ◽  
Xuezhou Cao ◽  
Jianxun Wang ◽  
Liang Li ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Interpolation Method ◽  
Signal To Noise Ratio ◽  
System Response ◽  
Prototype System ◽  
Squared Error ◽  
Positron Emission ◽  
Pet System ◽  
Imaging Performance ◽  
Better Than

For a rotatory dual-head positron emission tomography (PET) system, how to determine the rotation increments is an open problem. In this study, we simulated the characteristics of a rotatory dual-head PET system. The influences of different rotation increments were compared and analyzed. Based on this simulation, the imaging performance of a prototype system was verified. A reconstruction flowchart was proposed based on a precalculated system response matrix (SRM). The SRM made the relationships between the voxels and lines of response (LORs) fixed; therefore, we added the interpolation method into the flowchart. Five metrics, including spatial resolution, normalized mean squared error (NMSE), peak signal-to-noise ratio (PSNR), contrast-to-noise (CNR), and structure similarity (SSIM), were applied to assess the reconstructed image quality. The results indicated that the 60° rotation increments with the bilinear interpolation had advantages in resolution, PSNR, NMSE, and SSIM. In terms of CNR, the 90° rotation increments were better than other increments. In addition, the reconstructed images of 90° rotation increments were also flatter than that of 60° increments. Therefore, both the 60° and 90° rotation increments could be used in the real experiments, and which one to choose may depend on the application requirement.

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

Combining two soil property rasters using an adaptive gating approach

Soil Research ◽  
2015 ◽  
Vol 53 (8) ◽  
pp. 907 ◽  
Author(s):  
David Clifford ◽  
Yi Guo
Keyword(s):  
Confidence Intervals ◽  
Soil Property ◽  
Mean Squared Error ◽  
Geographic Space ◽  
Covariate Information ◽  
Squared Error ◽  
Inverse Variance ◽  
Machine Learning Approach ◽  
Further Development ◽  
Better Than

Given the wide variety of ways one can measure and record soil properties, it is not uncommon to have multiple overlapping predictive maps for a particular soil property. One is then faced with the challenge of choosing the best prediction at a particular point, either by selecting one of the maps, or by combining them together in some optimal manner. This question was recently examined in detail when Malone et al. (2014) compared four different methods for combining a digital soil mapping product with a disaggregation product based on legacy data. These authors also examined the issue of how to compute confidence intervals for the resulting map based on confidence intervals associated with the original input products. In this paper, we propose a new method to combine models called adaptive gating, which is inspired by the use of gating functions in mixture of experts, a machine learning approach to forming hierarchical classifiers. We compare it here with two standard approaches – inverse-variance weights and a regression based approach. One of the benefits of the adaptive gating approach is that it allows weights to vary based on covariate information or across geographic space. As such, this presents a method that explicitly takes full advantage of the spatial nature of the maps we are trying to blend. We also suggest a conservative method for combining confidence intervals. We show that the root mean-squared error of predictions from the adaptive gating approach is similar to that of other standard approaches under cross-validation. However under independent validation the adaptive gating approach works better than the alternatives and as such it warrants further study in other areas of application and further development to reduce its computational complexity.

scholarly journals Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception

2018 ◽  
Vol 10 (12) ◽  
pp. 4863 ◽  
Author(s):  
Chao Huang ◽  
Longpeng Cao ◽  
Nanxin Peng ◽  
Sijia Li ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Power Plants ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Multilayer Perception ◽  
Squared Error ◽  
The Mean ◽  
Effectiveness And Efficiency ◽  
Mlp Network ◽  
Grid Operation ◽  
Better Than

Photovoltaic (PV) modules convert renewable and sustainable solar energy into electricity. However, the uncertainty of PV power production brings challenges for the grid operation. To facilitate the management and scheduling of PV power plants, forecasting is an essential technique. In this paper, a robust multilayer perception (MLP) neural network was developed for day-ahead forecasting of hourly PV power. A generic MLP is usually trained by minimizing the mean squared loss. The mean squared error is sensitive to a few particularly large errors that can lead to a poor estimator. To tackle the problem, the pseudo-Huber loss function, which combines the best properties of squared loss and absolute loss, was adopted in this paper. The effectiveness and efficiency of the proposed method was verified by benchmarking against a generic MLP network with real PV data. Numerical experiments illustrated that the proposed method performed better than the generic MLP network in terms of root mean squared error (RMSE) and mean absolute error (MAE).

scholarly journals Target and Equalizer Design for High-Density Bit-Patterned Media Recording

1970 ◽  
Vol 6 (2) ◽  
pp. 128-135
Author(s):  
Santi Koonkarnkhai ◽  
Phongsak Keeratiwintakorn ◽  
Piya Kovintavewat
Keyword(s):  
Partial Response ◽  
Mean Squared Error ◽  
Bit Patterned Media ◽  
Patterned Media ◽  
Media Noise ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
Cross Track ◽  
Partial Response Maximum Likelihood ◽  
Better Than

In bit-patterned media recording (BPMR) channels, the inter-track interference (ITI) is extremely severe at ultra high areal densities, which significantly degrades the system performance. The partial-response maximum-likelihood (PRML) technique that uses an one-dimensional (1D) partial response target might not be able to cope with this severe ITI, especially in the presence of media noise and track mis-registration (TMR). This paper describes the target and equalizer design for highdensity BPMR channels. Specifically, we proposes a two-dimensional (2D) cross-track asymmetric target, based on a minimum mean-squared error (MMSE) approach, to combat media noise and TMR. Results indicate that the proposed 2D target performs better than the previously proposed 2D targets, especially when media noise and TMR is severe.

Detection of Rarefaction of Capillaries and Avascular Region in Nailfold Capillary Images

2018 ◽  
pp. 1940-1954
Author(s):  
Suma K. V. ◽  
Bheemsain Rao
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Capillary Density ◽  
Signal To Noise ◽  
Median Filters ◽  
Squared Error ◽  
Connected Component Labeling ◽  
Labeling Algorithm ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Reduction in the capillary density in the nailfold region is frequently observed in patients suffering from Hypertension (Feng J, 2010). Loss of capillaries results in avascular regions which have been well characterized in many diseases (Mariusz, 2009). Nailfold capillary images need to be pre-processed so that noise can be removed, background can be separated and the useful parameters may be computed using image processing algorithms. Smoothing filters such as Gaussian, Median and Adaptive Median filters are compared using Mean Squared Error and Peak Signal-to-Noise Ratio. Otsu's thresholding is employed for segmentation. Connected Component Labeling algorithm is applied to calculate the number of capillaries per mm. This capillary density is used to identify rarefaction of capillaries and also the severity of rarefaction. Avascular region is detected by determining the distance between the peaks of the capillaries using Euclidian distance. Detection of rarefaction of capillaries and avascular regions can be used as a diagnostic tool for Hypertension and various other diseases.

Detection of Rarefaction of Capillaries and Avascular Region in Nailfold Capillary Images

2016 ◽  
Vol 5 (2) ◽  
pp. 73-86
Author(s):  
Suma K. V. ◽  
Bheemsain Rao
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Capillary Density ◽  
Connected Component ◽  
Signal To Noise ◽  
Median Filters ◽  
Squared Error ◽  
Labeling Algorithm ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Reduction in the capillary density in the nailfold region is frequently observed in patients suffering from Hypertension (Feng J, 2010). Loss of capillaries results in avascular regions which have been well characterized in many diseases (Mariusz, 2009). Nailfold capillary images need to be pre-processed so that noise can be removed, background can be separated and the useful parameters may be computed using image processing algorithms. Smoothing filters such as Gaussian, Median and Adaptive Median filters are compared using Mean Squared Error and Peak Signal-to-Noise Ratio. Otsu's thresholding is employed for segmentation. Connected Component Labeling algorithm is applied to calculate the number of capillaries per mm. This capillary density is used to identify rarefaction of capillaries and also the severity of rarefaction. Avascular region is detected by determining the distance between the peaks of the capillaries using Euclidian distance. Detection of rarefaction of capillaries and avascular regions can be used as a diagnostic tool for Hypertension and various other diseases.

scholarly journals A Comprehensive Approach to Image Watermarking, Encryption and Steganography

2015 ◽  
Vol 8 (4) ◽  
pp. 32
Author(s):  
Sabarish Sridhar
Keyword(s):  
Data Hiding ◽  
Data Transmission ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Image Watermarking ◽  
Cover Image ◽  
Second Step ◽  
Squared Error ◽  
Feature Similarity ◽  
Secure Data Transmission

Steganography, water marking and encryption are widely used in image processing and communication. A general practice is to use them independently or in combination of two - for e.g. data hiding with encryption or steganography alone. This paper aims to combine the features of watermarking, image encryption as well as image steganography to provide reliable and secure data transmission .The basics of data hiding and encryption are explained. The first step involves inserting the required watermark on the image at the optimum bit plane. The second step is to use an RSA hash to actually encrypt the image. The final step involves obtaining a cover image and hiding the encrypted image within this cover image. A set of metrics will be used for evaluation of the effectiveness of the digital water marking. The list includes Mean Squared Error, Peak Signal to Noise Ratio and Feature Similarity.

Ratio-Type Estimation Using Scrabled Auxiliary Variables in Stratification Under Simple Random Sampling and Ranked Set Sampling

2022 ◽  
pp. 62-85
Author(s):  
Carlos N. Bouza-Herrera ◽  
Jose M. Sautto ◽  
Khalid Ul Islam Rather
Keyword(s):  
Random Sampling ◽  
Mean Squared Error ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Auxiliary Variables ◽  
Mild Conditions ◽  
Squared Error ◽  
The Mean ◽  
Better Than

This chapter introduced basic elements on stratified simple random sampling (SSRS) on ranked set sampling (RSS). The chapter extends Singh et al. results to sampling a stratified population. The mean squared error (MSE) is derived. SRS is used independently for selecting the samples from the strata. The chapter extends Singh et al. results under the RSS design. They are used for developing the estimation in a stratified population. RSS is used for drawing the samples independently from the strata. The bias and mean squared error (MSE) of the developed estimators are derived. A comparison between the biases and MSEs obtained for the sampling designs SRS and RSS is made. Under mild conditions the comparisons sustained that each RSS model is better than its SRS alternative.

scholarly journals A COMPARATIVE STUDY OF IMAGE FILTERING ON VARIOUS NOISY PIXELS

2013 ◽  
pp. 157-165
Author(s):  
SONALI R. MAHAKALE ◽  
NILESHSINGH V. THAKUR
Keyword(s):  
Comparative Study ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Signal To Noise Ratio ◽  
Research Work ◽  
Absolute Error ◽  
Image Filtering ◽  
Squared Error ◽  
The Comparative Study ◽  
Work Done

This paper deals with the comparative study of research work done in the field of Image Filtering. Different noises can affect the image in different ways. Although various solutions are available for denoising them, a detail study of the research is required in order to design a filter which will fulfill the desire aspects along with handling most of the image filtering issues. An output image should be judged on the basis of Image Quality Metrics for ex-: Peak-Signal-to-Noise ratio (PSNR), Mean Squared Error (MSE) and Mean Absolute Error (MAE) and Execution Time.

scholarly journals A Novel Image Processing Methodology for X-ray Image Compression and Enhancement

2022 ◽  
pp. 1-8
Author(s):  
Calvin Omind Munna
Keyword(s):  
Image Processing ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
X Ray ◽  
Squared Error ◽  
Structure Similarity Index ◽  
Structure Similarity ◽  
Clinical Pictures

Currently, there a growing demand of data produced and stored in clinical domains. Therefore, for effective dealings of massive sets of data, a fusion methodology needs to be analyzed by considering the algorithmic complexities. For effective minimization of the severance of image content, hence minimizing the capacity to store and communicate data in optimal forms, image processing methodology has to be involved. In that case, in this research, two compression methodologies: lossy compression and lossless compression were utilized for the purpose of compressing images, which maintains the quality of images. Also, a number of sophisticated approaches to enhance the quality of the fused images have been applied. The methodologies have been assessed and various fusion findings have been presented. Lastly, performance parameters were obtained and evaluated with respect to sophisticated approaches. Structure Similarity Index Metric (SSIM), Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) are the metrics, which were utilized for the sample clinical pictures. Critical analysis of the measurement parameters shows higher efficiency compared to numerous image processing methods. This research draws understanding to these approaches and enables scientists to choose effective methodologies of a particular application.

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