scholarly journals A Contribution to Sonograph Image Quality Estimation Using Point Spread Function

2004 ◽  
Vol 47 (4) ◽  
pp. 293-295
Author(s):  
Ladislav Doležal ◽  
Jan Hálek
Keyword(s):  
Image Quality ◽  
Point Spread Function ◽  
Signal To Noise Ratio ◽  
Region Of Interest ◽  
Measuring System ◽  
Point Spread ◽  
Scanning Area ◽  
Spread Function ◽  
Gain Compensation

In medical sonography, sonograph image quality is an essential aspect for the safety of both patient and doctor. Its evaluation therefore requires an accurate and objective method for measurement. In this regard, a number of methods are in current use. Most of these are based on tissue mimicking phantom imaging. In contrast, we have used another principle based on Point Spread Function (PSF) analysis which is a product of the measuring system we have developed. In this case, the measured sonograph scans a small metallic ball target that moves in a water bath on a specified trajectory. The Region Of Interest (ROI) of the sonogram containing the ball target picture is digitised and the amplitude of the pixels analysed. The result is the PSF from which we calculate the lateral resolution (LR). For this purpose, we use our own original software. Using this method, we have to date been able to plot LR characteristics over the scanning plane. The method allows us to differentiate separate scanning lines and even multiple focal areas for dynamic focussing systems. It can detect malfunctions in dynamic focussing, size of aperture, time gain compensation function and/or transducer element failure. The procedure itself is not as easy or as fast to use as tissue mimicking phantoms or 3D signal to noise ratio evaluation, but it provides accurate and objective numeric parameters corresponding to the quality of image at any specified point over the whole scanning area. It is also a very powerful tool when used in combination with the other methods mentioned above.

An Approach to Measuring the Point Spread Function of the Confocal Raman Microscope

2020 ◽  
Vol 74 (10) ◽  
pp. 1230-1237
Author(s):  
Xiang Ding ◽  
Yanzhe Fu ◽  
Jiyan Zhang ◽  
Yao Hu ◽  
Shihang Fu
Keyword(s):  
Image Quality ◽  
Size Effect ◽  
Point Spread Function ◽  
Performance Indicator ◽  
Signal To Noise Ratio ◽  
Polystyrene Microspheres ◽  
Point Spread ◽  
Spread Function ◽  
Confocal Raman

The confocal Raman microscope (CRM) is a powerful tool in analytical science. Image quality is the most important performance indicator of CRM systems. The point spread function (PSF) is one of the most useful tools to evaluate the image quality of microscopic systems. A method based on a point-like object is proposed to measure the PSF of CRM, and the size effect of spherical objects is discussed. A series of phantoms are fabricated by embedding different sizes of polystyrene microspheres into polydimethylsiloxane matrix. The diameters of microspheres are from 0.2 µm to 5 µm. The phantoms are tested by measuring the PSF of a commercial CRM whose nominal lateral resolution is about 1 µm. Results of the PSF are obtained and the accuracy of resolution is used to evaluate the size effect of the microspheres. Experimental results are well consistent with theoretical analysis. The error of the PSF can be decreased by reducing the diameter of the microsphere but meanwhile the signal-to-noise ratio (S/N) will be lowered as well. The proper diameter of microspheres is proposed in consideration of the trade-off between the S/N and the measurement error of the PSF. Results indicate that the method provides a useful approach to measurement of the PSF and the resolution of the CRM.

Wiener Filter Based on the Image to Blur

2012 ◽  
Vol 591-593 ◽  
pp. 1567-1570
Author(s):  
Chao Da Chen ◽  
Si Qing Zhang ◽  
Chui Xin Chen
Keyword(s):  
Image Quality ◽  
Image Restoration ◽  
Point Spread Function ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Point Spread ◽  
Spread Function ◽  
Degraded Image ◽  
Ill Posed ◽  
Fuzzy Image

Image restoration, refers to the removal or loss in the process of getting digital image degradation of the image quality, image restoration technology is the key to meet the requirements of the point spread function, degradation model is an ill-posed integral equations, in the frequency domain, when H ( U, V ) less or equal to zero, the noise will be amplified, the degraded image and interference in H ( U, V ) value of the spectrum will be small to restore the image influence. In view of the point spread function put forward Wiener filtering algorithm, the noise lead to ill-posed integral with specified signal-to-noise ratio to reduce image restoration effects, through the IPT toolbox for fuzzy image restoration, image quality to achieve the anticipated effect.

Optimization of PET/CT image quality using the GE ‘Sharp IR’ point-spread function reconstruction algorithm

2017 ◽  
Vol 38 (6) ◽  
pp. 471-479 ◽  
Author(s):  
Nicholas J. Vennart ◽  
Nicholas Bird ◽  
John Buscombe ◽  
Heok K. Cheow ◽  
Ewa Nowosinska ◽  
...  
Keyword(s):  
Image Quality ◽  
Point Spread Function ◽  
Reconstruction Algorithm ◽  
Ct Image ◽  
Point Spread ◽  
Pet Ct ◽  
Spread Function ◽  
Ct Image Quality ◽  
Function Reconstruction

The Influence of Alteration of kVp and mAs towards the Image Quality of Acrylic Using CBCT

2019 ◽  
Vol 829 ◽  
pp. 252-257
Author(s):  
Azhari ◽  
Yohanes Hutasoit ◽  
Freddy Haryanto
Keyword(s):  
Image Quality ◽  
Signal To Noise Ratio ◽  
Region Of Interest ◽  
Signal To Noise ◽  
Bite Block ◽  
Significant Difference ◽  
Noise Ratio ◽  
Post Hoc ◽  
Radiograph Image

CBCT is a modernized technology in producing radiograph image on dentistry. The image quality excellence is very important for clinicians to interpret the image, so the result of diagnosis produced becoming more accurate, appropriate, thus minimizing the working time. This research was aimed to assess the image quality using the blank acrylic phantom polymethylmethacrylate (PMMA) (C­5H8O2)n in the density of 1.185 g/cm3 for evaluating the homogeneity and uniformity of the image produced. Acrylic phantom was supported with a tripod and laid down on the chin rest of the CBCT device, then the phantom was fixed, and the edge of the phantom was touched by the bite block. Furthermore, the exposure of the X-ray was executed toward the acrylic phantom with various kVp and mAs, from 80 until 90, with the range of 5 kV and the variation of mA was 3, 5, and 7 mA respectively. The time exposure was kept constant for 25 seconds. The samples were taken from CBCT acrylic images, then as much as 5 ROIs (Region of Interest) was chosen to be analyzed. The ROIs determination was analyzed by using the ImageJ® software for recognizing the influence of kVp and mAs towards the image uniformity, noise and SNR. The lowest kVp and mAs had the result of uniformity value, homogeneity and signal to noise ratio of 11.22; 40.35; and 5.96 respectively. Meanwhile, the highest kVp and mAs had uniformity value, homogeneity and signal to noise ratio of 16.96; 26.20; and 5.95 respectively. There were significant differences between the image uniformity and homogeneity on the lowest kVp and mAs compared to the highest kVp and mAs, as analyzed with the ANOVA statistics analysis continued with the t-student post-hoc test with α = 0.05. However, there was no significant difference in SNR as analyzed with the ANOVA statistic analysis. The usage of the higher kVp and mAs caused the improvement of the image homogeneity and uniformity compared to the lower kVp and mAs.

Exploring the Parameter Space of Point Spread Function Determination for the Scanning Electron Microscope—Part II: Effect on Image Restoration Quality

2019 ◽  
Vol 25 (05) ◽  
pp. 1183-1194
Author(s):  
Mandy C. Nevins ◽  
Richard K. Hailstone ◽  
Eric Lifshin
Keyword(s):  
Particle Size ◽  
Electron Microscope ◽  
Image Quality ◽  
Scanning Electron Microscope ◽  
Image Restoration ◽  
Point Spread Function ◽  
Background Correction ◽  
Point Spread ◽  
Spread Function ◽  
Scanning Electron

AbstractPoint spread function (PSF) deconvolution is an attractive software-based technique for resolution improvement in the scanning electron microscope (SEM) because it can restore information which has been blurred by challenging operating conditions. In Part 1, we studied a modern PSF determination method for SEM and explored how various parameters affected the method's ability to accurately estimate the PSF. In Part 2, we extend this exploration to PSF deconvolution for image restoration. The parameters include reference particle size, PSF smoothing (K), background correction, and restoration denoising (λ). Image quality was assessed by visual inspection and Fourier analysis. Overall, PSF deconvolution improved image quality. Low λ enhanced image sharpness at the cost of noise, while high λ created smoother restorations with less detail. λ should be chosen to balance feature preservation and denoising based on the application. Reference particle size within ±0.9 nm and K within a reasonable range had little effect on restoration quality. Restorations using background-corrected PSFs had superior quality compared with using no background correction, but if the correction was too high, the PSF was cut off causing blurrier restorations. Future efforts to automatically determine parameters would remove user guesswork, improve this method's consistency, and maximize interpretability of outputs.

Benefits of Point-Spread Function and Time of Flight for PET/CT Image Quality in Relation to the Body Mass Index and Injected Dose

2013 ◽  
Vol 38 (6) ◽  
pp. 407-412 ◽  
Author(s):  
Go Akamatsu ◽  
Katsuhiko Mitsumoto ◽  
Kaori Ishikawa ◽  
Takafumi Taniguchi ◽  
Nobuyoshi Ohya ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Image Quality ◽  
Body Mass ◽  
Point Spread Function ◽  
The Body ◽  
Ct Image ◽  
Point Spread ◽  
Pet Ct ◽  
Spread Function ◽  
Ct Image Quality

scholarly journals Impact of Point-Spread Function Modeling on PET Image Quality in Integrated PET/MR Hybrid Imaging

2015 ◽  
Vol 57 (1) ◽  
pp. 78-84 ◽  
Author(s):  
B. Aklan ◽  
M. Oehmigen ◽  
K. Beiderwellen ◽  
M. Ruhlmann ◽  
D. H. Paulus ◽  
...  
Keyword(s):  
Image Quality ◽  
Point Spread Function ◽  
Hybrid Imaging ◽  
Point Spread ◽  
Spread Function

scholarly journals Point-spread Function Deconvolution of the IFU Data and Restoration of Galaxy Stellar Kinematics

2021 ◽  
Vol 257 (2) ◽  
pp. 66
Author(s):  
Haeun Chung ◽  
Changbom Park ◽  
Yong-Sun Park
Keyword(s):  
Point Spread Function ◽  
Performance Test ◽  
Spatial Information ◽  
Signal To Noise Ratio ◽  
Line Of Sight ◽  
Stellar Kinematics ◽  
Point Spread ◽  
Deconvolution Algorithm ◽  
Spread Function ◽  
Field Unit

Abstract We present a performance test of the point-spread function (PSF) deconvolution algorithm applied to astronomical integral field unit (IFU) spectroscopy data for restoration of galaxy kinematics. We deconvolve the IFU data by applying the Lucy–Richardson algorithm to the 2D image slice at each wavelength. We demonstrate that the algorithm can effectively recover the true stellar kinematics of the galaxy, by using mock IFU data with a diverse combination of surface brightness profile, signal-to-noise ratio, line-of-sight geometry, and line-of-sight velocity distribution (LOSVD). In addition, we show that the proxy of the spin parameter λ R e can be accurately measured from the deconvolved IFU data. We apply the deconvolution algorithm to the actual SDSS-IV MaNGA IFU survey data. The 2D LOSVD, geometry, and λ R e measured from the deconvolved MaNGA IFU data exhibit noticeable differences compared to the ones measured from the original IFU data. The method can be applied to any other regular-grid IFU data to extract the PSF-deconvolved spatial information.

scholarly journals Blurred Image Restoration with Unknown Point Spread Function

2018 ◽  
Vol 29 (1) ◽  
pp. 189 ◽  
Author(s):  
Ghada Sabah Karam
Keyword(s):  
Image Quality ◽  
Point Spread Function ◽  
Image Quality Assessment ◽  
Blind Deconvolution ◽  
Assessment Method ◽  
Reference Image ◽  
Point Spread ◽  
Spread Function ◽  
Quality Assessment Method ◽  
Blurred Image

Blurring image caused by a number of factors such as de focus, motion, and limited sensor resolution. Most of existing blind deconvolution research concentrates at recovering a single blurring kernel for the entire image. We proposed adaptive blind- non reference image quality assessment method for estimation the blur function (i.e. point spread function PSF) from the image acquired under low-lighting conditions and defocus images using Bayesian Blind Deconvolution. It is based on predicting a sharp version of a blurry inter image and uses the two images to solve a PSF. The estimation down by trial and error experimentation, until an acceptable restored image quality is obtained. Assessments the qualities of images have done through the applications of a set of quality metrics. Our method is fast and produces accurate results.

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