Evaluation Of Imaging Performance Research Articles

Small animal, positron emission tomography-magnetic resonance imaging system based on a clinical magnetic resonance imaging scanner: evaluation of basic imaging performance.

Development of advanced preclinical imaging techniques has had an important impact on the field of biomedical research, with positron emission tomography (PET) imaging the most mature of these efforts. Developers of preclinical PET scanners have joined the recent multimodality imaging trend by combining PET imaging with other modalities, such as magnetic resonance imaging (MRI). Our group has developed a combined PET-MRI insert for the imaging of animals up to the size of rats in a clinical 3T MRI scanner. The system utilizes a sequential scanner configuration instead of the more common coplanar geometry. The PET component of the system consists of a ring of 12 liquid-cooled, SiPM-based detector modules ( ). System performance was evaluated with the NEMA NU 4-2008 protocol. Spatial resolution is 5cm from the center of the field-of-view measured from single-slice rebinned filtered backprojection-reconstructed images. Peak noise equivalent count rate is 17.7kcps at 8.5MBq; peak sensitivity is 2.9%. The MRI component of the system is composed of a 12-cm-diameter birdcage transmit/receive coil with a dual-preamplifier interface possessing very low noise preamplifiers. System performance was evaluated using American College of Radiology-based methods. Image homogeneity is 99%; the ghosting ratio is 0.0054. The signal-to-noise ratio is 95 and spatial resolution is . There was no discernable cross-modality interference.

Performance Evaluation of Adaptive Imaging Based on Multiphase Apodization with Cross-correlation: A Pilot Study in Abdominal Ultrasound.

Degradation of image contrast caused by phase aberration, off-axis clutter, and reverberation clutter remains one of the most important problems in abdominal ultrasound imaging. Multiphase apodization with cross-correlation (MPAX) is a novel beamforming technique that enhances ultrasound image contrast by adaptively suppressing unwanted acoustic clutter. MPAX employs multiple pairs of complementary sinusoidal phase apodizations to intentionally introduce grating lobes that can be used to derive a weighting matrix, which mostly preserves the on-axis signals from tissue but reduces acoustic clutter contributions when multiplied with the beamformed radio-frequency (RF) signals. In this paper, in vivo performance of the MPAX technique was evaluated in abdominal ultrasound using data sets obtained from 10 human subjects referred for abdominal ultrasound at the USC Keck School of Medicine. Improvement in image contrast was quantified, first, by the contrast-to-noise ratio (CNR) and, second, by the rating of two experienced radiologists. The MPAX technique was evaluated for longitudinal and transverse views of the abdominal aorta, the inferior vena cava, the gallbladder, and the portal vein. Our in vivo results and analyses demonstrate the feasibility of the MPAX technique in enhancing image contrast in abdominal ultrasound and show potential for creating high contrast ultrasound images with improved target detectability and diagnostic confidence.

A novel optimized neutrosophic k-means using genetic algorithm for skin lesion detection in dermoscopy images

This paper implemented a new skin lesion detection method based on the genetic algorithm (GA) for optimizing the neutrosophic set (NS) operation to reduce the indeterminacy on the dermoscopy images. Then, k-means clustering is applied to segment the skin lesion regions. Therefore, the proposed method is called optimized neutrosophic k-means (ONKM). On the training images set, an initial value of $$\alpha $$ in the $$\alpha $$ -mean operation of the NS is used with the GA to determine the optimized $$\alpha $$ value. The Jaccard index is used as the fitness function during the optimization process. The GA found the optimal $$\alpha $$ in the $$\alpha $$ -mean operation as $$\alpha _{\mathrm{optimal}} =0.0014$$ in the NS, which achieved the best performance using five fold cross-validation. Afterward, the dermoscopy images are transformed into the neutrosophic domain via three memberships, namely true, indeterminate, and false, using $$\alpha _{\mathrm{optimal}}$$ . The proposed ONKM method is carried out to segment the dermoscopy images. Different random subsets of 50 images from the ISIC 2016 challenge dataset are used from the training dataset during the fivefold cross-validation to train the proposed system and determine $$\alpha _{\mathrm{optimal}}$$ . Several evaluation metrics, namely the Dice coefficient, specificity, sensitivity, and accuracy, are measured for performance evaluation of the test images using the proposed ONKM method with $$\alpha _{\mathrm{optimal}} =0.0014$$ compared to the k-means, and the $$\gamma $$ –k-means methods. The results depicted the dominance of the ONKM method with $$99.29\pm 1.61\%$$ average accuracy compared with k-means and $$\gamma $$ –k-means methods.

Initial evaluation of image performance of a 3-D x-ray system: phantom-based comparison of 3-D tomography with conventional computed tomography.

Phantom-based initial performance assessment of a prototype three-dimensional (3-D) x-ray system and comparison of 3-D tomography with computed tomography (CT) were proposed. A 3-D image quality phantom was scanned with a prototype version of 3-D cone-beam CT imaging implemented on a twin robotic x-ray system using three trajectories (163deg = table, 188deg = upright, and 200deg = side), six tube voltages (60, 70, 81, 90, 100, and 121kV), and four detector doses (0.348, 0.696, 1.740, and [Formula: see text]). CT was obtained with a clinical protocol. Spatial resolution (line pairs/cm) and soft-tissue-contrast resolution were assessed by two independent readers. Radiation dose was assessed. Descriptive and analysis of variance (ANOVA) ([Formula: see text]) were performed. With 3-D tomography, a maximum of 16 lp/cm was visible and best soft-tissue-contrast resolution was 2mm at 30 Hounsfield units (HU) for 160 projections. With CT, 10 lp/cm was visible and soft-tissue-contrast resolution was 4mm at 20 HU. The upright trajectory yielded significantly better spatial resolution and soft tissue contrast, and the side trajectory yielded significantly higher soft tissue contrast than the table trajectory ([Formula: see text]). Radiation dose was higher in 3-D tomography (45 to 704 mGycm) than CT (44 mGycm). Three-dimensional tomography renders overall equal or higher spatial resolution and comparable soft tissue contrast to CT for medium- and high-dose protocols in the side and upright trajectories, but with higher radiation doses.

A Performance Evaluation of a Real-time Image Mosaicing with a Rapid Prototype Hardware

A Performance Evaluation of a Real-time Image Mosaicing with a Rapid Prototype Hardware

Quantitative study of total variation (TV) noise reduction algorithm with chest X-ray imaging

The noise in chest X-ray image causes the decrease in the pulmonary nodule diagnosis accuracy and the increase in the misdiagnosis rate. For this reason, the noise decrease is very important in the diagnosis image, and total variation (TV) noise reduction algorithm was suggested and developed in a very effective method. In this study, we quantitatively evaluated and analyzed image performances as function of acquisition parameter in chest X-ray image using TV noise reduction algorithm. We performed simulation study with MATLAB and experimental study in chest X-ray image for evaluation of image performance. For that purpose, median filter, Anscombe's transform and proposed TV noise reduction algorithm were modeled to apply to each image. We acquired image with respect to the mAs (1.2, 3.6 and 5.9) at fixed 120 kVp. Also, we acquired image with respect to the kVp (70, 90 and 110) at fixed 3.8 mAs. Normalized noise power spectrum (NNPS), coefficient of variation (COV) and contrast to noise ratio (CNR) were used in this study. Both the simulation and experimental results, the noise was greatly improved compared to conventional noise reduction methods when using TV noise reduction algorithm so it was confirmed the NNPS, COV and CNR performances were all improved. In particular to analyze CNR, if TV noise reduction algorithm is used at 1.2 mAs and 70 kVp, it will be greatly improved compared to when using no technique at 5.9 mAs and 110 kVp, which will make a great contribution to the decrease in the exposure dose. In conclusion, as TV noise reduction algorithm can obtain the image performance excellently in the chest X-ray image, it is expected to be used substantially for the field of the diagnostic imaging.

Analiza performansi objektivnih mera procene kvaliteta slika i videa sa potpunim referenciranjem

This paper presents the performance evaluation of image and video quality assessment metrics on two publicly available datasets with subjective quality ratings. In addition to the performance analysis at the global level - at the level of complete datasets, the paper presents the objective measures performance evaluation on subsets of signals inside them. The image dataset contains five subsets created by using different types of JPEG compression, while the video dataset contains six subsets of sequences - four created by compression of original sequences, and two subsets are with video signal transmission characteristic degradations. To determine the success of objective measures, i.e. comparison of subjective and objective quality scores, there were used measures accepted by the International Telecommunication Union - ITU (linear correlation coefficient, rank-order correlation coefficient, mean absolute error, root mean squared error and outlier ratio). It was shown that objective quality measures can reach a high level of agreement with the results of subjective tests on subsets of datasets. Objective measures performances depend on the type of degradation which significantly affects the performance at the complete dataset level. The difference in performances is more pronounced on video sequences due to considerable visual differences in sequences created by using compression, packet losses and additive Gaussian noise. Therefore, we can say that a universal objective measure, i.e. measure that is useful for different types of signal degradation, for different degradation levels, and for different applications currently does not exist.

CBCT 기반 FBP 재구성 변수에 따른 영상 특성 평가의 시뮬레이션 연구

Computed Tomography (CT) 가 발전함에 따라 Cone Beam CT (CBCT) 또한 비약적인 발전이 있었다. CBCT에서 영상화질의 향상을 위해 재구성방법이 사용되어지고 있는데, 영상의 분석학적 영상 재구성 방법 중 Feldkamp (FDK) 방법은 가장 널리 선호되는 알고리즘들 중에 하나이다. 본 연구에서는 MATLAB 프로그램을 통하여 CBCT를 모델링하여 FDK 방법으로 영상을 재구성하고 변수에 따른 영향을 알아보고자 한다. 팬텀은 Shepp-Logan을 사용하였고, 재구성 변수를 다양한 filter의 종류, 대역제한 주파수 그리고 투영 데이터의 수로 설정하였다. 영상의 정량적 평가는 재구성된 3D 데이터 중에서 깊이 방향의 중앙에 있는 단면을 선택하여 profile을 그리고 원본 영상과 비교하였으며, Coefficient of Variation (COV)와 Root Mean Square Error (RMSE)를 사용하였다. 필터의 종류로는 Ram-Lak, Shepp-Logan, Hamming, Hann 필터가 사용되었고 대역제한 주파수는 1.00, 0.75, 0.50, 0.25를 사용하였으며, 투영 데이터의 수는 360개, 180개, 90개, 45개로 정하였다. 결과적으로, Ram-lak filter를 사용했을 때와 대역제한 주파수가 1.00이였을 때, COV가 가장 높았고 RMSE는 가장 낮았다. 또한, 투영 데이터의 수가 360개 일 때 COV와 RMSE는 가장 낮은 값을 나타내었다. 결론적으로 CBCT에서 FDK 영상 재구성 시 변수가 재구성된 영상에 어떤 영향을 미치는지 알 수 있었고, 이는 화질을 결정하는데 주요한 역할을 한다는 것을 증명하였다.

Performance evaluation of image denoising developed using convolutional denoising autoencoders in chest radiography

When processing medical images, image denoising is an important pre-processing step. Various image denoising algorithms have been developed in the past few decades. Recently, image denoising using the deep learning method has shown excellent performance compared to conventional image denoising algorithms. In this study, we introduce an image denoising technique based on a convolutional denoising autoencoder (CDAE) and evaluate clinical applications by comparing existing image denoising algorithms. We train the proposed CDAE model using 3000 chest radiograms training data. To evaluate the performance of the developed CDAE model, we compare it with conventional denoising algorithms including median filter, total variation (TV) minimization, and non-local mean (NLM) algorithms. Furthermore, to verify the clinical effectiveness of the developed denoising model with CDAE, we investigate the performance of the developed denoising algorithm on chest radiograms acquired from real patients. The results demonstrate that the proposed denoising algorithm developed using CDAE achieves a superior noise-reduction effect in chest radiograms compared to TV minimization and NLM algorithms, which are state-of-the-art algorithms for image noise reduction. For example, the peak signal-to-noise ratio and structure similarity index measure of CDAE were at least 10% higher compared to conventional denoising algorithms. In conclusion, the image denoising algorithm developed using CDAE effectively eliminated noise without loss of information on anatomical structures in chest radiograms. It is expected that the proposed denoising algorithm developed using CDAE will be effective for medical images with microscopic anatomical structures, such as terminal bronchioles.

Quantitative performance evaluation of a back-illuminated sCMOS camera with 95% QE for super-resolution localization microscopy.

Scientific Complementary Metal Oxide Semiconductor (sCMOS) cameras were introduced into the market in 2009 and are now becoming a major type of commercial cameras for low-light imaging. sCMOS cameras provide simultaneously low read noise, high readout speed, and large pixel array; however, the relatively low quantum efficiency (QE) of sCMOS cameras has been a major limitation for its application in single molecule imaging, especially super-resolution localization microscopy which requires high detection sensitivity. Here we report the imaging performance of a newly released back-illuminated sCMOS camera (called Dhyana 95 from Tucsen) which is claimed to be the world's first 95% QE sCMOS camera. The imaging performance evaluation is based on a new methodology which is designed to provide paired images from two tested cameras under almost identical experimental conditions. We verified that this new 95% QE sCMOS camera is able to provide superior imaging performance over a representative front-illuminated sCMOS camera (Hamamatsu Flash 4.0 V2) and a popular back-illuminated EMCCD camera (Andor iXon 897 Ultra) in a wide signal range. We hope this study will inspire more studies on using sCMOS cameras in super-resolution localization microscopy, or even single molecule imaging. © 2017 International Society for Advancement of Cytometry.

Performance Evaluation of Modified Color Image Steganography Using Discrete Wavelet Transform

AbstractSteganography is the foremost influential approach to hide data. Images serve as the most appropriate cover media for steganography. This paper intends to do a performance evaluation of color images and its comparison with the recently proposed approaches, using the modified technique already proposed for grayscale images, by the authors. This approach hides large data in color image using the blocking concept. The blocking process is applied on approximation coefficients of secret image and detail coefficients of red, green and blue components of cover image. The blocks of detail coefficients are replaced with approximation coefficients of secret image using root mean square error method. The key is used to store the position of best matching blocks. It is being predicated that the work will be able to hide large data in a single image. The stego image (ST) has better visual quality based on the peak signal to noise ratio values.

Performance evaluation of Bragg coherent diffraction imaging

In this study, we present a numerical framework for modeling three-dimensional (3D) diffraction data in Bragg coherent diffraction imaging (Bragg CDI) experiments and evaluating the quality of obtained 3D complex-valued real-space images recovered by reconstruction algorithms under controlled conditions. The approach is used to systematically explore the performance and the detection limit of this phase-retrieval-based microscopy tool. The numerical investigation suggests that the superb performance of Bragg CDI is achieved with an oversampling ratio above 30 and a detection dynamic range above 6 orders. The observed performance degradation subject to the data binning processes is also studied. This numerical tool can be used to optimize experimental parameters and has the potential to significantly improve the throughput of Bragg CDI method.

Pixel-wise estimation of noise statistics on iterative CT reconstruction from a single scan.

As iterative CT reconstruction continues to advance, the spatial distribution of noise standard deviation (STD) and accurate noise power spectrum (NPS) on the reconstructed CT images become important for method evaluation as well as optimization of algorithm parameters. Using a single CT scan, we propose a practical method for pixel-wise calculation of noise statistics on an iteratively reconstructed CT image, which enables accurate calculation of noise STD for each pixel and NPS. We first derive the noise propagation from measured projections to an iteratively reconstructed CT image provided that the projection noise is known. We then show that the model of noise propagation remains approximately unchanged for extra simulated noise added on the measured projections. To compute the noise STD map and the NPS map on an iteratively reconstructed CT image from a single scan, we first iteratively reconstruct the CT image from the measured projections using an existing reconstruction algorithm. The same measured projections are added by different sets (a total of 32 sets in our implementation) of projection noise simulated from an estimated projection noise model, and are then used to iteratively reconstruct different CT images. The calculations of the noise STD map and the NPS map are finally performed on the entire stack of these different reconstruction images. We evaluate our method on an anthropomorphic head phantom, and demonstrate the clinical utility on a set of head and neck patient CT data, using two iterative CT reconstruction algorithms: the penalized weighted least-square (PWLS) algorithm and the total-variation (TV) regularization. In the head phantom case, repeated scans are acquired to generate the ground truths of noise STD and NPS maps. Using only one single scan, the proposed method accurately calculates the noise STD maps with a root-mean-square error (RMSE) of less than 5HU. In the NPS map estimation, we compare the result of our proposed method with that of the conventional method which calculates the NPS maps on a uniform region of interest on one CT image. Our method outperforms the conventional method on the NPS map estimation with RMSE reduced by 92%. The implementation of the proposed method on the patient data successfully provides the noise STD values around complex structures and a high-quality NPS map. The proposed method accurately calculates noise STD for each pixel and NPS on an iteratively reconstructed CT image, with no requirement of repeated CT scans. It provides a detailed evaluation of imaging performance of different iterative reconstruction methods on the same CT dataset.

电致发光缺陷检测仪的成像性能评估

电致发光缺陷检测仪的成像性能评估

Brain Tumor Analysis of Rician Noise Affected MRI Images

Magnetic Resonance Imaging (MRI) established itself as a key imaging modality in diagnosis and treatment of brain tumors. Automatic segmentation of tumors becomes a tedious task due to complex anatomical brain structure. In addition, presence of noise degrades the quality of MRI scans. MRI images are usually corrupted by Rician noise which would mislead the image analysis algorithms and results in improper diagnosis of the diseases. Also, poor tumor boundary becomes a major hurdle for the subsequent stages of tumor analysis such as: feature extraction, classification and quantification. Classification accuracy mainly depends on quality of the denoised images and sharpness of the tumor boundary. This paper investigates the performance evaluation of different image matting techniques to extract tumor from Rician noise affected MRI brain images. General Terms Medical Image Processing

Evaluation of conventional imaging performance in a research whole-body CT system with a photon-counting detector array

This study evaluated the conventional imaging performance of a research whole-body photon-counting CT system and investigated its feasibility for imaging using clinically realistic levels of x-ray photon flux. This research system was built on the platform of a 2nd generation dual-source CT system: one source coupled to an energy integrating detector (EID) and the other coupled to a photon-counting detector (PCD). Phantom studies were conducted to measure CT number accuracy and uniformity for water, CT number energy dependency for high-Z materials, spatial resolution, noise, and contrast-to-noise ratio. The results from the EID and PCD subsystems were compared. The impact of high photon flux, such as pulse pile-up, was assessed by studying the noise-to-tube-current relationship using a neonate water phantom and high x-ray photon flux. Finally, clinical feasibility of the PCD subsystem was investigated using anthropomorphic phantoms, a cadaveric head, and a whole-body cadaver, which were scanned at dose levels equivalent to or higher than those used clinically. Phantom measurements demonstrated that the PCD subsystem provided comparable image quality to the EID subsystem, except that the PCD subsystem provided slightly better longitudinal spatial resolution and about 25% improvement in contrast-to-noise ratio for iodine. The impact of high photon flux was found to be negligible for the PCD subsystem: only subtle high-flux effects were noticed for tube currents higher than 300 mA in images of the neonate water phantom. Results of the anthropomorphic phantom and cadaver scans demonstrated comparable image quality between the EID and PCD subsystems. There were no noticeable ring, streaking, or cupping/capping artifacts in the PCD images. In addition, the PCD subsystem provided spectral information. Our experiments demonstrated that the research whole-body photon-counting CT system is capable of providing clinical image quality at clinically realistic levels of x-ray photon flux.

A brief survey on multispectral iris recognition

In this survey, we report the current state of the art of multispectral iris recognition methods. We discuss the existing multispectral iris recognition schemes in the contexts of image acquisition, iris segmentation, texture analysis, matching, and performance evaluation. Other important research includes experimental evaluations, image databases, applications and systems, spectral ranges, and multispectral fusion that may affect the multispectral iris recognition.

Blind Estimation of Single Look Side Scan Sonar Image from the Observation Model

The presence of multiplicative speckle noise in side scan sonar images unfavourably affects the detection and classification of ob- jects in the image. This noise is related to the sensing system and the image formation process. Speckle removal is a pre-processing step required in such images for applications like segmentation and registration. Most of the speckle reduction techniques intro- duces a bias after the homomorphic approach is applied on the observation model of the side scan sonar image to convert the multiplicative model into an additive one. In this paper an unscented Kalman filter based approach for single look side scan sonar image estimation from the observation model of the image is proposed. First the noise level in the single look side scan sonar image is estimated using a patch based algorithm and then non blind denoising is done. For the blind denoising part, the heterogeneity of side scan sonar image patches is exploited. Simulation results are given to substantiate the effectiveness of the despeckling method. The different non reference image performance evaluation measures are used to assess the proposed method.

A Rotatable Quality Control Phantom for Evaluating the Performance of Flat Panel Detectors in Imaging Moving Objects.

As the use of diagnostic X-ray equipment with flat panel detectors (FPDs) has increased, so has the importance of proper management of FPD systems. To ensure quality control (QC) of FPD system, an easy method for evaluating FPD imaging performance for both stationary and moving objects is required. Until now, simple rotatable QC phantoms have not been available for the easy evaluation of the performance (spatial resolution and dynamic range) of FPD in imaging moving objects. We developed a QC phantom for this purpose. It consists of three thicknesses of copper and a rotatable test pattern of piano wires of various diameters. Initial tests confirmed its stable performance. Our moving phantom is very useful for QC of FPD images of moving objects because it enables visual evaluation of image performance (spatial resolution and dynamic range) easily.

Optimal Color Estimation by Combining Multiple Spectrodensitometer Measurements

Cultural heritage digitization centers worldwide rely on quality management systems to insure the accuracy and consistency of their digitization efforts. Universal to all digitization quality management is the accurate measurement of color test targets. Testing at the Library of Congress has shown that significant variability exists between manufacturers and models of spectrodensitometers reading standard test targets. This variability has been measured commonly as ΔE2000 3 and as large as ΔE2000 4, leading to improper calibration of imaging systems and less than ideal color accuracy. In this paper we present two robust statistics-based algorithms that estimate the true color from multiple spectrodensitometer measurements, effectively reducing measurement error. The estimated colors can then be regarded as the ground truth for imaging device calibration and performance evaluation. Besides the true color estimation, the second algorithm in particular characterizes the performance of the devices by estimating their bias and variances.