Subjective Image Quality Evaluation Research Articles

Self-calibrated dilated convolutional neural networks for SAR image despeckling

ABSTRACT Synthetic aperture radar (SAR) imaging process is essentially disturbed by speckle noise due to its unique mechanism. Speckle noise causes severe degradation of SAR image quality, which significantly limits its practical application. Recently, convolutional neural networks (CNNs) have indicated good potential for various image processing tasks. In this article, we propose a self-calibrated dilated convolutional neural network for SAR image despeckling called SAR-SCDCNN. The main body of SAR-SCDCNN is formed by seven self-calibrated blocks (SeCaBlock). Firstly, in each SeCaBlock, the input features are split into two branches: one represents the contextual features in the original space, and another represents those in the long-range space. Then, the down-up sampling operation and the convolutions with hybrid dilated rates are employed to enlarge the receptive field. The weights for calculating features themselves are adaptively extracted in the second branch. And then, two branch features are concatenated and fused through a convolution. Finally, a skip connection is used between the input and output of each SeCaBlock to give full play to the expressive power of the deep neural network and enhance training stability. Experiments on synthetic speckled and real SAR images are conducted to perform objective quantitative and subjective visual evaluations of image quality. Results show that our proposed method can effectively suppress speckle noise and adequately preserve detailed features.

QAPP: A quality-aware and privacy-preserving medical image release scheme

The direct release of medical image may face the dilemma: the privacy protection of medical images inevitably affects the visual quality of images. To balance medical image quality and privacy, this paper proposes a quality-aware and privacy-preserving medical image release scheme, QAPP, which effectively integrates the discrete cosine transform (DCT) with differential privacy (DP). Specifically, QAPP is composed of three phases. First, DCT is applied to each medical image to obtain its cosine coefficients matrix. Second, the original cosine coefficients matrix is compressed into k*k cosine coefficients matrix, which can retain the main features of each image. Third, the appropriate Laplace noise is injected into the formed k*k matrix to achieve differential privacy, and these noise-added coefficients are used to reconstruct the noise-added medical images through inverse DCT. Especially, considering there two error sources affecting the image quality in our work: the compression error caused by DCT, and the injected noise error caused by DP, Therefore, a selection function is proposed to determine the optimal compression dimension k, which can minimize the influence of these two errors to improve the visualization quality of the medical image. Subjective and objective image quality evaluation, and extensive experiments of image classification and segmentation using the real medical image dataset demonstrate that the proposed method QAPP can better balance medical image quality and privacy than other similar DP-based methods.

Clinical Application of "Three-Low" Technique Combined with Artificial Intelligence Iterative Reconstruction Algorithm in Aortic CT Angiography

To explore the application value of the "three-low" technique (low radiation dose, low contrast agent dosage and low contrast agent flow rate) combined with artificial intelligence iterative reconstruction (AIIR) in aortic CT angiography (CTA). A total of 33 patients who underwent aortic CTA were prospectively enrolled. Based on the time of their follow-up examinations, the imaging data were divided into Group A and Group B, with Group A being the control group (100 kV, 0.8 mL/kg, 5 mL/s) and Group B being the "three-low" technique group (70 kV, 0.5 mL/kg, 3 mL/s). In group A, the images were reconstructed by Karl iterative algorithm. Group B was divided into B1 and B2 subgroups, with their images being reconstructed by Karl iterative algorithm and AIIR, respectively. The CT and SD values of the ascending aorta, descending aorta, abdominal aorta, left common iliac artery and right common iliac artery were measured, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The subjective scoring of image quality was performed. The radiation dose parameters were documented. Differences in the CT value, SD value, SNR and CNR of the three groups were statistically significant ( P<0.001). The CT value, SNR and CNR of group B2 were significantly higher than those of group B1, while the SD value of group B2 was significantly lower than that of group B1 ( P<0.017). There was no significant difference between the CT values of group A and those of group B2 ( P>0.017). The SD values, SNR and CNR in group B2 were better than those in group A ( P>0.017). There was significant difference in the subjective evaluation of image quality among the three groups ( P<0.05), but there was no significant difference between group A and group B2 ( P>0.017). The radiation dose and contrast medium dosage in group B decreased 84.14% and 37.08%, respectively, compared with those of group A. With the "three-low" technique combined with AIIR algorithm, the image quality of aortic CTA obtained is comparable to that of conventional dose scanning, while the radiation dose, contrast agent dosage and contrast agent flow rate of patients are significantly reduced.

Optimization of scan parameters to reduce acquisition time for RESOLVE-based diffusion kurtosis imaging (DKI) in nasopharyngeal carcinoma (NPC).

To shorten acquisition time of readout segmentation of long variable echo trains (RESOLVE)-based diffusion kurtosis imaging (DKI) via Readout Partial Fourier (RPF) and b-value combinations. The RESOLVE-based DKI images of 38 patients with nasopharyngeal carcinoma (NPC) were prospectively enrolled. For RESOLVE-based DKI images with 5/8 RPF and without RPF, objective and subjective evaluations of image quality were performed. A total of nine groups with different b-value combinations were simulated, and the influence of different b-value combinations for RESOLVE-RPF-based DKI sequences was assessed using the intraclass correlation coefficient (ICC). The mean values of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in DKI images without RPF were higher than those with 5/8 RPF (252.9 ± 77.7 vs 247.3 ± 85.5 and 5.8 ± 2.8 vs 5.4 ± 2.3, respectively), but not significantly (p = 0.460 and p = 0.180, respectively). In comparing the ICCs between nine groups of different b-value combinations in RESOLVE-RPF-based DKI, group (200, 800, 2000 s/mm2), group (200, 400, 800, 2000 s/mm2) and group (200, 800, 1500, 2000 s/mm2) were not significantly different (p > 0.001) and showed excellent agreement (0.81-1.00) with that of group (200, 400, 800, 1500, 2000 s/mm2). Using b-value optimization and RPF technology, the group with RPF (200, 400, 800, 2000 s/mm2) showed a 56% reduced scanning compared with the group without RPF (200, 400, 800, 1500, 2000 s/mm2; 3 min 46 s vs 8 min 31 s, respectively). DKI with RPF did not significantly affect image quality, but both RPF and different b-value combinations can affect the scanning time. The combination of RPF and b-value optimization can ensure the stability of DKI parameters and reduce the scanning time by 56%. This work is to optimize scan parameters, e.g. RPF and b-value combinations, to reduce acquisition time for RESOLVE-based DKI in NPC. To our knowledge, the effect of RESOLVE-RPF and b-value combinations on DKI has not been reported.

Study on the Relationship between Different Body Mass Indexes and Puncture Pain and Image Quality in Patients Undergoing Coronary Angiography with Intravenous Indwelling Needle.

Objective To investigate the effect of different body mass indexes (BMIs) on patients' puncture pain, puncture success rate, and image quality in coronary angiography (CAG) performed with an intravenous indwelling needle, and to provide a basis for selecting the appropriate intravenous indwelling needle for CAG in patients with different BMIs in an outpatient clinic. Method In this study, 300 patients undergoing CTA at the department of radiology in the First Affiliated Hospital of Wenzhou Medical University from January to May 2021 were divided into group 1 (BMI 1 ≤ 18.5), group 2 (18.5 < BMI ≤ 24), and group 3 (BMI > 24) according to their BMI, with 100 cases in each group, and a 20 G intravenous indwelling needle was used in each group. The age, sex, height, and weight of each patient were recorded, and the primary puncture success rate, contrast leakage rate, injection success rate, pain perception, and subjective ratings of image quality and objective indicators were compared in patients with different BMI values. Results There was no statistically significant difference between the age, gender, and heart rate of the patients in the three groups (P > 0.05). There was no statistically significant difference between the primary puncture success rate, injection success rate, and contrast leakage rate of the three groups of patients (P > 0.05). The pain scores of group 3 during contrast injection were significantly higher than those of the remaining two groups (P < 0.05), while the differences between the pain scores of group 2 and group 1 during contrast injection were not statistically significant (P > 0.05); the comparison of the pain scores of the three groups during puncture and during retention was not statistically significant (P > 0.05). The differences between the subjective ratings of image quality and the objective indicators of the three groups were not statistically significant (P > 0.05). Conclusion The 20 G indwelling needle can basically meet the coronary angiography examination of patients with different body mass indexes, but patients with a BMI greater than 24 are recommended to use a larger diameter indwelling needle to reduce contrast leakage as well as to reduce patient pain and improve patient comfort.

Image Quality Assessment Based on Three Features Fusion in Three Fusion Steps

The objective image quality assessment (IQA) method was developed to replace subjective observer image quality evaluations in various applications. A reliable full reference color IQA method that allows reference and distorted images to be compared in a symmetric way is designed via three fusion steps described in this article. The three fusion steps include luminance channels fusion, similarity maps fusion, and features fusion. A fusion weight coefficient is designed to fuse the luminance channels of input images as an enhancement operator for features. The extracted SR (spectral residual), gradient, and chrominance features, by means of symmetric calculations for the reference and distorted images, are conducted via similarity fusion processing. Then, based on the human visual system (HVS) characteristics of achromatic and chromatic information receiving, a features fusion map represents the weighted sum of three similarity fusion maps. Finally, a deviation pooling strategy is utilized to export the quality score after features fusion. The novel method is called the features fusion similarity index (FFS). Various experiments are carried out based on statistical evaluation criteria to optimize the parameters of FFS, after which the proposed method of FFS is compared with other state-of-the-art IQA methods using large-scale benchmark single distortion databases. The results show that FFS performs with higher consistency with respect to subjective scores in terms of prediction accuracy, e.g., the PLCC can achieve at least 0.9116 accuracy and at most 0.9774 accuracy for four databases. In addition, the average running time of FFS is 0.0657 s—a value representing a higher computational efficiency.

(Retracted) Infrared image filtering and enhancement processing method based upon image processing technology

Due to the processing method used in many existing image enhancement algorithms, some inherent noise in the image is amplified when the image is enhanced globally or locally. The quality of infrared images directly affects the wide application of infrared imaging technology, yet the quality of infrared image depends largely on the advanced nature and correct application of infrared image processing technology (IPT). This research mainly discusses the infrared image filtering enhancement processing method based on IPT. We briefly summarize the research significance, application fields and research status of the image registration and enhancement. We summarize the commonly used methods. We use a contrast saliency filter to obtain the region of interest and perform statistical segmentation on the region to make it more suitable for observation and subsequent processing. No matter what the background environment is, the image detail enhancement algorithm enhances all the details in the image in a balanced manner. Therefore, we adopt the scheme of separating the background and the details to avoid the influence of the background difference on the detail display effect in other enhancement methods. Objective experiments have also proved that it is difficult to achieve consistency between subjective and objective evaluation of image quality. Therefore, the evaluation of processed image quality still needs to combine subjective visual effects and objective evaluation. We use optimized hierarchical differential expression theory to solve the acquired saliency region and obtain the differential vector that realizes the gray difference amplification of this region. The dynamic range of the original image is very narrow, roughly concentrated in [0, 50]. The gray range of the image processed by the enhancement algorithm has been well expanded, basically covering [0, 255]. The proposed infrared image enhancement method achieves a better visual enhancement effect.

Efficient method for real-time range enhancement of electro-optical imaging system

Electro-optical (EO) imaging sensors are employed for object detection, recognition, identification, and tracking. The observation range of EO systems is defined as the maximum distance at which a target of interest can be seen and perceived. The performance of these systems deteriorates under degraded weather conditions, which severely affects the image quality and thus the observation range of EO surveillance systems. An improved image quality results in an enhanced observation range. Image quality can be improved by applying suitable digital image enhancement techniques by considering target/background signature knowledge. Under bad weather conditions, these digital techniques become ineffective due to the presence of scattered background signals. To reduce the effect of scattering, optical methods are also used to mitigate degradation caused by the atmosphere. It is observed that the optical method alone cannot produce the desired result. Observation range enhancement in surveillance system is achieved using the proposed hybrid method, which utilizes the optimal combination of optical and digital techniques in real-time. The proposed method not only improves human visual perception but also reveals more image content for better image analysis. The performance of the proposed range enhancement technique is compared with the predicted observation range model. The processed images using the hybrid method show more than 30% observation range enhancement under bad weather conditions. In addition to the subjective evaluation, qualitative and quantitative evaluations of image quality achieved through the proposed method are also presented. It is observed that the proposed hybrid method significantly improves the observation range and image quality of EO sensors under degraded weather in real time.

Comparison of 2D and 3D quiescent-interval slice-selective non-contrast MR angiography in patients with peripheral artery disease.

To evaluate the potential clinical benefit of the superior spatial resolution of 3D prototype thin-slab stack-of-stars (tsSOS) quiescent-interval slice-selective (QISS) MRA over standard 2D-QISS MRA for the detection peripheral artery disease (PAD), using computed tomography angiography (CTA) as reference. Twenty-three patients (70 ± 8 years, 18 men) with PAD who had previously undergone run-off CTA were prospectively enrolled. Patients underwent non-contrast MRA using 2D-QISS and tsSOS-QISS at 1.5 T. Eighteen arterial segments were evaluated for subjective and objective image quality (normalized signal-to-noise, nSNR), vessel sharpness, and area under the curve (AUC) for > 50% stenosis detection. Overall subjective image quality ratings for the entire run-off were not different between tsSOS-QISS and 2D-QISS (3 [3; 4] vs 4 [3; 4], respectively; P = 0.813). Sharpness of primary branch vessels demonstrated improved image quality using tsSOS-QISS compared with 2D-QISS (4 [3; 4] vs 3 [2; 3], P = 0.008). Objective image quality measures were not different between 2D-QISS and tsSOS-QISS (nSNR 5.0 ± 1.9 vs 4.2 ± 1.8; P = 0.132). AUCs for significant stenosis detection by tsSOS-QISS and 2D-QISS were 0.877 and 0.856, respectively (P = 0.336). The prototype 3D tsSOS-QISS technique provides similar accuracy in patients with PAD to a standard commercially available 2D-QISS technique, indicating that the use of relatively thick slices does not limit the diagnostic performance of 2D-QISS. However, subjective image quality for branch vessel depiction is improved using the 3D approach.

Overview and research progress of no reference image quality evaluation methods

Image quality will greatly affect the acquisition of human visual information, but the image will inevitably cause a certain degree of distortion in the process of transmission, compression, coding and so on. At this time, the accuracy of image quality evaluation (IQA) is particularly important, which also makes IQA an important research direction in the field of image processing. IQA methods are divided into subjective image quality evaluation method and objective image quality evaluation method.there are many subjective evaluation methods, and the international telecommunications union (ITU) has also proposed many subjective evaluation methods and standards. there are three most commonly used methods, namely, the double stimulation continuous quality classification method, the single stimulation continuous quality classification method and the double stimulation damage classification method. Correspondingly, there are three kinds of objective evaluation methods, full reference, half reference and no reference. At present, this paper selects the research focus of IQA field-no reference image quality evaluation method as the main research object, summarizes the advantages and disadvantages of each non-reference evaluation method, and prospects the future development of the non-reference image quality evaluation method.

Image Quality Assessment for Clinical Cadmium Telluride-Based Photon-Counting Computed Tomography Detector in Cadaveric Wrist Imaging.

Detailed visualization of bone microarchitecture is essential for assessment of wrist fractures in computed tomography (CT). This study aims to evaluate the imaging performance of a CT system with clinical cadmium telluride-based photon-counting detector (PCD-CT) compared with a third-generation dual-source CT scanner with energy-integrating detector technology (EID-CT). Both CT systems were used for the examination of 8 cadaveric wrists with radiation dose equivalent scan protocols (low-/standard-/full-dose imaging: CTDIvol = 1.50/5.80/8.67 mGy). All wrists were scanned with 2 different operating modes of the photon-counting CT (standard-resolution and ultra-high-resolution). After reformatting with comparable reconstruction parameters and convolution kernels, subjective evaluation of image quality was performed by 3 radiologists on a 7-point scale. For estimation of interrater reliability, we report the intraclass correlation coefficient (absolute agreement, 2-way random-effects model). Signal-to-noise and contrast-to-noise ratios were calculated to provide semiquantitative assessment of image quality. Subjective image quality of standard-dose PCD-CT examinations in ultra-high-resolution mode was superior compared with full-dose PCD-CT in standard-resolution mode (P = 0.016) and full-dose EID-CT (P = 0.040). No difference was ascertained between low-dose PCD-CT in ultra-high-resolution mode and standard-dose scans with either PCD-CT in standard-resolution mode (P = 0.108) or EID-CT (P = 0.470). Observer evaluation of standard-resolution PCD-CT and EID-CT delivered similar results in full- and standard-dose scans (P = 0.248/0.509). Intraclass correlation coefficient was 0.876 (95% confidence interval, 0.744-0.925; P < 0.001), indicating good reliability. Between dose equivalent studies, signal-to-noise and contrast-to-noise ratios were substantially higher in photon-counting CT examinations (all P's < 0.001). Superior visualization of fine anatomy is feasible with the clinical photon-counting CT system in cadaveric wrist scans. The ultra-high-resolution scan mode suggests potential for considerable dose reduction over energy-integrating dual-source CT.

24.2: Invited Paper: Study on Influential Factors of Image Quality for Laser Projection Television

There are some important attributes influencing image quality of the laser projection television, such as white level, black level, color saturation, and contour rendering. In this paper, just noticeable differences of these four attributes are measured by the method of the two alternative forced choice with the staircase method of 1 up 2 down. The similarities and differences of just noticeable differences of the four attributes between laser projection television and liquid crystal display are compared and analyzed. Based on experimental results, a subjective image quality evaluation model can be established, which is helpful for the optimization of image quality.

Blind Image Quality Assessment for a Single Image From Text-to-Image Synthesis

A fundamental bottleneck in text-to-image synthesis is that there are rarely subjective quality evaluation metrics for a single generated image. To address this issue, this paper proposed a procedure to evaluate the single generated image, which includes a specific dataset named multiple metrics quality assessment for birds(MMQA Birds) and a learning model named blind generated image evaluator(BGIE). The motivation of our proposal is twofold. On the one hand, subjective image quality evaluation is a human perceptual task; Therefore, it tends to be a process of supervised learning. To the best of our knowledge, there are not any datasets for this study. Thus, we handle this problem via designing a specific dataset. On the other hand, we observed that the spatial content of generated image attracts more attention when humans judge its quality; According to this finding, an efficient machine-learning model that combines both pixel-level features and spatial features is proposed. Extensive experiments manifest our method can solve this problem to some extent. In the generated image dataset, BGIE surpasses the state-of-art NSS-based method by 6.3% in PLCC and SRCC. In practice, we further discuss the rationality of the MMQA Birds dataset and the application of BGIE. It proves that both in subjective and objective aspects, our method achieves convincing results.

NDNetGaming - development of a no-reference deep CNN for gaming video quality prediction

Gaming video streaming services are growing rapidly due to new services such as passive video streaming of gaming content, e.g. Twitch.tv, as well as cloud gaming, e.g. Nvidia GeForce NOW and Google Stadia. In contrast to traditional video content, gaming content has special characteristics such as extremely high and special motion patterns, synthetic content and repetitive content, which poses new opportunities for the design of machine learning-based models to outperform the state-of-the-art video and image quality approaches for this special computer generated content. In this paper, we train a Convolutional Neural Network (CNN) based on an objective quality model, VMAF, as ground truth and fine-tuned it based on subjective image quality ratings. In addition, we propose a new temporal pooling method to predict gaming video quality based on frame-level predictions. Finally, the paper also describes how an appropriate CNN architecture can be chosen and how well the model performs on different contents. Our result shows that among four popular network architectures that we investigated, DenseNet performs best for image quality assessment based on the training dataset. By training the last 57 convolutional layers of DenseNet based on VMAF values, we obtained a high performance model to predict VMAF of distorted frames of video games with a Spearman’s Rank correlation (SRCC) of 0.945 and Root Mean Score Error (RMSE) of 7.07 on the image level, while achieving a higher performance on the video level leading to a SRCC of 0.967 and RMSE of 5.47 for the KUGVD dataset. Furthermore, we fine-tuned the model based on subjective quality ratings of images from gaming content which resulted in a SRCC of 0.93 and RMSE of 0.46 using one-hold-out cross validation. Finally, on the video level, using the proposed pooling method, the model achieves a very good performance indicated by a SRCC of 0.968 and RMSE of 0.30 for the used gaming video dataset.

Carotid and cerebrovascular dual-energy computed tomography angiography: Optimization of window settings for virtual monoenergetic imaging reconstruction

PurposeDedicated post-processing of dual-energy computed tomography angiography (DE-CTA) datasets has been shown to allow for increased vascular contrast. The goal of our study was to define optimal window settings for displaying virtual monoenergetic images (VMI) reconstructed from dual-energy carotid and cerebrovascular DE-CTA. MethodsFifty-seven patients who underwent clinically-indicated carotid and cerebrovascular third-generation dual-source DE-CTA were retrospectively evaluated. Standard linearly-blended (M_0.6), 70-keV traditional VMI (M70), and 40-keV noise-optimized VMI (M40+) reconstructions were analyzed. For M70 and M40+ datasets, the subjectively best window setting (width and level, B–W/L) was independently determined by two observers and subsequently related with aortic arch attenuation to calculate optimized values (O–W/L) using linear regression. Subjective evaluation of image quality (IQ) between W/L settings were assessed by two additional readers. Repeated measures analysis of variance were performed to compare W/L settings and IQ indices between M_0.6, M70, and M40 + . ResultsB–W/L and O–W/L for M70 were 580/210 and 560/200, and for M40+ were 1630/570 and 1560/550, respectively, higher than standard DE-CTA W/L settings (450/100). Highest subjective scores were observed for M40+ regarding overall IQ (all p < 0.001). ConclusionApplication of O–W/L settings is mandatory to optimize subjective IQ of VMI reconstructions of DE-CTA.

A robust blind watermarking algorithm for depth-image-based rendering 3D images

The protection of 3D contents from illegal distribution has attracted considerable attention and depth-image-based rendering (DIBR) is proved to be a promising technology for 3D image and video displaying. In this paper, we propose a new digital watermarking scheme for DIBR 3D images based on feature regions and ridgelet transform (RT). In this scheme, the center view and the depth map are made available at the content provider side. After selecting the reference points of the center view, we construct the feature regions for watermark embedding. Considering the sparse image representation and directional sensitivity of the RT, the watermark bits are embedded into the amplitudes of the ridgelet coefficients of the most energetic direction. The virtual left and virtual right views are generated from the watermarked center view and the associated depth map at the content consumer side. The watermarked view has good perceptual quality under both the objective and subjective image quality evaluations. The embedded watermark can be detected blindly with low bit error rate (BER) from the watermarked center view, the synthesized left and right views even when the views are distorted and distributed separately. The experimental results demonstrate that the proposed scheme exhibits good performance in terms of robustness against various image processing attacks. Meanwhile, our method can be robust to common DIBR processing, such as depth image variation, baseline distance adjustment and different rendering conditions. Furthermore, compared with other related and state-of-the-art methods, the proposed algorithm shows higher accuracy in watermark extraction.

Study on Influential Factors of Image Quality for Laser Projection Television

There are some important attributes influencing image quality of the laser projection television, such as white level, black level, color saturation, and contour rendering. In this paper, just noticeable differences of these four attributes are measured by the method of the two alternative forced choice with the staircase method of 1 up 2 down. The similarities and differences of just noticeable differences of the four attributes between laser projection television and liquid crystal display are compared and analyzed. Based on experimental results, a subjective image quality evaluation model can be established, which is helpful for the optimization of image quality.

Can Dual-energy CT-based Virtual Monoenergetic Imaging Improve the Assessment of Hypodense Liver Metastases in Patients With Hepatic Steatosis?

To evaluate the impact of noise-optimized virtual monoenergetic imaging (VMI) on lesion demarcation and measuring accuracy of hypoattenuating liver metastases in patients with fatty liver disease compared to standard reconstructions. Twenty-eight patients (mean age 62.2 ± 7.7 years) with fatty liver disease and hypoattenuating liver metastases who underwent unenhanced and contrast-enhanced portal-venous dual-energy CT (DECT) were enrolled. Standard linearly blended and VMI series were reconstructed in 10-keV intervals. Lesion-to-parenchyma contrast-to-noise ratio (CNR) was calculated and the best VMI series was further investigated in a subjective evaluation of overall image quality and lesion demarcation. Size measurements were performed independently by measuring all hypodense lesions (n = 58) twice in a predefined sequence. Inter- and intra-rater agreement was assessed using intra-class correlation coefficient (ICC) statistics. The calculated CNR was greatest at 40-keV VMI (4.3 ± 2.6), significantly higher compared to standard reconstructions (2.9 ± 1.9; p < 0.001). Subjective ratings for overall image quality showed no significant difference between the 2 reconstruction techniques (both medians 4; p = 0.147), while lesion margin demarcation was found to be superior for 40-keV VMI (median 5; p ≤ 0.001). Inter- (ICC, 0.98 for 40-keV VMI; ICC, 0.93 for standard reconstruction) and intra-rater (ICC, 0.99 for 40-keV VMI; ICC, 0.94 for standard image series) analysis showed an excellent agreement for lesion measurements in both reconstruction techniques. Noise-optimized VMI reconstructions significantly improve contrast and lesion demarcation of hypoattenuating liver metastases in patients with the fatty liver disease compared to standard reconstruction.

FPGA Acceleration of Color Interpolation Algorithm Based on Gradient and Color Difference

To quickly and efficiently achieve a high-quality color image on the embedded platform, designed and used FPGA to complete Bayer pattern image Color interpolation. In this paper, we proposed a color interpolation algorithm based on image gradient direction and color difference. The algorithm calculates the G component according to the gradient value and color difference at the pixel to be interpolated, and then calculates the missing R component and B component based on the G component interpolation. Finally, the pixels of the image boundary area are interpolated. Tested on the Kodak dataset, the experimental results show that the subjective visual quality and objective image quality evaluation of the algorithm is superior to the conventional algorithm. On the other hand, and reduce problems such as image aliasing and edge blurring. The proposed algorithm has been applied to the actual Xilinx’s FPGA platform, a real-time speed of 120 MHz, enabling real-time image color Interpolation.

Relationship between CNR and visibility of anatomical structures of cone-beam computed tomography images under different exposure parameters.

The purpose of this study was to investigate the image quality of cone beam CT (CBCT) under different exposure parameters and the relationship between contrast-to-noise and visibility of eight anatomical structures. CBCT images for the evaluation of subjective image quality were acquired on an anthropopathic phantom containing a human skeleton embedded in soft tissue equivalent materials using 25 exposure protocols. Visibility of eight anatomical structures was evaluated by five independent observers. Using the SEDENTEXCT IQ Image Quality phantom, the contrast-to-noise ratio (CNR) was calculated by ImageJ software. A reduction on the visibility of anatomical structures was seen under lower exposure parameters. However, for 84% of the protocols, visibility of anatomical structures remained acceptable even under some lower parameter settings. As CNR increased, the visibility of anatomical structures also increased correspondingly. A change point could be found in the CNR interval 29.42-36.51 after which the visibility of anatomical structures no longer increases with the increase of CNR. Although CNR decrease under a lower exposure parameter, the image quality often remained acceptable at exposure levels below the manufacture's recommended settings. It is possible to standardize subjective image quality by physical factors. Currently, it is not possible to predetermine a change point CNR value due to different CBCT machine and variation of diagnostic tasks.