Subjective Image Quality Measures Research Articles

Training ESRGAN with multi-scale attention U-Net discriminator

In this paper, we propose MSA-ESRGAN, a novel super-resolution model designed to enhance the perceptual quality of images. The key innovation of our approach lies in the integration of a multi-scale attention U-Net discriminator, which allows for more accurate differentiation between subject and background areas in images. By leveraging this architecture, MSA-ESRGAN surpasses traditional methods and several state-of-the-art super-resolution models in terms of Natural Image Quality Evaluator (NIQE) scores as well as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) across various benchmark datasets, including BSD100, Set5, Set14, Urban100, and OST300. Additionally, subjective evaluations further confirm the enhanced visual quality delivered by MSA-ESRGAN, particularly in terms of preserving texture and overall image realism. To ensure a fair comparison with Real-ESRGAN, we initialized our generator with a pre-trained Real-ESRNET model and followed the same training setup. Our model was trained on the DIV2K dataset using high-resolution image patches and the Adam optimizer, incorporating exponential moving average (EMA) for stability and performance enhancement. Evaluations on multiple benchmark datasets demonstrate that MSA-ESRGAN consistently delivers superior perceptual quality, as evidenced by higher NIQE, PSNR, and SSIM scores compared to other methods. Specifically, our model shows significant improvements in both objective and subjective measures of image quality. Furthermore, an ablation study highlighted the critical role of our multi-scale attention U-Net discriminator in enhancing the model’s performance. The results underscore the effectiveness of MSA-ESRGAN in maintaining image naturalness and perceptual quality, providing a robust benchmark for blind super-resolution tasks.

Evaluating Text-to-Image GANs Performance: A Comparative Analysis of Evaluation Metrics

Generative Adversarial Networks (GANs) have emerged as powerful techniques for generating high-quality images in various domains but assessing how realistic the generated images are is a challenging task. To address this issue, researchers have proposed a variety of evaluation metrics for GANs, each with its own strengths and limitations. This paper presents a comprehensive analysis of popular GAN evaluation metrics, including FID, Mode Score, Inception Score, MMD, PSNR, and SSIM. The strengths, weaknesses, and calculation processes of these metrics are discussed, focusing on assessing image fidelity and diversity. Two approaches, pixel distance, and feature distance, are employed to measure image similarity, while the importance of evaluating individual objects using input captions is emphasized. Experimental results on a basic GAN trained on the MNIST dataset demonstrate improvement in various metrics across different epochs. The FID score decreases from 497.54594 at Epoch 0 to 136.91156 at Epoch 100, indicating improved differentiation between real and generated images. In addition, the Inception Score increases from 1.1533 to 1.6408, reflecting enhanced image quality and diversity. These findings highlight the effectiveness of the GAN model in generating more realistic and diverse images with training progression. However, when it comes to evaluating GANs on complex datasets, challenges arise, highlighting the need to combine evaluation metrics with visual inspection and subjective measures of image quality. By adopting a comprehensive evaluation approach, researchers can gain a deeper understanding of GAN performance and guide the development of advanced models.

Abdominopelvic CT Image Quality: Evaluation of Thin (0.5-mm) Slices Using Deep Learning Reconstruction.

BACKGROUND. Because thick-section images (typically 3-5 mm) have low image noise, radiologists typically use them to perform clinical interpretation, although they may additionally refer to thin-section images (typically 0.5-0.625 mm) for problem solving. Deep learning reconstruction (DLR) can yield thin-section images with low noise. OBJECTIVE. The purpose of this study is to compare abdominopelvic CT image quality between thin-section DLR images and thin- and thick-section hybrid iterative reconstruction (HIR) images. METHODS. This retrospective study included 50 patients (31 men and 19 women; median age, 64 years) who underwent abdominopelvic CT between June 15, 2020, and July 29, 2020. Images were reconstructed at 0.5-mm section using DLR and at 0.5-mm and 3.0-mm sections using HIR. Five radiologists independently performed pairwise comparisons (0.5-mm DLR and either 0.5-mm or 3.0-mm HIR) and recorded the preferred image for subjective image quality measures (scale, -2 to 2). The pooled scores of readers were compared with a score of 0 (denoting no preference). Image noise was quantified using the SD of ROIs on regions of homogeneous liver. RESULTS. For comparison of 0.5-mm DLR images and 0.5-mm HIR images, the median pooled score was 2 (indicating a definite preference for DLR) for noise and overall image quality and 1 (denoting a slight preference for DLR) for sharpness and natural appearance. For comparison of 0.5-mm DLR and 3.0-mm HIR, the median pooled score was 1 for the four previously mentioned measures. These assessments were all significantly different (p < .001) from 0. For artifacts, the median pooled score for both comparisons was 0, which was not significant for comparison with 3.0-mm HIR (p = .03) but was significant for comparison with 0.5-mm HIR (p < .001) due to imbalance in scores of 1 (n = 28) and -1 (slight preference for HIR, n = 1). Noise for 0.5-mm DLR was lower by mean differences of 12.8 HU compared with 0.5-mm HIR and 4.4 HU compared with 3.0-mm HIR (both p < .001). CONCLUSION. Thin-section DLR improves subjective image quality and reduces image noise compared with currently used thin- and thick-section HIR, without causing additional artifacts. CLINICAL IMPACT. Although further diagnostic performance studies are warranted, the findings suggest the possibility of replacing current use of both thin- and thick-section HIR with the use of thin-section DLR only during clinical interpretations.

Detection of Incidental Nonosseous Thoracic Pathology on State-of-the-Art Ultralow-Dose Protocol Computed Tomography in Pediatric Patients With Pectus Excavatum.

The aim of the study was to compare a pediatric ultralow-dose pectus excavatum computed tomography (CT) protocol versus standard-dose pediatric thoracic CT in terms of radiation dose, subjective and objective image quality, and its ability to detect incidental nonosseous thoracic pathology compared with imaging and clinical reference. A single institution radiology database identified a total of 104 ultralow-dose pediatric thoracic CT cases with an equal number of age-matched standard-dose chest CT cases also selected for retrospective analysis. Objective image quality (contrast-to-noise and signal-to-noise ratios) and radiation dose were assessed. Qualitative Likert scorings of the bone, lung, and soft tissues were performed by 2 expert radiologists. Electronic health records of the ultralow-dose cohort were reviewed for at least 1 year to evaluate for potentially missed thoracic pathology and symptoms. Variables were compared using parametric and nonparametric tests in R software 4.0.5. The ultralow-dose protocol group had statistically significant reductions (P < 0.001) in the volume CT dose index (0.31 ± 0.19 vs 2.20 ± 1.64 mGy), effective radiation dose (0.14 ± 0.08 vs 1.07 ± 0.86 mSv), and size-specific dose estimates (0.50 ± 0.30 vs 3.43 ± 2.56 mGy) compared with the standard protocol, yielding an 86.51% and 85.32% reduction, respectively. The signal-to-noise ratio (20.49 ± 6.19 vs 36.48 ± 10.20), contrast-to-noise (21.65 ± 6.57 vs 38.47 ± 10.59), and subjective measures of image quality (lung parenchyma [3.07 ± 0.92 vs 4.42 ± 0.47], bony structures [3.30 ± 0.86 vs 4.52 ± 0.51], and surrounding soft tissues [2.57 ± 0.63 vs 3.89 ± 0.65]) were also significantly lower in the ultralow-dose protocol (P < 0.001). No differences were seen in the number and size of pulmonary nodules between groups. Clinical and imaging follow of all 104 patients undergoing ultralow-dose CT demonstrated no evidence of missed thoracic pathology causing symptoms. Ultralow-dose thoracic CT is an acceptable modality for imaging pediatric patients with pectus excavatum and other conditions primarily causing osseous pathology, with effective radiation dose comparable to plain radiographs and a moderate increase in image noise that did not significantly reduce its ability to detect incidental nonosseous thoracic pathology.

Comparison of Sensitivity Encoding (SENSE) and Compressed Sensing-SENSE for Contrast-Enhanced T1-Weighted Imaging in Patients With Crohn Disease Undergoing MR Enterography.

BACKGROUND. Long acquisition times for breath-hold contrast-enhanced (CE) T1-weighted imaging in MR enterography (MRE) protocols result in reduced image quality. OBJECTIVE. The purpose of this study was to compare CE T1-weighted imaging performed using sensitivity encoding (SENSE) and compressed sensing-SENSE (CS-SENSE) in terms of image quality and diagnostic performance for active inflammation in Crohn disease (CD). METHODS. This retrospective study included 41 patients (31 men, 10 women; mean age, 34 ± 12 [SD] years) who underwent MRE for known or suspected CD between June 2020 and September 2020. MRE was performed in one of two scanning rooms depending on scheduling availability. Per institutional protocol, in one room, the enteric phase was acquired using SENSE (acceleration factor, 3) and the portal phase was acquired using CS-SENSE (acceleration factor, 5); this order was reversed in the other room. Two radiologists independently assessed sequences for subjective image quality measures at the patient level and for active inflammation at the bowel-segment level. Mean image quality scores between readers were computed. Diagnostic performance for active inflammation was compared between SENSE and CS-SENSE using generalized estimating equations; a separate experienced radiologist reviewed the full MRE protocol to establish the reference standard. RESULTS. The mean acquisition time of CE T1-weighted imaging was 17.2 ± 1.1 seconds for SENSE versus 11.5 ± 0.8 seconds for CS-SENSE (p < .001). CS-SENSE scored significantly better than SENSE in overall image quality (4.2 ± 0.7 vs 3.7 ± 1.1; p = .02), motion artifacts (4.0 ± 0.8 vs 3.6 ± 1.2; p = .006), and aliasing artifacts (4.8 ± 0.4 vs 4.2 ± 0.6; p < .001). CS-SENSE scored significantly worse than SENSE in synthetic appearance (4.6 ± 0.5 vs 4.8 ± 0.4; p = .003). Contrast, sharpness, and blurring were not different between sequences (p > .05). For reader 1, CS-SENSE, compared with SENSE, showed a sensitivity of 86% versus 81% (p = .09), specificity of 88% versus 83% (p = .08), and accuracy of 87% versus 82% (p = .56). For reader 2, CS-SENSE, compared with SENSE, showed a sensitivity of 92% versus 79% (p = .006), specificity of 90% versus 98% (p = .16), and accuracy of 91% versus 86% (p = .002). CONCLUSION. Use of CS-SENSE for CE T1-weighted imaging in MRE protocols results in reduced scan times with reduced artifact and improved image quality. CLINICAL IMPACT. The benefits of CS-SENSE in MRE protocols may improve the diagnostic performance for active inflammation in CD.

An Ultrasound Image Enhancement Method Using Neutrosophic Similarity Score.

Ultrasound images, having low contrast and noise, adversely impact in the detection of abnormalities. In view of this, an enhancement method is proposed in this work to reduce noise and improve contrast of ultrasound images. The proposed method is based on scaling with neutrosophic similarity score (NSS), where an image is represented in the neutrosophic domain through three membership subsets T, I, and F denoting the degree of truth, indeterminacy, and falseness, respectively. The NSS measures the belonging degree of pixel to the texture using multi-criteria that is based on intensity, local mean intensity and edge detection. Then, NSS is utilized to extract the enhanced coefficient and this enhanced coefficient is applied to scale the input image. This scaling reflects contrast improvement and denoising effect on ultrasound images. The performance of proposed enhancement method is evaluated on clinical ultrasound images, using both subjective and objective image quality measures. In subjective evaluation, with proposed method, overall best score of 4.3 was obtained and that was 44% higher than the score of original images. These results were also supported by objective measures. The results demonstrated that the proposed method outperformed the other methods in terms of mean brightness preservation, edge preservation, structural similarity, and human perception-based image quality assessment. Thus, the proposed method can be used in computer-aided diagnosis systems and to visually assist radiologists in their interactive-decision-making task.

4159 Prostate cancer multiparametric MRI comparison study of 3T versus 7T in terms of lesion detection and image quality

OBJECTIVES/GOALS: The goal of this study was to perform a comparative, multi-reader, retrospective clinical evaluation of prostate multiparametric MRI (mpMRI) at 3 Tesla (3T) vs. 7 Tesla (7T) primarily in terms of prostate cancer localization. Subjective measures of image quality and artifacts were also evaluated. METHODS/STUDY POPULATION: Nineteen subjects were imaged at 3T and 7T between March 2016 and October 2018 under IRB-approved protocols. Four radiologists retrospectively and independently reviewed the data, and completed a two-part assessment for each dataset. First, readers assessed likelihood of cancer using Prostate Imaging Reporting &amp; Data System (PI-RADS) guidelines. Accuracy of cancer detection was compared to findings from prostate biopsy. The numbers of correctly or incorrectly classified sextants were summed across all four readers, then used to summarize detection performance. Second, readers assigned a score on a five-point Likert scale to multiple image quality characteristics for the 3T and 7T datasets. RESULTS/ANTICIPATED RESULTS: Sensitivity and specificity of 3T and 7T datasets for sextant-wise cancer detection were compared by paired two-tailed t-tests. Readers identified more sextants harboring cancer with the 3T datasets while false-positive rates were similar, resulting in significantly higher sensitivity at 3T with no significant differences in specificity. Likert scores for image quality characteristics for 3T and 7T datasets were compared by applying paired two-tailed t-tests to mean scores of the four radiologists for each dataset. Readers generally preferred the 3T datasets, in particular for staging and assessment of extraprostatic extension as well as overall quality of the contrast-enhanced data. DISCUSSION/SIGNIFICANCE OF IMPACT: Readers agreed 7T prostate mpMRI produced images with more anatomic detail, though with equivocal clinical relevance and more pronounced artifacts. Reader unfamiliarity with 7T images is a major extenuating factor. Forthcoming technological developments are anticipated to improve upon the results.

Objective estimation of subjective image quality assessment using multi‐parameter prediction

Objective evaluation of a subjective image quality assessment plays a significant role in the various image processing applications, such as compression, interpolation and noise reduction. The subjective image quality assessment does not only depend on some objective measurable artefacts, but also on image content and kind of distortions. Thus, a multi-parameter prediction of the objective image quality assessment is proposed in this study. The prediction parameters are found minimising the mean square error related to the known subjective image quality measure (DMOS). This approach includes mostly used image quality metrics (peak signal-to-noise ratio, multi-scale structural similarity image measure, feature similarity image measure, video quality measure) and two-dimensional image quality metrics (2D IQM). The proposed multi-parameter prediction has been verified on the test image database (LIVE) for compression, noise and blur distortions with available subjective image quality measures (DMOS). More reliable estimations are obtained using multi-parameter prediction instead of only one measure. The best results are reached when an image content indicator is combined with the 2D IQM measure separately for different kinds of distortions.

Heavily Calcified Coronary Arteries: Advanced Calcium Subtraction Improves Luminal Visualization and Diagnostic Confidence in Dual-Energy Coronary Computed Tomography Angiography.

The aim of this study was to evaluate a prototype dual-energy computed tomography calcium subtraction algorithm and its impact on luminal visualization in patients with heavily calcified coronary arteries. Twenty-nine patients (62% male; mean age, 64 ± 7 years) who had undergone dual-energy coronary computed tomography angiography were retrospectively included in this institutional review board-approved, Health Insurance Portability and Accountability Act-compliant study. Linearly blended (M_0.6) and calcium-subtracted images were reconstructed. Two independent observers assessed luminal visualization of the coronary arteries in a segment-based analysis, subjective image quality, and diagnostic confidence using 5-point Likert scales. Contrast-to-noise ratios for both data sets were calculated. Wilcoxon testing and Cohen's κ were used for statistical comparisons. Calcium-subtracted image series showed improved lumen visualization of the coronary arteries (P = 0.008), with excellent interreader agreement (mean score, 3.3; κ = 0.82), compared with M_0.6 series (mean score, 2.9; κ = 0.77). The calcium subtraction algorithm improved diagnostic confidence compared with the M_0.6 reconstructions (mean scores, 4.0 and 3.1, respectively; all P ≤ 0.002). The image quality analysis showed no significant differences between calcium-subtracted and M_0.6 data sets (subjectively: mean scores, 4.1 and 4.2, respectively, P = 0.442; objectively: mean contrast-to-noise ratio, 37.0 and 38.2, respectively, P = 0.733). A prototype algorithm for calcium subtraction improves coronary lumen visualization and diagnostic confidence in patients with heavy coronary calcifications without differences in conventional subjective and objective measures of image quality.

How much image noise can be added in cardiac x-ray imaging without loss in perceived image quality?

Cardiologists use x-ray image sequences of the moving heart acquired in real-time to diagnose and treat cardiac patients. The amount of radiation used is proportional to image quality; however, exposure to radiation is damaging to patients and personnel. The amount by which radiation dose can be reduced without compromising patient care was determined. For five patient image sequences, increments of computer-generated quantum noise (white + colored) were added to the images, frame by frame using pixel-to-pixel addition, to simulate corresponding increments of dose reduction. The noise adding software was calibrated for settings used in cardiac procedures, and validated using standard objective and subjective image quality measurements. The degraded images were viewed next to corresponding original (not degraded) images in a two-alternative-forced-choice staircase psychophysics experiment. Seven cardiologists and five radiographers selected their preferred image based on visualization of the coronary arteries. The point of subjective equality, i.e., level of degradation where the observer could not perceive a difference between the original and degraded images, was calculated; for all patients the median was 33%±15% dose reduction. This demonstrates that a 33%±15% increase in image noise is feasible without being perceived, indicating potential for 33%±15% dose reduction without compromising patient care.

Comparative Analysis of Image Enhancement Technique for Hyperspectral Palmprint Images

Enhancement is one of the most important and difficult techniques, also the first stage in the pre-processing of images which have to be subjected to image recognition algorithms. The goal of image enhancement is to improve the quality of images. Palmprint image quality is an important factor in the performance of hyperspectral palmprint recognition system. To the best of knowledge there is no evidence of work specifically directed towards image enhancement techniques on hyperspectral palm print images. In this paper different thirteen types of image enhancement techniques are compared based on image quality measure (subjective and objective), subjective image quality measure based on histogram and objective quality measure is based on mean square error (MSE), Peak signal to noise ratio (PSNR), Normalized cross correlation (NK), Average difference (AD), Structural content (SC), Maximum difference (MD), Normalized absolute error (NAE). Such a comparison would be useful in determining the best suited image enhancement method for hyperspectral palmprint. Median filter gives good performance as compare to other image enhancement techniques. The performance of different thirteen image enhancement techniques are tested on PolyU hyperspectral palmprint database. The comparative results are tabled.

A novel multi-focus image fusion algorithm based on random walks

In multi-focus image fusion, the aim is to create a single image where the whole scene is focused by fusing multiple images captured with different focus distances. The fused image has greater depth of field than each of the input images. In this paper, we present a new method for multi-focus image fusion via random walks on graphs. The proposed method first evaluates the focus areas in a local sense and identifies nodes corresponding to consistency of nodes in a global sense. Several popular feature sets based on focus measure and color consistency are evaluated and employed to create a fully connected graph to model the global and local characteristics, respectively, of the random walks. The behavior of random walks on the graph is utilized to compute the weighting factor for each of the shallow depth-of-field input image. Experimental results show that the proposed method outperforms many state-of-the-art techniques in both subjective and objective image quality measures.

Comparative assessment of three image reconstruction techniques for image quality and radiation dose in patients undergoing abdominopelvic multidetector CT examinations

To compare image quality and radiation dose of abdominal CT examinations reconstructed with three image reconstruction techniques. In this Institutional Review Board-approved study, contrast-enhanced (CE) abdominopelvic CT scans from 23 patients were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction (ASiR) and iterative reconstruction in image space (IRIS) and were reviewed by two blinded readers. Subjective (acceptability, sharpness, noise and artefacts) and objective (noise) measures of image quality were recorded for each image data set. Radiation doses in CT dose index (CTDI) dose-length product were also calculated for each examination type and compared. Imaging parameters were compared using the Wilcoxon signed rank test and a paired t-test. All 69 CECT examinations were of diagnostic quality and similar for overall acceptability (mean grade for ASiR, 3.9±0.3; p=0.2 for Readers 1 and 2; IRIS, 3.9±0.4, p=0.2; FBP, 3.8±0.9). Objective noise was considerably lower with both iterative techniques (p<0.0001 and 0.0016 for ASiR and IRIS). Recorded mean radiation dose, i.e. CTDI(vol), was 24% and 10% less with ASiR (11.4±3.4 mGy; p<0.001) and IRIS (13.5±3.7 mGy; p=0.06), respectively, than with FBP: 15.0±3.5 mGy. At the system parameters used in this study, abdominal CT scans reconstructed with ASiR and IRIS provide diagnostic images with reduced image noise and 10-24% lower radiation dose than FBP. CT images reconstructed with FBP are frequently noisy on lowering the radiation dose. Newer iterative reconstruction techniques have different approaches to produce images with less noise; ASiR and IRIS provide diagnostic abdominal CT images with reduced image noise and radiation dose compared with FBP. This has been documented in this study.

Abdominal CT with model-based iterative reconstruction (MBIR): initial results of a prospective trial comparing ultralow-dose with standard-dose imaging.

The purpose of this study was to report preliminary results of an ongoing prospective trial of ultralow-dose abdominal MDCT. Imaging with standard-dose contrast-enhanced (n = 21) and unenhanced (n = 24) clinical abdominal MDCT protocols was immediately followed by ultralow-dose imaging of a matched series of 45 consecutively registered adults (mean age, 57.9 years; mean body mass index, 28.5). The ultralow-dose images were reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and model-based iterative reconstruction (MBIR). Standard-dose series were reconstructed with FBP (reference standard). Image noise was measured at multiple predefined sites. Two blinded abdominal radiologists interpreted randomly presented ultralow-dose images for multilevel subjective image quality (5-point scale) and depiction of organ-based focal lesions. Mean dose reduction relative to the standard series was 74% (median, 78%; range, 57-88%; mean effective dose, 1.90 mSv). Mean multiorgan image noise for low-dose MBIR was 14.7 ± 2.6 HU, significantly lower than standard-dose FBP (28.9 ± 9.9 HU), low-dose FBP (59.2 ± 23.3 HU), and ASIR (45.6 ± 14.1 HU) (p < 0.001). The mean subjective image quality score for low-dose MBIR (3.0 ± 0.5) was significantly higher than for low-dose FBP (1.6 ± 0.7) and ASIR (1.8 ± 0.7) (p < 0.001). Readers identified 213 focal noncalcific lesions with standard-dose FBP. Pooled lesion detection was higher for low-dose MBIR (79.3% [169/213]) compared with low-dose FBP (66.2% [141/213]) and ASIR (62.0% [132/213]) (p < 0.05). MBIR shows great potential for substantially reducing radiation doses at routine abdominal CT. Both FBP and ASIR are limited in this regard owing to reduced image quality and diagnostic capability. Further investigation is needed to determine the optimal dose level for MBIR that maintains adequate diagnostic performance. In general, objective and subjective image quality measurements do not necessarily correlate with diagnostic performance at ultralow-dose CT.

Impact of iterative reconstruction on image quality and radiation dose in multidetector CT of large body size adults

To compare image quality and radiation dose using Adaptive Statistical Iterative Reconstruction (ASiR) and Filtered Back Projection (FBP) in patients weighing ≥ 91 kg. In this Institution Review Board-approved retrospective study, single-phase contrast-enhanced abdominopelvic CT examinations of 100 adults weighing ≥ 91 kg (mean body weight: 107.6 ± 17.4 kg range: 91-181.9 kg) with (1) ASiR and (2) FBP were reviewed by two readers in a blinded fashion for subjective measures of image quality (using a subjective standardized numerical scale and objective noise) and for radiation exposure. Imaging parameters and radiation dose results of the two techniques were compared within weight and BMI sub-categories. All examinations were found to be of adequate quality. Both subjective (mean = 1.4 ± 0.5 vs. 1.6 ± 0.6, P < 0.05) and objective noise (13.0 ± 3.2 vs.19.5 ± 5.7, P < 0.0001) were lower with ASiR. Average radiation dose reduction of 31.5 % was achieved using ASiR (mean CTDIvol. ASiR: 13.5 ± 7.3 mGy; FBP: 19.7 ± 9.0 mGy, P < 0.0001). Other measures of image quality were comparable between the two techniques. Trends for all parameters were similar in patients across weight and BMI sub-categories. In obese individuals, abdominal CT images reconstructed using ASiR provide diagnostic images with reduced image noise at lower radiation dose. • CT images in obese adults are noisy, even with high radiation dose. • Newer iterative reconstruction techniques have theoretical advantages in obese patients. • Adaptive statistical iterative reconstruction should reduce image noise and radiation dose. • This has been proven in abdominopelvic CT images of obese patients.

Referenceless image quality evaluation for whole slide imaging

Objective: The image quality in whole slide imaging (WSI) is one of the most important issues for the practical use of WSI scanners. In this paper, we proposed an image quality evaluation method for scanned slide images in which no reference image is required. Methods: While most of the conventional methods for no-reference evaluation only deal with one image degradation at a time, the proposed method is capable of assessing both blur and noise by using an evaluation index which is calculated using the sharpness and noise information of the images in a given training data set by linear regression analysis. The linear regression coefficients can be determined in two ways depending on the purpose of the evaluation. For objective quality evaluation, the coefficients are determined using a reference image with mean square error as the objective value in the analysis. On the other hand, for subjective quality evaluation, the subjective scores given by human observers are used as the objective values in the analysis. The predictive linear regression models for the objective and subjective image quality evaluations, which were constructed using training images, were then used on test data wherein the calculated objective values are construed as the evaluation indices. Results: The results of our experiments confirmed the effectiveness of the proposed image quality evaluation method in both objective and subjective image quality measurements. Finally, we demonstrated the application of the proposed evaluation method to the WSI image quality assessment and automatic rescanning in the WSI scanner.

Optimization of Contrast Material Delivery for Dual-Energy Computed Tomography Pulmonary Angiography in Patients With Suspected Pulmonary Embolism

To prospectively compare subjective and objective measures of image quality using 4 different contrast material injection protocols in dual-energy computed tomography pulmonary angiography (CTPA) studies of patients with suspected pulmonary embolism. A total of 100 consecutive patients referred for CTPA for the exclusion of pulmonary embolism were randomized into 1 of 4 contrast material injection protocols manipulating iodine concentration and iodine delivery rate (IDR, expressed as grams of iodine per second): Iomeprol 400 at 3 mL/s (IDR = 1.2 gI/s), iomeprol 400 at 4 mL/s (IDR = 1.6 gI/s), iomeprol 300 at 5.4 mL/s (IDR = 1.6 gI/s), or iomeprol 300 at 4 mL/s (IDR = 1.2 gI/s). Total iodine delivery was held constant. Dual-energy CTPA of the lungs were acquired and used to calculate virtual 120 kV CTPA images as well as iodine perfusion maps. Attenuation values in the thoracic vasculature and image quality of virtual 120 kV CTPAs were compared between groups. Iodine perfusion maps were also compared by identifying differences in the extent of beam-hardening artifacts and subjective image quality. Protocols with an IDR of 1.6 gI/s provided the best attenuation profiles. CTPA image quality was greatest in the high concentration, high IDR (1.6 gI/s) protocol (P < 0.05 for all group comparisons) with no differences between the other groups (all P ≥ 0.05). Extent of beam-hardening artifacts and perfusion map image quality was significantly better using the high concentration, high IDR protocol as compared with all groups (P < 0.05 for all comparisons) and significantly worse using the low concentration, low IDR protocol as compared with all groups (all P ≥ 0.05); no difference was found between the high concentration, low IDR protocol and the low concentration, high IDR protocol (P = 0.73 for comparison of beam-hardening artifacts; P = 0.50 for comparison of perfusion map image quality). High iodine concentration and high IDR contrast material delivery protocols provide the best image quality of both CTPA and perfusion map images of the lung through high attenuation in the pulmonary arteries and minimization of beam-hardening artifacts.

New joint demosaicing and zooming algorithm for color filter array

This paper presents a new joint demosaicing and zooming algorithm for digital cameras, each equipped with a single CCD/CMOS sensor and a color filter array (CFA). According to the proposed adaptive heterogeneity projection masks and Sobel- and luminance estimation-based masks, we can extract edge information of each pixel in terms of the direction of variation and the gradient from the mosaic image directly and accurately, and the extracted more accurate edge information will be utilized to assist the design of our proposed new joint demosaicing and zooming algorithm. Based on twenty-four popular testing mosaic images, our proposed new zooming algorithm has better image quality performance in terms of two objective color image quality measures, the color peak signal-to-noise ratio (CPSNR) and the S-CIELAB DeltaE* ab metric, and one subjective color image quality measure, the color artifacts, when compared with several previous zooming algorithms.

P‐190L: Late‐News Poster: Universal Evaluation of Stereoscopic 3‐D Displays

AbstractRecently, various types of stereoscopic three‐dimensional (3‐D) displays have been developed and commercialized. with this increased public awareness and interest in 3‐D displays, the need is rapidly emerging for a universal method to quantitatively compare the various technologies' ability to confer a sense of depth to viewers. In recent years, a number of methods for comparison have been suggested such as crosstalk, visual fatigue, and subjective measures of image quality; however, these methods fail to directly assess the various displays' ability to confer a sense of depth to the user solely on the basis of disparity information. This paper will outline a universal quantitative evaluation of stereoscopic 3‐D display technologies that enables one to quantitatively compare stereoscopic 3‐D displays in terms of their ability to confer a sense of depth to the viewer.

An improved normal-mesh-based image coder

The normal-mesh-based image coder of Jansen, Baraniuk, and Lavu (JBL) is studied. First, the JBL coder is introduced, and several of its shortcomings are identified. Then, to address these shortcomings, three modifications to this coder are proposed, namely, the use of a data-dependent base mesh, an integer representation for normal/vertical offsets, and a different scan-conversion scheme based on bicubic interpolation. Experimental results show that these proposed changes lead to improved coding performance in terms of both objective and subjective image-quality measures. In particular, the use of a data-dependent base mesh helps to locate horizons more quickly and preserve image edges better. The number of bits required to encode the normal/vertical offsets is reduced by representing this information with integers (as opposed to real numbers). Lastly, bicubic interpolation is found to yield higher-quality image reconstructions, while still maintaining sharp edges.