Backlight Unit Excessive Dimming Based on Perceptual Image Analysis

Objective To compare and analyze the difference of setup errors associated with radiotherapy for thorax and abdomen cancer with Cone beam CT (CBCT) between standard scan pattern and fast scan pattern, and to investigate the feasibility of online calibration in fast scan pattern for radiotherapy of thorax and abdomen cancer. Methods A total of 50 patients with thorax and abdomen cancer were selected and all the patients were designated either clockwise standard CBCT scan and anticlockwise fast CBCT scan before first volumetric modulated arc therapy (VMAT) either. The images of two scan patterns and the setup error of thorax and abdomen cancer were also compared. A physician, a physicists and a technician evaluated the quality of all images using the mean opinion score (MOS) method. Results The deviation between setup error from standard imaging pattern and setup error from fast imaging pattern of x, y, z axis translation and rotation were (0.2±0.2), (-0.6±0.4), (-0.2±0.3) mm and (0.2±0.1)°, (0.0±0.1)°, (0.1±0.1)°, which was not statistically significant (P>0.05). The setup error from standard imaging pattern and setup error from fast imaging pattern were highly correlated with statistical significance (r=0.92-0.98, P 0.05). Conclusions Fast imaging pattern has high correlation and consistency with the standard imaging model in the CBCT setup error correction, and the quality of images has no significant deterioration. The CBCT fast imaging pattern could be chosen to determine the setup error using online calibration for imaging thorax and abdomen cancer. Key words: Thorax and abdomen cancer; Image guide radiotherapy; Cone beam CT; Fast imaging model; Setup error

The size of pixels in a digital recording device, such as a CCD array, limits the spatial resolution in images obtained by an optical imaging system, thereby degrading the image quality. Digital super-resolution (DSR) techniques are used to reconstruct a high-resolution (HR) image from multiple sub-pixel-shifted low-resolution images in order to improve the image quality. In this article, we formulate a mathematical framework for DSR using the generalized sampling theorem (GST). The GST-based DSR method’s performance is evaluated by comparing it to existing resolution enhancement methods on the basis of evaluation metrics like percentage mean square error (%MSE), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). The GST DSR method exhibits an overall superior quality image reconstruction based on quantitative analysis (with a near zero %MSE, SSIM around one, and improved PSNR in dB) and qualitative comparison. The robustness of the GST DSR method is further demonstrated in the presence of frame-to-frame shift estimation error using %MSE and SSIM by comparing it with multi-frame interpolation approaches.

Image quality assessment based on the space similarity decomposition model

Indexes used for image quality evaluation are provided as computational models to measure the quality of images in a perceptually consistent manner. This paper presents a novel evaluation index for assessing image qualities. The index is a modification of the existing traditional Structural Similarity Index Measure (SSIM) by adding another factor to reflect the shape of the brightness histogram of the assessed image. The proposed index therefore is a combination of four major factors luminance, contrast, structure and shape of histogram. This index is mathematically simple and applicable in various image processing. For demonstration a new image de-noising approach using an adaptive shrinkage threshold in the shearlet domain is used. Experimental results show that the new image quality indexes give better prediction accuracy, better prediction monotonicity than PSNR, HQI, UIQI and SSIM.

This paper proposes a new video-based face recognition method that uses video frames of a subject rotating his/her head. In the experiment discussed here, the manifolds of video frames embedded in a high-dimensional video space were extracted using neural network (NN)-based models. This increased the recognition rate in comparison with a simple NN architecture (from 72.9 to 81.3 %). These models were inspired by manifold interpretations of the brain's visual perception. Next, the pose and person manifolds were separated using the neurons trained in the hidden or bottleneck layer of the network. Finally, the separated manifolds were used to synthesize face images at different angles from a single frontal image. Using video frames to extract these manifolds produces higher-quality images than using manifolds extracted from single images. This improvement in image quality was verified using the structural similarity index measure.

The demand on the electron beam (e-beam) for the inspection of semiconductor devices or display panel is rapidly increasing since e-beam cannot only monitor the small structures but also has the potential of detecting electrical troubles or repairing the defects. However, the merit of e-beam is limited because of the high cost, low throughput, and the possible damage due to the high e-beam energy. A microcolumn is a strong candidate to solve these limitations as its size is extremely miniaturized (both column diameter and height can be reduced down to a few millimeters) and the output e-beam energy is as low as 100–1000 eV. In this work, the authors tried to test the inspection of defects by applying a low voltage microcolumn to liquid crystal display panel. In order to demonstrate the authors’ inspection method, they extracted a 7’’ thin film transistor-liquid crystal display (TFT-LCD) panel from the production line just after completing the pixel structures and used this panel as a test sample. On the selected panel, the authors intentionally made some defects such as open data or gate lines by cutting some points using a laser beam. They operated their microcolumn with a beam energy of 300 eV and obtained the scanning images of the panel while operating the panel with specific operation conditions. The operation parameters for the test TFT-LCD panel such as the voltages applied to the gate lines, data lines, and storage capacitors was fixed at two specific sets of values. Then, the image obtained by the secondary electron reflects the information on the electrical state of the pixels as well as the geometrical ones. By combining the two sets of data, the authors could explain the correlation between the irregular behavior in the image contrast and the open defects and the detailed results will be discussed.

Introduction[18F]fluorodeoxyglucose ([18F]FDG) brain PET is used clinically to detect small areas of decreased uptake associated with epileptogenic lesions, e.g., Focal Cortical Dysplasias (FCD) but its performance is limited due to spatial resolution and low contrast. We aimed to develop a deep learning-based PET image enhancement method using simulated PET to improve lesion visualization.MethodsWe created 210 numerical brain phantoms (MRI segmented into 9 regions) and assigned 10 different plausible activity values (e.g., GM/WM ratios) resulting in 2100 ground truth high quality (GT-HQ) PET phantoms. With a validated Monte-Carlo PET simulator, we then created 2100 simulated standard quality (S-SQ) [18F]FDG scans. We trained a ResNet on 80% of this dataset (10% used for validation) to learn the mapping between S-SQ and GT-HQ PET, outputting a predicted HQ (P-HQ) PET. For the remaining 10%, we assessed Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Root Mean Squared Error (RMSE) against GT-HQ PET. For GM and WM, we computed recovery coefficients (RC) and coefficient of variation (COV). We also created lesioned GT-HQ phantoms, S-SQ PET and P-HQ PET with simulated small hypometabolic lesions characteristic of FCDs. We evaluated lesion detectability on S-SQ and P-HQ PET both visually and measuring the Relative Lesion Activity (RLA, measured activity in the reduced-activity ROI over the standard-activity ROI). Lastly, we applied our previously trained ResNet on 10 clinical epilepsy PETs to predict the corresponding HQ-PET and assessed image quality and confidence metrics.ResultsCompared to S-SQ PET, P-HQ PET improved PNSR, SSIM and RMSE; significatively improved GM RCs (from 0.29 ± 0.03 to 0.79 ± 0.04) and WM RCs (from 0.49 ± 0.03 to 1 ± 0.05); mean COVs were not statistically different. Visual lesion detection improved from 38 to 75%, with average RLA decreasing from 0.83 ± 0.08 to 0.67 ± 0.14. Visual quality of P-HQ clinical PET improved as well as reader confidence.ConclusionP-HQ PET showed improved image quality compared to S-SQ PET across several objective quantitative metrics and increased detectability of simulated lesions. In addition, the model generalized to clinical data. Further evaluation is required to study generalization of our method and to assess clinical performance in larger cohorts.

Speckle noise in ultrasound images compromises image quality and hinders diagnostic accuracy. Traditional ultrasound denoising methods often struggle to preserve anatomical details while effectively reducing noise, especially under high-noise conditions. In this study, we propose an innovative approach that integrates a lightweight channel attention mechanism (LCAM) within a convolutional variational autoencoder (CVAE) framework to enhance ultrasound image denoising. The proposed approach efficiently reduces speckle noise while maintaining essential anatomical features. Comprehensive evaluations across six diverse ultrasound datasets demonstrate that the LCAM-CVAE outperforms conventional denoising techniques in both subjective image quality and objective performance metrics, including peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), standard deviation in PSNR (SD-PSNR), standard deviation in SSIM (SD-SSIM), PSNR statistical relevance tests, and computational efficiency (CE). The LCAM-CVAE approach demonstrates exceptional performance, particularly under high-noise conditions, ensuring the preservation of key anatomical structures for accurate diagnosis. These results highlight the LCAM-CVAE approach as a robust and promising solution for ultrasound image denoising, with significant clinical potential to improve diagnostic quality in noisy environments.

Quiescent frame, contrast-enhanced coronary magnetic resonance angiography reconstructed using limited number of physiologic frames from 5D free-running acquisitions

Steganography as a practice is highly useful as it allows information to be concealed within digital images while maintaining visual quality. However, it can be a challenge to balance imperceptibility, capacity, and robustness while ensuring that embedded data remains secure against statistical and Deep Learning (DL)-based steganalysis. Therefore, we propose a new framework that utilizes the BOSSbase dataset for cover images, the USC-SIPI dataset for secreted images, and introduces adaptive image steganography, which combines fused map-based embedding with evolutionary optimization to solve existing problems. The system starts by encrypting the secret image through Blowfish cipher encryption to protect its confidentiality. The model generates a fused map through a combination of entropy and Laplacian-based noise maps from the cover image to determine the most suitable embedding areas. Next, we optimize the process of embedding location selection and ordering using Particle Swarm Optimization (PSO) to maximize both security and image quality. Next, we use priority-guided Least Significant Bits (LSB) substitution in the embedding process to generate an optimized priority map for inserting the encrypted bitstream into the image. The system can function on different cover image sizes, so at [Formula: see text] the resolution is 51-61 dB Peak Signal-to-Noise Ratio (PSNR) and 0.9972-1.0 Structural Similarity Index Measure (SSIM) for stego images at 0.1-1.0 Bits Per Pixel (BPP) with 262,144 bits while maintaining perfect secret reconstruction (SSIM = 1.000) and processing times under 0.25 seconds for embedding and extraction. Security tests demonstrate that the proposed system remains undetectable since the Area Under Curve (AUC) for [Formula: see text] reaches 0.49-0.57 across all payload levels and Regular Singular (RS) statistics remain at 0.59-0.66 between 0.1-1.0 BPP. The proposed model provides effective capacity while maintaining high visual quality and security against DL-based steganalysis, such as that using Convolutional Neural Networks (CNN).

Robust structural similarity index measure for images with non-Gaussian distortions

Aim and Objective:The objective of this study was to optimize the threshold for discrete cosine transform (DCT) coefficients for near-lossless compression of Tc-99 m Dimercaptosuccinic acid (DMSA) scan images using discrete cosine transformation.Materials and Methods:Two nuclear medicine (NM) Physicians after reviewing several Tc-99 m DMSA scan images provided 242 Tc-99 m DMSA scan images that had scar. These Digital imaging and communication in medicine (DICOM) images were converted in the Portable Network Graphics (PNG) format. DCT was applied on these PNG images, which resulted in DCT coefficients corresponding to each pixel of the image. Four different thresholds equal to 5, 10, 15, and 20 were applied and then inverse discrete cosine transformation was applied to get the compressed Tc-99 m DMSA scan images. Compression factor was calculated as the ratio of the number of nonzero elements after thresholding DCT coefficients to the number of nonzero elements before thresholding DCT coefficients. Two NM physicians who had provided the input images visually compared the compressed images with its input image, and categorized the compressed images as either acceptable or unacceptable. The quality of compressed images was also assessed objectively using the following eight image quality metrics: perception-based image quality evaluator, structural similarity index measure (SSIM), multiSSIM, feature similarity indexing method, blur, global contrast factor, contrast per pixel, and brightness. Pairwise Wilcoxon signed-rank sum tests were applied to find the statistically significant difference between the value of image quality metrics of the compressed images obtained at different thresholds and the value of the image quality metrics of its input images at the level of significance = 0.05.Results:At threshold 5, (1) all compressed images (242 out of 242 Tc-99 m DMSA scan images) were acceptable to both the NM Physicians, (2) Compressed image looks identical to its original image and no loss of clinical details was noticed in compressed images, (3) Up to 96.65% compression (average compression: 82.92%) was observed, and (4) Result of objective assessment supported the visual assessment. The quality of compressed images at thresholds 10, 15, and 20 was significantly better than that of input images at P < 0.0001. However, the number of unacceptable compressed images at thresholds 10, 15, and 20 was 6, 38, and 70, respectively.Conclusions:Up to 96.65%, near-losses compression of Tc-99 m DMSA images was found using DCT by thresholding DCT coefficients at a threshold value equal to 5.

We use images in several important areas such as military, health, security, and science. Images can be distorted during the capturing, recording, processing, and storing. Image quality metrics are the techniques to measure the quality and quality accuracy level of the images and videos. Most of the quality measurement algorithms does not affect by small distortions in the image. Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasonic Imaging (UI) are widely used in the health sector. Because of several reasons it might be artifacts in the medical images. Doctor decisions might be affected by these image artifacts. Image quality measurement is an important and challenging area to work on. There are several metrics that have been done in the literature such as mean square error, peak signal-noise ratio, gradient similarity measure, structural similarity index, and universal image quality. Patient information can be an embedded corner of the medical image as a watermark. Watermark can be considered one of the image distortions types. The most common objective evaluation algorithms are simple pixel based which are very unreliable, resulting in poor correlation with the human visual system. In this work, we proposed a new image quality metric which is a Measure of Singular Value Decomposition (M-SVD). Experimental results show that novel M-SVD algorithm gives very promising results against Peak Signal to Noise Ratio (PSNR), the Mean Square Error (MSE), Structural Similarity Index Measures (SSIM), and 3.4. Universal Image Quality (UIQ) assessments in watermarked and distorted images such as histogram equalization, JPEG compression, Gamma Correction, Gaussian Noise, Image Denoising, and Contrast Change.

This study quantitatively compared image quality and radiation dose between fixed scanning conditions and automatic exposure control (AEC)-applied conditions in ankle CT. The experiments were conducted using a Siemens SOMATOM X.ceed CT scanner and an RSD ankle phantom. The study was conducted with a total of four groups (A-D). Group A applied CARE kV and CARE Dose4D. Groups B and C applied only CARE Dose4D, with varying tube voltages (kVp). Group D was set to the fixed scanning condition. Dose was evaluated using CT dose index volume (CTDIvol), dose length product (DLP), and size-specific dose estimate (SSDE). Image quality was compared by setting region of interset (ROI) using Image J and FIJI programs to measure signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). SNR and CNR were highest in Group A and lowest in Group D. The results showed that SNR and CNR were highest in Group A and lowest in Group D,(p&lt;0.001) and PSNR exhibited a similar trend. SSIM values were greater than 0.999 in all groups, indicating high structural similarity. Radiation dose was lowest in Group D, followed by Group A. The results showed that the AEC-applied condition in ankle CT maintained a radiation dose comparable to that of the fixed scanning condition while demonstrating relatively favorable quantitative image quality metrics. However, as this study was limited to a phantom-based experiment and quantitative image quality evaluation, further patient-based studies are required to confirm its clinical applicability. These findings suggest that AEC may serve as an alternative scanning strategy for balancing radiation dose and image quality in ankle CT.

Deep Learning (DL) techniques have enabled up to 6-fold acceleration in musculoskeletal magnetic resonance imaging (MRI) while preserving diagnostic image quality. Further, improvements in acceleration and generalization require novel approaches. We propose a DL framework that integrates Simultaneous Multislice (SMS) imaging with Parallel Imaging (PI) to enhance current DL-based reconstruction. To advance musculoskeletal Magnetic Resonance Imaging (MRI), by developing a DL reconstruction framework that combines SMS and PI, enabling acceleration of up to 8-fold and beyond, while maintaining image quality suitable for clinicalinterpretation. End-to-End (E2E) DL framework for reconstructing Turbo Spin Echo (TSE) MRI data acquired with SMS and PI acceleration. The method unrolls a proximal gradient algorithm with Nesterov momentum and integrates a novel DL network for joint regularization across simultaneously acquired slices. Slice separation and k-space-to-image reconstruction are unified by embedding the full SMS forward model into the DL architecture. Data Consistency (DC) is modulated to enhance denoising, and a super-resolution module improves image sharpness. The robust DL model was trained on over 200000 slices from 1.5T to 3T scans with diverse acquisitionsettings. The proposed E2E DL model outperforms prior methods at 8-fold and 12-fold acceleration, as measured by PSNR (peak signal-to-noise ratio) and SSIM (structural similarity index measure) metrics. Evaluation on prospectively acquired clinical scans by two radiologists confirms, that image quality and abnormality detection are comparable to standard acquisitions at loweracceleration. We extend state-of-the-art DL reconstruction frameworks by integrating slice separation directly into the model for SMS acquisitions. Our E2E DL approach achieves clinical-grade image quality at 8-fold acceleration across 20 subjects, reducing acquisition time by 27%. Preliminary results suggest potential for further acceleration up to 12-fold, demonstrating significant advancement beyond existing DLtechniques.