Restoration of Tc-99m methyl diphosphonate bone scan image using Richardson-Lucy algorithm.

Edge detection is a fundamental and important tool in image processing and computer vision. The research topic discusses edge extraction of a digital image using various digital image processing techniques. Digital image edge identification using variety of digital image processing methods. The most popular technique for identifying discontinuities in intensity levels is edge detection. The actual image contains noise that could affect the digital image's quality. Some edge detection filters were used such as Canny, Sobel, and Prewitt. Laplace of Gaussian (LOG) edge recognition, Robert boundary detection, zero edge recognition and analysis. In light of the results of the comparison of edge filters the best edge filter had been obtained. Also, a hybrid method that merges between Canny filter and morphology operations were produced for edge detection. The research aims to reach an appropriate image purification by using different types of filters and to reduce distortions at the edges of the image and to determine which type of filter is the best. As well as, a comparison operation is applied between the traditional method and the hybrid method. Following the results explained by the filters used and displaying the resulting images. The results evaluated from hybrid filter show that this method is optimal than the traditional filters based on the Blind/Reference less Image Spatial Quality Evaluator (BRISQUE) with a value equals 43.43. A smaller score indicates better perceptual quality. Also, the edged images quality consequences from the traditional filters were measured by this metric. The results produced from the traditional filters prove that the best filter for edge image detection is Canny filter based on Blind/Reference less Image Spatial Quality Evaluator (BRISQUE) that reaches 46.01. In addition, the correlation coefficient was estimated to find which of the resulting images are better and closer to the original image. While for correlation coefficient the better filter was Prewitt filter with 0.3133 value. This study program was applied under the MATLAB R2020b system.

Image Quality Assessment (IQA) is essential for the accuracy of systems for automatic recognition of tree species for wood samples. In this study, a No-Reference IQA (NR-IQA), wood NR-IQA (WNR-IQA) metric was proposed to assess the quality of wood images. Support Vector Regression (SVR) was trained using Generalized Gaussian Distribution (GGD) and Asymmetric Generalized Gaussian Distribution (AGGD) features, which were measured for wood images. Meanwhile, the Mean Opinion Score (MOS) was obtained from the subjective evaluation. This was followed by a comparison between the proposed IQA metric, WNR-IQA, and three established NR-IQA metrics, namely Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), deepIQA, Deep Bilinear Convolutional Neural Networks (DB-CNN), and five Full Reference-IQA (FR-IQA) metrics known as MSSIM, SSIM, FSIM, IWSSIM, and GMSD. The proposed WNR-IQA metric, BRISQUE, deepIQA, DB-CNN, and FR-IQAs were then compared with MOS values to evaluate the performance of the automatic IQA metrics. As a result, the WNR-IQA metric exhibited a higher performance compared to BRISQUE, deepIQA, DB-CNN, and FR-IQA metrics. Highest quality images may not be routinely available due to logistic factors, such as dust, poor illumination, and hot environment present in the timber industry. Moreover, motion blur could occur due to the relative motion between the camera and the wood slice. Therefore, the advantage of WNR-IQA could be seen from its independency from a “perfect” reference image for the image quality evaluation.

Fully automated region of interest segmentation pipeline for UAV based RGB images

Objective. This cross-sectional study compared the color accuracy and image quality of intraoral photographs taken with DSLR cameras, smartphones, and smartphones with auxiliary lighting. Methods. Forty participants had five images captured per device, yielding 600 images. Image quality was evaluated using Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) via No-Reference Matrix (NRM), while experts provided qualitative evaluations. Results. Smartphones achieved the highest average NRM score (53.14 ± 28.2), followed by smartphones with auxiliary lighting (47.62 ± 1.9) and DSLR cameras (45.32 ± 2.2), with no significant difference between DSLR cameras and smartphones with auxiliary lighting (p = 0.34). Color accuracy (ΔE) was closest between DSLR cameras and smartphones with auxiliary lighting (4.89 ± 2.47), while other pairs showed higher differences. These two also showed comparable color consistency (p = 0.63 and 0.57 for a; b values), although smartphones produced brighter images (L = 50.56 ± 4.82 vs. 31.84 ± 4.82). Experts preferred DSLR images for caries diagnosis and presentation (96.7% preference), but found smartphone images with auxiliary lighting clinically acceptable. Conclusion. While DSLR cameras delivered superior image quality, smartphones with auxiliary lighting demonstrated comparable diagnostic performance as a practical, low-cost alternative in resource-limited settings. Further validation with newer devices is recommended.

One of the most popular waveform based coding scheme for images is Transform coding. It is also known that the wavelet coefficients of no reference images possess certain regularities by the image distortions. The NRDPF-IQA (No Reference Distortion Patch Features-Image Quality Assessment) is one of the No-Reference Image Quality algorithms, which is the proposed algorithm in this paper. The proposed algorithm estimates quality based on features of the image. However the previous BRISQUE (Blind/Referenceless Image Spatial Quality Evaluator) algorithm operates based on low computational complexity and C-DIIVINE (complex extension of the DIIVINE) algorithm based on complex Gaussian scale mixture model. In this paper, we present NRDPF-IQA algorithm, which blindly assesses image quality based on advanced generalized Gaussian distortion model. All these distortions have characteristic features that are consistent across No-Reference images. We also compare the results with all existing algorithms. The experimental results show that this NRDPF-IQA to yield an improvement to perform the better results compare with other recent No-Reference algorithms.

To discuss the analysis and evaluation of highway landslides, the application of data mining methods combined with deep learning frameworks in geologic hazard evaluation and monitoring is explored preliminarily. On the premise of optimizing the processing of landslide images, first, the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) based on the natural statistical characteristics of the spatial domain is introduced, which is initially combined with Super‐Resolution Convolutional Neural Network (SRCNN). Then, the AlexNet is fine‐tuned and applied to highway landslide monitoring and surveying. Finally, an entropy weight gray clustering evaluation method based on data mining analysis is proposed, and the performances of several methods are verified. The results show that the average score of the BRISQUE algorithm in Image Quality Assessment (IQA) is above 0.9, and the average running time is 0.1523 s. The combination of BRISQUE and SRCNN can improve the image quality significantly. After fine‐tuning, the recognition accuracy of AlexNet for landslide images can reach about 80%. The evaluation method based on gray clustering can effectively determine the correlation between soil moisture content and slope angle and thereby be applied to the analysis and evaluation of highway landslides. The results are beneficial to the judgment and assessment of highway landslide conditions, which can be extended to research on other geologic hazards.

The digital cadmium-zinc-telluride (CZT)-based SPECT system offers good energy resolution. However, the impacts of dual-energy window scatter correction on image quality under narrower primary energy windows remain to be determined. This study aimed to assess the impacts of SC (with scatter correction) and NOSC (without scatter correction) on image quality under different primary energy windows. We used a standard NEMA/IEC 2007 phantom containing six hollow spheres of various sizes to simulate the human body at four target-to-background ratios (T/B ratio, the ratio of the average radioactive activity concentration in the target region to that in the background region, 32:1, 16:1, 8:1, and 4:1, respectively), by filling with 99mTcO4-. For clinical validation, we recruited 20 patients and analyzed their whole-body bone scan images. Phantom and patient images were acquired with the Discovery NM/CT 670 CZT with list mode. Image quality of phantom was measured by calculating indicators from the NEMA NU 2-2018 standard, including percent background variability and percent contrast. Image quality of patients was measured by calculating coefficient of variation (COV), contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR). Visual quality was evaluated by two experts. Higher percent background variability and COV values were present when SC was applied regardless of which energy window, acquisition time, or T/B ratio used, and statistically significant differences were observed between all SC and NOSC groups for both phantoms and patients. For phantoms, percent contrast values of most SC groups were higher than those of NOSC groups. For patients, CNRs and SNRs of SC groups were higher than those of NOSC groups (all P < 0.05). Visually, SC had higher visual scores than NOSC for both phantoms and patients. SC significantly improves image contrast while minimizing concomitant image noise to a great extent and provides good visual image quality across different primary energy windows overall.

Feasibility study on carbonated water for Doppler imaging in medical ultrasonic applications

Face detection in low-light conditions remains challenging due to underexposure, noise, and unstable contrast, which significantly degrade the performance of convolutional-based models. Conventional enhancement techniques, such as Histogram Equalization, Contrast-Limited Adaptive Histogram Equalization (CLAHE), and Low-Light Image Enhancement (LIME), often improve brightness but introduce visual artifacts and lack robustness for face detection. This study proposes Low-light Unified Multi-stage Enhancement Network (LUMEN), a lightweight multi-stage image enhancement pipeline designed as a preprocessing module to improve RetinaFace-based face detection under low-light conditions. LUMEN integrates Multiscale Retinex with Color Restoration, adaptive gamma correction, CLAHE-based local contrast enhancement, controlled image fusion, and Non-Local Means denoising to jointly stabilize illumination, preserve texture, and maintain visual naturalness. The method is evaluated on a low-light subset of the Human Faces Object Detection Dataset using RetinaFace. Detection performance is assessed using detection rate and confidence score, while visual quality is evaluated using no-reference metrics such as the Natural Image Quality Evaluator (NIQE) and the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Experimental results show that LUMEN achieves a face detection rate of 91% with a high confidence score (0.9545), rep-resenting an improvement of 33 percentage points over raw low-light images (58%), while maintaining detection performance comparable to CLAHE, which ranks as the second-best method, LUMEN delivers superior perceptual quality, evidenced by the lowest BRISQUE score, as well as more stable visual appearance with reduced noise amplification and fewer contrast artifacts in low-light facial regions. LUMEN also achieves the lowest BRISQUE score, indicating superior texture preservation and perceptual stability. Ablation studies confirm that Retinex and CLAHE are the most critical components for detection robustness, while gamma correction, fusion, and denoising mainly contribute to visual naturalness. These results demonstrate that LUMEN provides an effective and practical preprocessing solution for low-light face detection.

The main problem in nuclear medicine imaging system is the resulting images degraded with large amounts of noise. In this work we want use four different enhancement methods to enhance whole-body bone scan image so as to reduce the noise from the image and improve the resolution to achieve a better image quality and maintain quality for accurate diagnosis. Different enhancement methods and two pair bone scan images used to perform this work. The enhancement algorithms are successfully demonstrated on real bone scan images.

Breakthroughs in the convolution neural network(CNN) have resolved and improved many challenges of pattern recognition in natural images. With the increased use of proximal sensing and low-cost cameras, monitoring and automation systems have gained popularity in the agriculture fields. Detection, segmentation, clustering, and counting are some fundamental problems associated with it. Here we are working on the detection of wine grapes, a crop with a variety of shapes, colors, sizes, and structures. Object detection is a challenging task especially when we are working on natural images. It is an even more difficult task when we are working on blurred images. Blur arises when images are taken via handheld camera, moving object in the automation system, or low rate video frame.Here we are trying to solve the motion blur problem in grape detection using three existing image deblurring algorithms. Performance of the deblurring algorithm is generally measured by peak-signal to noise ratio(PSNR) and structure similarity index(SSIM), but in addition to it, we have also considered blind/referenceless image spatial quality evaluator(BRISQUE). In this paper, we have comparatively analyzed: Scale recurrent network(SRN) for deep image deblurring, Multiscale convolution neural network for dynamic scale deblurring(Deep deblur), and DeblurGANv2: Deblurring(orders of magnitude) faster and better. Grape detection has experimented with yolov5x. Raw images from the standard dataset(GoPro and WGSID) were corrupted with various kinds of motion blurs. From the obtained result we can conclude that image deblurring significantly improves the performance of grape detection on the corrupted motion blur dataset.

Today's, the image processing applications are obvious in the various sciences. The medical image processing has achieved to acceptable results in the field of diagnosing disease. One of these applications which is discussed in this paper is diagnosis injury in whole body bone scan. Whole body bone scan has some applications in the diagnosis of benign lesions such as cardiovascular disease and metabolic bone and sports injuries and also to investigate the pervasiveness of bone. One of the main criteria to diagnose complications in the body is area with high brightness ‎to the brightness of adjacent areas in the image or asymmetry of the brightness in the two-sided areas of the body. In this study, a software has been implemented about the detection of disease-suspected areas that strategies to increase the pixel intensity have been considered to make easier the image segmentation and innovative methods of researcher have been implemented in image preparation to convert to binary mode and find suspected areas. In addition, the software automatically creates reports, this feature will be a big contribution to the doctor. Software assessment has been done on 11 whole body bone scan images and provided the results to the physician, the software succeeded to find the 41 correct areas from 44 injured areas, 93.18% of cases have been successfully identified. In terms of the number of errors in the areas of diagnosis, 30 cases were observed in 11 images which shows an average of 2.72 errors on each image.

Quantitative studies of bone using (99m)Tc-methylene diphosphonate ((99m)Tc-MDP) have a potentially valuable role in investigating the treatment of patients with metabolic bone disease. In this study we compared 3 different methods of measuring whole-skeleton (99m)Tc-MDP plasma clearance (K(bone)) in 12 osteoporotic postmenopausal women (mean age, 67.3 y) before participation in a clinical trial of an osteoporosis therapy. The aim was to compare the consistency and accuracy of the 3 methods before their use in evaluating the subjects' response to treatment. Subjects were injected with 600 MBq (99m)Tc-MDP and 3 MBq (51)Cr-ethylenediaminetetraacetic acid ((51)Cr-EDTA) and whole-body bone scan images were acquired at 10 min, 1, 2, 3, and 4 h. Two-minute static images of the thighs were acquired immediately after the 1- to 4-h whole-body scans. Six blood samples were taken between 5 min and 4 h, and free (99m)Tc-MDP was measured using ultrafiltration. The glomerular filtration rate (GFR) was estimated from the (51)Cr-EDTA plasma curve. The methods used to evaluate K(bone) were (a) the area-under-the-curve (AUC) method, in which the GFR measurement was subtracted from the total (bone plus renal) clearance (K(total)) measured from the free (99m)Tc-MDP plasma curve; (b) the modified Brenner method, in which (99m)Tc-MDP renal clearance estimated from the whole-body counts was subtracted from the total clearance measured from the rate of elimination of tracer from soft tissue; and (c) the Patlak plot method, which was also used to derive regional values of K(bone) for the skull, spine, pelvis, arms, and legs. There was good agreement between the 3 methods of measuring K(bone). (mean K(bone) +/- SD: AUC method, 30.3 +/- 6.4 mL x min(-1); Brenner method, 31.1 +/- 5.8 mL x min(-1); Patlak method, 35.7 +/- 5.8 mL x min(-1)). The correlation coefficients between the methods varied from r = 0.767 (P = 0.004) to r = 0.805 (P = 0.002). Regional measurements of (99m)Tc-MDP clearance gave the following percentages of the whole-skeleton clearance: skull, 13.3%; spine, 16.6%; pelvis, 17.2%; arms, 11.1%; legs, 23.7%. The 3 methods gave consistent and accurate measurements of K(bone). The Patlak method can be used to study regional as well as total-skeleton values of K(bone).

Image quality metrics have become more and more popular in the image processing community. However, so far, no one has been able to define an image quality metric well correlated with the percept for overall image quality. One of the causes is that image quality is multi-dimensional and complex. One approach to bridge the gap between perceived and calculated image quality is to reduce the complexity of image quality, by breaking the overall quality into a set of quality attributes. In our research we have presented a set of quality attributes built on existing attributes from the literature. The six proposed quality attributes are: sharpness, color, lightness, artifacts, contrast, and physical. This set keeps the dimensionality to a minimum. An experiment validated the quality attributes as suitable for image quality evaluation. The process of applying image quality metrics to printed images is not straightforward, because image quality metrics require a digital input. A framework has been developed for this process, which includes scanning the print to get a digital copy, image registration, and the application of image quality metrics. With quality attributes for the evaluation of image quality and a framework for applying image quality metrics, a selection of suitable image quality metrics for the different quality attributes has been carried out. Each of the quality attributes has been investigated, and an experimental analysis carried out to find the most suitable image quality metrics for the given quality attributes. For the sharpness attributes the Structural SIMilarity index (SSIM) by Wang et al. (2004) is the the most suitable, and for the other attributes further evaluation is required.

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