Standard Grayscale Images Research Articles

Comparative Analysis of Color Space and Channel, Detector, and Descriptor for Feature-Based Image Registration.

The current study aimed to quantify the value of color spaces and channels as a potential superior replacement for standard grayscale images, as well as the relative performance of open-source detectors and descriptors for general feature-based image registration purposes, based on a large benchmark dataset. The public dataset UDIS-D, with 1106 diverse image pairs, was selected. In total, 21 color spaces or channels including RGB, XYZ, Y'CrCb, HLS, L*a*b* and their corresponding channels in addition to grayscale, nine feature detectors including AKAZE, BRISK, CSE, FAST, HL, KAZE, ORB, SIFT, and TBMR, and 11 feature descriptors including AKAZE, BB, BRIEF, BRISK, DAISY, FREAK, KAZE, LATCH, ORB, SIFT, and VGG were evaluated according to reprojection error (RE), root mean square error (RMSE), structural similarity index measure (SSIM), registration failure rate, and feature number, based on 1,950,984 image registrations. No meaningful benefits from color space or channel were observed, although XYZ, RGB color space and L* color channel were able to outperform grayscale by a very minor margin. Per the dataset, the best-performing color space or channel, detector, and descriptor were XYZ/RGB, SIFT/FAST, and AKAZE. The most robust color space or channel, detector, and descriptor were L*a*b*, TBMR, and VGG. The color channel, detector, and descriptor with the most initial detector features and final homography features were Z/L*, FAST, and KAZE. In terms of the best overall unfailing combinations, XYZ/RGB+SIFT/FAST+VGG/SIFT seemed to provide the highest image registration quality, while Z+FAST+VGG provided the most image features.

Dual-Core Adaptive NLM Image Denoising Algorithm Based on Variable-Size Window and Neighborhood Multifeatures

To solve the problem that the similarity calculation between neighbors was easily disturbed by noise in the traditional nonlocal mean (NLM) denoising algorithm, a dual-core NLM denoising algorithm based on neighborhood multifeatures and variable-size search window was proposed. The algorithm first proposed to use the eigenvalues of the structure tensor to classify the region where the target pixel points were located and used different sizes of the search window to search for similar neighborhoods for target pixel points in different categories of the region, thus effectively avoiding the problem of oversmoothing or inadequate denoising of the image caused by the use of the global size. Then, the gradient features between image blocks were defined and combined with grayscale features and spatial features to measure the similarity of neighborhood blocks, which solved the problem of noise interfering with the search of similar blocks. Then, an adaptive algorithm with Gaussian–Sinusoidal dual kernel function and quantitative estimation of the optimal values of the filtering parameters was designed to calculate the neighborhood similarity weights to improve the accuracy of image denoising. Finally, the similarity weights were used to weight and average the search neighborhood of the target pixel points to achieve the denoising of the target pixel points. To test the effectiveness of the algorithm, denoising tests were performed using multiple standard grayscale images with different levels of Gaussian white noise added and compared with several advanced denoising algorithms. The experimental results showed that the algorithm was effective. The algorithm improved the image peak signal-to-noise ratio by more than 56.54% on average when Gaussian white noise was removed, and the structural similarity reached more than 0.701 on average. Compared with the traditional NLM algorithm and other improved algorithms, the algorithm proposed in this paper had strong denoising ability, better protection of edges and texture details, and the quality of the image was greatly improved, which had a good application prospect.

Diagnostic value of DECT-based colored collagen maps for the assessment of cruciate ligaments in patients with acute trauma

ObjectivesThe purpose of this study was to evaluate the diagnostic accuracy of third-generation dual-source dual-energy CT (DECT) color-coded collagen reconstructions for the assessment of the cruciate ligaments compared to standard grayscale image reconstruction.MethodsPatients who underwent third-generation dual-source DECT followed by either 3-T MRI or arthroscopy of the knee joint within 14 days between January 2016 and December 2021 were included in this retrospective study. Five radiologists independently evaluated conventional grayscale DECT for the presence of injury to the cruciate ligaments; after 4 weeks, readers re-evaluated the examinations using grayscale images and color-coded collagen reconstructions. A reference standard for MRI was provided by a consensus reading of two experienced readers and arthroscopy. Sensitivity and specificity were the primary metrics of diagnostic performance.ResultsEighty-five patients (mean age, 44 years ± 16; 50 male) with injury to the ACL or PCL (n = 31) were ultimately included. Color-coded collagen reconstructions significantly increased overall sensitivity (94/105 [90%] vs. 67/105 [64%]), specificity (248/320 [78%] vs. 215/320 [67%]), PPV (94/166 [57%] vs. 67/162 [39%]), NPV (248/259 [96%] vs. 215/253 [85%]), and accuracy (342/425 [81%] vs. 282/425 [66%]) for the detection of injury to the anterior cruciate ligament (all parameters, p < .001). For injury to the posterior cruciate ligament, diagnostic accuracy increased for complete tears (p < .001). Color-coded collagen reconstructions achieved superior diagnostic confidence, image quality, and noise scores compared to grayscale CT (all parameters, p < .001) and showed good agreement with MRI examinations.ConclusionsDECT-derived color-coded collagen reconstructions yield substantially higher diagnostic accuracy and confidence for assessing the integrity of the cruciate ligaments compared to standard grayscale CT in patients with acute trauma.Key Points• Color-coded collagen reconstructions derived from dual-energy CT yield substantially higher diagnostic accuracy and confidence for the assessment of the cruciate ligaments compared to standard grayscale CT in patients with acute trauma.• Color-coded collagen reconstructions demonstrate good agreement with MRI for the assessment cruciate ligament injury.• Dual-energy CT may serve as a readily available screening approach for patients with acute trauma to the knee when injury to the cruciate ligaments is suspected.

Improved Multiscale Learning Dictionary Image Denoising Algorithm in Curved Wave Domain

It was proposed to develop a better multiscale learning dictionary picture de-noising technique. The approach improves the adaptive threshold curvilinear transform, which can divide an image into different scale information and be used to build a curvilinear multiscale learning dictionary. The method finished the dictionary and sparse coefficient updates in the picture through circular iterations and then superimposed the matching curvilinear wave domain image blocks and performed the curvilinear inverse transform to generate the denoised image. The test was carried out by adding additive Gaussian noise to a standard grayscale image, and the results revealed that the peak signal-to-noise ratio of the grayscale image de-noising result of this paper’s method was improved by 56.6% on average, and the structural similarity was improved by 0.44 on average, compared to the conventional de-noising algorithm. It was determined that the enhanced approach preserved the picture’s edge and texture information well, that image quality was greatly improved, and that the algorithm’s execution efficiency was superior to that of the conventional de-noising algorithm.

Reversible Data Hiding Method for Interpolated Images Based on Modulo Operation and Prediction-Error Expansion

Image reversible data hiding (RDH) using interpolation technology (IT) provides high single-layer embedding capacity and has become a research hotspot. To solve serious image distortion in existing methods caused by secondary embedding based on interpolated pixels, we propose an RDH method for interpolated images based on modulo operation and prediction-error expansion (PEE). Our method differs from existing RDH methods that only consider embedding data into interpolated pixels. First, an image interpolation method is used to generate an interpolated image, which is partitioned into overlapping 3 × 3 blocks. For each block, secret data are embedded into interpolated pixels using the modulo operation and into reference pixel using the PEE. Standard grayscale images of size 512 × 512 were selected from the popular BossBase and BOWS-2 image databases as input images for experiments. Extensive experimental results show that the proposed method avoids pixel values underflow/overflow caused by embedding data and reduces image distortion effectively. For each standard test image, the maximum bits per pixel (BPP) is higher than 1.5, and the corresponding peak signal to noise ratio (PSNR) is higher than 45.0 dB. At the given embedding capacity, the proposed method outperforms recent similar methods regarding the watermarked image quality. Moreover, the proposed method can resist histogram analysis, and the subjective evaluation proves that the proposed method has good imperceptibility.

A Derivative Fidelity-Based Total Generalized Variation Method for Image Restoration

Image edge is the most indicative feature that forms a significant role in image analysis and image understanding, but edge-detail preservation is a difficult task in image restoration due to noise and blur during imaging. The balance between edge preservation and noise removal has always been a difficult problem in image restoration. This paper proposes a derivative fidelity-based total generalized variation method (D-TGV) to improve this balance. First, an objective function model that highlights the ability to maintain details is proposed for the image restoration problem, which is combined with a fidelity term in derivative space and a total generalized variation regularization term. This is designed to achieve the advantage of preserving details in derivative space and eliminate the staircase effect caused by traditional total variation. Second, the alternating direction method of the multipliers (ADMM) is used to solve the model equations by decomposing the original, highly complex model into several simple sub-problems to attain rapid convergence. Finally, a series of experiments conducted on standard grayscale images showed that the proposed method exhibited a good balance between detail preservation and denoising but also reached completion with the fewest iterations compared with the currently established methods.

Visual Cryptography Using Computation-Free Bit-Plane Reconstruction

Visual cryptography (VC) using bit-plane decomposition improves the quality of the reconstructed image. The disadvantage of this scheme is that the decoder needs computation in order to reconstruct the secret image from its bit-planes. To solve this problem, we propose a no-computation bit-plane decomposition visual cryptography (NC-BPDVC). In NC-BPDVC, we convert the grayscale secret image into a multitone image by multilevel halftoning. Then, by exploring the difference between a digital pixel and a printed dot, we design different dot patterns to render a digital pixel. By doing so, we abandon the usual assumption that DPI (dots per inch) equals PPI (pixels per inch) during printing. By adopting the more realistic assumption that DPI can be larger than PPI as is supported by most printers, we use different patterns to render different tone levels. These patterns are carefully designed so that no computation is needed when one needs to reconstruct the multitone image from its bit-planes. Our algorithm is tested on a batch of twenty standard grayscale images. The experimental results confirm the correctness and advantages of the proposed scheme. Compared with the ordinary bit-plane decomposition VC, NC-BPDVC does not need computation. The security of the proposed algorithm is also analyzed and verified.

Hybrid image compression-encryption scheme based on multilayer stacked autoencoder and logistic map

Secure transmission of images over a communication channel, with limited data transfer capacity, possesses compression and encryption schemes. A deep learning based hybrid image compression-encryption scheme is proposed by combining stacked auto-encoder with the logistic map. The proposed structure of stacked autoencoder has seven multiple layers, and back propagation algorithm is intended to extend vector portrayal of information into lower vector space. The randomly generated key is used to set initial conditions and control parameters of logistic map. Subsequently, compressed image is encrypted by substituting and scrambling of pixel sequences using key stream sequences generated from logistic map. The proposed algorithms are experimentally tested over five standard grayscale images. Compression and encryption efficiency of proposed algorithms are evaluated and analyzed based on peak signal to noise ratio (PSNR), mean square error (MSE), structural similarity index metrics (SSIM) and statistical, differential, entropy analysis respectively. Simulation results show that proposed algorithms provide high quality reconstructed images with excellent levels of security during transmission.

Image Denoising Using Sparse Representation and Principal Component Analysis

This study proposes an image denoising algorithm based on sparse representation and Principal Component Analysis (PCA). The proposed algorithm includes the following steps. First, the noisy image is divided into overlapped [Formula: see text] blocks. Second, the discrete cosine transform is applied as a dictionary for the sparse representation of the vectors created by the overlapped blocks. To calculate the sparse vector, the orthogonal matching pursuit algorithm is used. Then, the dictionary is updated by means of the PCA algorithm to achieve the sparsest representation of vectors. Since the signal energy, unlike the noise energy, is concentrated on a small dataset by transforming into the PCA domain, the signal and noise can be well distinguished. The proposed algorithm was implemented in a MATLAB environment and its performance was evaluated on some standard grayscale images under different levels of standard deviations of white Gaussian noise by means of peak signal-to-noise ratio, structural similarity indexes, and visual effects. The experimental results demonstrate that the proposed denoising algorithm achieves significant improvement compared to dual-tree complex discrete wavelet transform and K-singular value decomposition image denoising methods. It also obtains competitive results with the block-matching and 3D filtering method, which is the current state-of-the-art for image denoising.

Otsu’s Image Segmentation Algorithm with Memory-Based Fruit Fly Optimization Algorithm

In this paper, the most common pepper noise in grayscale image noise is investigated in depth in the median filtering algorithm, and the improved median filtering algorithm, adaptive switching median filtering algorithm, and adaptive polar median filtering algorithm are applied to the OTSU algorithm. Two improved OTSU algorithms such as the adaptive switched median filter-based OTSU algorithm and the polar adaptive median filter-based OTSU algorithm are obtained. The experimental results show that the algorithm can better cope with grayscale images contaminated by pretzel noise, and the segmented grayscale images are not only clear but also can better retain the detailed features of grayscale images. A genetic algorithm is a kind of search algorithm with high adaptive, fast operation speed, and good global space finding ability, and it will have a good effect when applied to the threshold finding of the OTSU algorithm. However, the traditional genetic algorithm will fall into the local optimal solution in different degrees when finding the optimal threshold. The advantages of the two interpolation methods proposed in this paper are that one is the edge grayscale image interpolation algorithm using OTSU threshold adaptive segmentation and the other is the edge grayscale image interpolation algorithm using local adaptive threshold segmentation, which can accurately divide the grayscale image region according to the characteristics of different grayscale images and effectively improve the loss of grayscale image edge detail information and jagged blur caused by the classical interpolation algorithm. The visual effect of grayscale images is enhanced by selecting grayscale images from the standard grayscale image test set and interpolating them with bilinear interpolation, bucolic interpolation, NEDI interpolation, and FEOI interpolation for interpolation simulation validation. The subjective evaluation and objective evaluation, as well as the running time, are compared, respectively, showing that the method of this paper can effectively improve the quality of grayscale image interpolation.

CSIS: Compressed sensing‐based enhanced‐embedding capacity image steganography scheme

Image steganography plays a vital role in securing secret data by embedding it in the cover images. Usually, these images are communicated in a compressed format. Existing techniques achieve this but have low embedding capacity. Hence, the goal here is to enhance the embedding capacity while preserving the visual quality of the stego-image. It is also intended to ensure that the scheme is resistant to steganalysis attacks. This paper proposes a compressed sensing image steganography (CSIS) scheme to achieve these goals. In CSIS, the cover image is sparsified block-wise, linear measurements are obtained, and then permissible measurements are selected. Next, the secret data is encrypted, and 2 bits of this encrypted data are embedded into each permissible measurement. For the reconstruction of the stego-image, ADMM and LASSO are used for the resultant optimization problem. Experiments are performed on several standard greyscale images and a colour image. Higher embedding capacity, 1.53 times more compared to the most recent scheme, is achieved. An average of 37.92 dB PSNR value, and average values close to 1 for both the mean SSIM index and the NCC coefficients are obtained, which is considered good. These metrics show that CSIS substantially outperforms existing similar steganography schemes.

Traffic Sign Detection and Classification based on Combination of MSER Features and Multi-language OCR

Road signs are so important because they help preserve safe driving conditions; they also influence the safety of drivers and pedestrians. Without these signs, no one would know the driving speed limit, on which direction to drive down a road, any upcoming hazard, or whether they are approaching a merge. It would be chaotic to drive in such situations. Moreover, these signs help new drivers to find their way in the absence of navigators. Therefore, traffic sign recognition takes a critical place in computer vision applications to develop an effective algorithm. In order to tackle this challenge, we proposed the use of Multi-language Traffic Sign Detection and Classification. One of our contributions in this work is that, instead of using the standard grayscale image, we used the RGB colored image. This image is converted into the 2D highest-level grayscale image using the largest values of each pixel in the RGB channels. The novel generated image has the strongest features of the RGB image that make the features distinct and more informative in the classification step. Consider that, in general, the traffic sign has two colors only, the foreground (text location) and background (non-text location). The Maximally Stable Extremal Regions (MSER) used to extract features from the 2D image where the locations of interest are well-identified exclusively by an extremal property of the intensity function in the location and on its outer boundary. The geometrical properties and thinning operations were used to remove the non-text locations. A multi-language OCR was used to understand multi-language. This proposed method has been tested using 240 images which were collected from the Internet and two datasets. The experimental results demonstrated the performance of the proposed method where the traffic sign detected in 92% of the tested images with a very high percentage of localization.

Incremental Diagnostic Value of Virtual Noncalcium Dual-Energy Computed Tomography for the Depiction of Cervical Disk Herniation Compared With Standard Gray-Scale Computed Tomography.

The aim of this study was to investigative the diagnostic accuracy of colored dual-energy computed tomography (CT) virtual noncalcium (VNCa) series for analyzing cervical disk herniation compared with standard gray-scale CT images, with magnetic resonance imaging (MRI) serving as standard of reference. Data from 57 patients who underwent noncontrast dual-source CT and 3.0-Tesla (T) MRI within 2 weeks between January 2017 and December 2018 were retrospectively analyzed. Five radiologists analyzed standard gray-scale dual-energy CT scans for the presence and degree of cervical disk herniation and spinal nerve root impingement. Readers reassessed scans after 8 weeks using colored VNCa series. Two experienced radiologists set the reference standard in consensus MRI reading sessions. Primary indices of diagnostic accuracy for both CT approaches were sensitivity and specificity, which were compared by application of the McNemar test. A total of 57 patients (mean age, 64 ± 11 years; 30 women) were evaluated (337 intervertebral disks). Magnetic resonance imaging indicated a total of 103 cervical disk herniations. The VNCa reconstructions had higher overall sensitivity compared with gray-scale CT (487/515 [95%; 95% confidence interval (CI), 91%-98%] vs 392/515 [76%; 95% CI, 70%-83%]), as well as higher specificity (1107/1170 [95%; 95% CI, 90%-99%] vs 906/1170 [77%; 95% CI, 72%-82%]) for assessing cervical disk herniation (all P < 0.001). The VNCa reconstructions had higher diagnostic accuracy for analyzing spinal nerve root impingement in comparison with gray-scale CT (sensitivity, 195/230 [85%; 95% CI, 79%-90%] vs 115/230 [50%; 95% CI, 40%-59%]; specificity, 1430/1455 [98%; 95% CI, 94%-100%] vs 1325/1455 [91%; 95% CI, 88%-98%]; accuracy, 1625/1685 [96%; 95% CI, 93%-99%] vs 1440/1685 [86%; 95% CI, 82%-90%]; all P < 0.001). Color-coded VNCa series improved the diagnostic accuracy for assessing cervical disk herniation and spinal nerve root impingement compared with standard gray-scale CT.

Computational ghost imaging with spatiotemporal encoding pseudo-random binary patterns.

Computational ghost imaging (CGI) can reconstruct the pixelated image of a target without lenses and image sensors. In almost all spatial CGI systems using various patterns reported in the past, people often only focus on the distribution of patterns in the spatial dimension but ignore the possibility of encoding in the time dimension or even the space-time dimension. Although the random illumination pattern in CGI always brings some inevitable background noise to the recovered image, it has considerable advantages in optical encryption, authentication, and watermarking technologies. In this paper, we focus on stimulating the potential of random lighting patterns in the space-time dimension for embedding large amounts of information. Inspired by binary CGI and second-order correlation operations, we design two novel generation schemes of pseudo-random patterns for information embedding that are suitable for different scenarios. Specifically, we embed a total of 10,000 ghost images (64 × 64 pixels) of the designed Hadamard-matrix-based data container patterns in the framework of CGI, and these ghost images can be quantitatively decoded to two 8-bit standard grayscale images, with a total data volume of 1, 280, 000 bits. Our scheme has good noise resistance and a low symbol error rate. One can design the number of lighting patterns and the information capacity of the design patterns according to the trade-off between accuracy and efficiency. Our scheme, therefore, paves the way for CGI using random lighting patterns to embed large amounts of information and provides new insights into CGI-based encryption, authentication, and watermarking technologies.

Stego-key-based image steganography scheme using edge detector and modulus function

In this paper, an image steganography scheme is proposed with the intention to enhance the security along with the payload capacity of the cover image. To improve the embedding capacity of the cover image, the hiding process of secret message bits is furnished with the help of cover image characteristic, where more number of secret message bits are embedded into the edge-region instead of the smooth region of the cover image. The secret message bits are concealed into the cover image using the modulus function-based embedding process. For improving the security of the content, we have embedded the secret message bits into the cover-image with reference of keys, i.e., known as a stego-key. The scheme is implemented on some standard greyscale images and satisfactory results are achieved in terms of visual quality along with the higher payload.

Aberrant artery within the brachial plexus: Doppler ultrasound as a savior

Sir, Anatomical variations of the brachial plexus at the supraclavicular region are quite common, and it is important to identify such abnormalities for a successful block. The use of ultrasound to identify these variations will prevent potential adverse complications and will improve the safety and quality of the block.[1] Herewith, we report the case of an aberrant artery within the brachial plexus and the effective use of color Doppler ultrasound for the identification of the artery. A 28-year-old, male patient, a case of malunited fracture of right both bone forearms was posted for open reduction and internal fixation. Preoperative investigations and vitals were within the normal limits. In the operation theater, after attaching all standard monitors, preprocedural ultrasound was done in two-dimensional mode, and an abnormal structure was noticed within the plexus [Figure 1]. Subsequent color Doppler revealed that it was a blood vessel [Figure 2a] and a spectral Doppler also confirmed that it was an artery, but we could not identify its origin [Figure 2b]. After taking all precautions to treat local anesthetic systemic toxicity, brachial plexus block under ultrasound guidance was performed using linear probe by in-plane technique. Needle was inserted from lateral to medial direction, and the tip was placed above the artery. After confirming negative aspiration, injection bupivacaine 0.5% 10 ml was injected. The needle was then redirected below the artery, and the rest of the local anesthetic was injected (10 ml of injection bupivacaine 0.5%). A successful block was confirmed by the complete anesthesia of the upper limb, and the rest of the intraoperative period remained uneventful. Postprocedure, a scan on the other side (left) did not reveal any such aberrant vessel inside the brachial plexus.Figure 1: Two-dimensional image of brachial plexus in the supraclavicular a b region, with aberrant vessel inside the plexus. BP: Brachial plexus, SA: Subclavian artery, AA: Aberrant artery, ASM: Anterior scalenus muscle, MSM: Middle scalenus muscleFigure 2: (a) Color Doppler image of the brachial plexus with aberrant vessel and (b) Spectral Doppler view of the aberrant artery inside the brachial plexusSupraclavicular brachial plexus block under ultrasound guidance offers added advantages such as better anatomical interpretation, precise needle location, and avoidance of intravascular and intraneural injection.[2] Interpretation requires not only careful anatomical orientation, but also precise image optimization, the recognition of artifact, and the ability to distinguish nerves from the tendons, muscle, and vessels. It is important to note that not all circular hypoechoic structures seen on transverse scanning are nerves. Cadaveric studies have shown that vascular structures can be found within the brachial plexus in around 90% of general population, and the most common vessels include aberrant subclavian artery, dorsal scapular artery, and transverse cervical artery.[3] As these variations can compromise the success and safety of supraclavicular technique, we believe that careful preprocedural scanning, especially using color Doppler, is important to differentiate between vascular structures and nerves. Small noncompressible vessels can mimic proximal nerve roots during gray-scale ultrasound evaluations, making color Doppler imperative when sonographically evaluating the brachial plexus.[4] Hence, in addition to standard gray-scale imaging, color Doppler imaging should be done as a routine before selecting the optimal needle path and location of anesthetic placement, to reduce the possibility of inadvertent vascular injury or intravascular anesthetic deposition. Spectral waveforms obtained with Doppler ultrasonography of small arteries will show triphasic pattern but with a less diastolic flow as seen in our case.[5] Although we could not trace the origin and course of the vessel in our case, appreciation of such vessel within the brachial plexus by Doppler ultrasound enabled us to advance and place the needle tip close to the nerve without puncturing the vessel. Before attempting block in such cases, it is also equally important to take all standard precautions to treat local anesthetic systemic toxicity, as this complication may still occur with the use of ultrasound guidance.[6] Thus, in addition to standard imaging, anesthesiologists should consider preprocedural color Doppler to identify the presence of any abnormal vessel inside the plexus for a safe and successful block. Declaration of patient consent The authors certify that they have obtained all appropriate patient consent forms. In the form, the patient has given his consent for his images and other clinical information to be reported in the journal. The patient understands that name and initials will not be published and due efforts will be made to conceal identity, but anonymity cannot be guaranteed. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.

Standard echocardiography versus very-low mechanical index contrast-imaging: left ventricle volumes and ejection fraction multi-reader variability and reference values in a subgroup with no risk factors or cardiac disease.

We retrospectively assessed the rest-phase images of 300 contrast stress- echocardiograms, during which very-low mechanical index (VLMI) imaging with ultrasound enhancing agents (UEAs) was routinely acquired in addition to greyscale echocardiography; intra- and inter-reader variability for left ventricle (LV) volumes and ejection fraction (LVEF) at rest was established in the overall cohort and normal values were calculated in the subset of subjects with no cardiac risk factors or cardiac disease and a normal stress-echocardiogram. Current recommendations for chamber quantification using echocardiography advise the use of UEAs in case of insufficient quality of endocardial visualization, but normal values for LV volumes and LVEF using UEAs have never been published using currently recommended VLMI method. Single-centre retrospective study, enrolling subjects referred to contrast stress- echocardiography for suspect coronary artery disease, including the acquisition of both standard 2D greyscale and bolus UEAs with VLMI, regardless of the image quality. This enables offline measurement of the LV volumes and LVEF for both greyscale and UEAs-VLMI images at rest in all subjects. Images were allocated to 3 readers in random order. Normal range for LV volumes and LVEF was also derived in a subset of apparently healthy subjects. In the 298 exams finally assessed, measurement variability among the three readers was lower with UEAs-VLMI, in particular for end-systolic volumes (intra-class correlation coefficient for concordance improved from 0.855 to 0.916, for LVEF from 0.68 to 0.783, p < 0.01), intra- observer variability reduced (Lin's correlation coefficient for LVEF from 0.747 to 0.857, p < 0.01). Normal mean indexed LV end-diastolic volume with UEAs-VLMI, compared to greyscale imaging, was + 14ml/m2, while LVEF was + 5 to + 6% points. This is the largest study specifically addressing UEAs-VLMI imaging and comparing data with standard greyscale imaging; it demonstrates a reduction in measurement variability of LV volumes and LVEF. Normal reference values for VLMI ultrasound are reported for the first time in comparison with greyscale values.

Digital Image Enhancement Gray Scale Images In Frequency Domain

The aim of the research is to improve the distorted digital images using one of the methods of frequency domain. Where The Transform Cosine Discrete (DCT) function was used as one methods of frequency domain. Good results have been obtained using this method. standard gray scale images, which have distortions, have been improved and get improved image results.

A novel multi-dimensional encryption technique to secure the grayscale images and color images in public cloud storage

Image encryption is one of the techniques which is used to maintain the image confidentiality. Trust is needed to be created and retained in the cloud between the service provider and the end user. The existing image encryption methods are using a map structure, Rubik cube method, DCT-based approach and s-box designs. The existing algorithms are mostly designed with the concept of offline image encryption. While coming to the online encryption, the image encryption algorithms needed to be redefined with a lightweight approach with an improved security level or same security level of existing algorithms. These things are taken into consideration in the proposed multi-dimensional encryption technique design. The proposed technique is using two phases to perform the encryption. The proposed technique’s two phases are image pixel shuffling phase and image pixel rearrange phase. The proposed technique is already tested successfully on the standard grayscale images, and the results were obtained and it satisfied the objectives. In this paper, the proposed technique was tested on the standard color images and the gained results. Those results have been used to analyze the performance and efficiency of proposed technique with the existing technique, by using different parameters which include PSNR, MSE, information entropy, coefficient correlation, NPCR and UPCI.

Improvement of quantized adaptive switching median filter for impulse noise reduction in gray-scale digital images

Digital images may suffer from fixed value impulse noise due to several causes. The noise significantly degrades the quality of the image, which may affect the subsequence image processing. Therefore, a noise reduction technique is required to restore the image. In this paper, a new method, which is called improvement of quantized adaptive switching median filter (IQASMF), has been proposed to reduce the fixed value impulse noise from gray-scale digital images. The implementation of IQASMF has five processing blocks. The first processing block is the noise detection block, where the noise pixel candidates are detected based on the intensity value. Then estimation of the local noise density is done by the second processing block. Next, the third processing block filters the corrupted pixel candidates with filters of predefined size, depending on the local noise density. After that, the noise mask is updated in the fourth processing block. Finally, the fifth processing block processes the noise residuals from the third processing block by using a size adaptive filter. Experimental results from twenty standard gray-scale images of various sizes have shown that IQASMF has the ability to restore images for up to 99 % of the impulse noise corruption. As compared with the other five median filter-based methods, from the measures of mean squared error (MSE) and structural similarity index (SSIM), it is shown that the performance of IQASMF is equivalent to the performance of other methods at low and medium levels of corruption. However, at high corruption levels, IQASMF has demonstrated the best performance in terms of MSE and SSIM. The outputs from IQASMF also have the best visual appearance.