Standard Deviation Of Pixel Intensity Research Articles

Experimental characterisation of the spreading of polymeric powders in powder bed fusion additive manufacturing process at changing temperature conditions

This study investigates the spreadability behaviour of four different polymeric materials, namely Polyamide 6, Polyamide 6 Black, Polypropylene, and Thermoplastic Polyurethane, under different spreading speeds (30 and 3 mm/s) and powder bed temperatures (25, 80, 110 °C) using a purposely developed experimental apparatus. Macroscopic and microscopic images of the powder layer were taken after completing the powder spreading step. A thresholding-based image processing method was utilised to evaluate the fraction of the bed area not covered by particles (NCF), and the standard deviation of pixel intensities in grayscale images (SDG) was calculated to evaluate powder layer quality in macroscopic images. NCF and SDG can provide quantitative evaluation of the quality of the spread layer, NCF in a logarithmic scale ranking and SDG in a linear scale ranking. A wavelet analysis technique was developed on microscopic panorama images obtained with grazing light to characterise the surface roughness of the layer. Results indicate that the spreadability generally worsens much more significantly than powder flow properties at increasing temperatures and, remarkably, that flowability and spreadability are unrelated. As expected, the temperature effect on powder spreading changes for the different powders are mostly governed by the approach to the powder melting temperature. Minor effects on the final layer quality were also observed at changing spreading speed.

Particle Tracking and Micromixing Performance Characterization with a Mobile Device.

Strategies to stir and mix reagents in microfluid devices have evolved concomitantly with advancements in manufacturing techniques and sensing. While there is a large array of reported designs to combine and homogenize liquids, most of the characterization has been focused on setups with two inlets and one outlet. While this configuration is helpful to directly evaluate the effects of features and parameters on the mixing degree, it does not portray the conditions for experiments that involve more than two substances required to be subsequently combined. In this work, we present a mixing characterization methodology based on particle tracking as an alternative to the most common approach to measure homogeneity using the standard deviation of pixel intensities from a grayscale image. The proposed algorithm is implemented on a free and open-source mobile application (MIQUOD) for Android devices, numerically tested on COMSOL Multiphysics, and experimentally tested on a bidimensional split and recombine micromixer and a three-dimensional micromixer with sinusoidal grooves for different Reynolds numbers and geometrical features for samples with fluids seeded with red, blue, and green microparticles. The application uses concentration field data and particle track data to evaluate up to eleven performance metrics. Furthermore, with the insights from the experimental and numerical data, a mixing index for particles (mp) is proposed to characterize mixing performance for scenarios with multiple input reagents.

Comparison of two ultrasound contrast agents for left ventricular opacification

Abstract Funding Acknowledgements Type of funding sources: None. Background Several ultrasound contrast agents have been approved and used for measurement of LV volumes and ejection fraction (EF). However, there has been no direct comparison between these agents. Purpose To compare different imaging parameters between two widely used ultrasound contrast agents for LV opacification when imaged with a low power contrast specific imaging modality Methods A retrospective study was performed using recordings of the local echocardiography registry for patients undergoing chemotherapy for cancer treatment. Patients were included who underwent follow-up echocardiograms (3 months) with 2 different ultrasound contrast agents and had only minor changes in the LV ejection fraction (EF) between the studies (<5%). Qualitative and quantitative analysis of the contrast effect was performed in apical four- and two chamber views (4CV,2CV). For quantitative analysis of the LV opacification square regions of interest (ROI) were placed in the apical and basal regions of the ventricle in each echocardiographic recording and the arithmetic mean and standard deviation of pixel intensity within each ROI was recorded throughout the entire loop. Qualitative visual assessment was performed using a three step visual score in order to assess the endocardial border delineation, basal attenuation and apical swirling. Wilcoxon signed-rank test was used to compare the measurements obtained with different contrast agents. Results 41 patients fulfilled the inclusion criteria. In all patients suitable recordings for measurement of LV volumes and EF were obtained. The mean + standard deviation of the EF was 61 + 9%. Both contrast agents provided intensive LV opacification in the basal and apical cavities. The video intensities (mean + standard deviation) in the apical and basal cavities were comparable across both contrast agents in the 2CV apical ROI (219.96 + 21.42 vs 224.69 + 20.23, p = 0.32), the 2CV basal ROI (114.55, 42.02 vs 112.79, 42.57, p = 0.81), the 4CV apical ROI (228.02, 17.80 vs 225.51, 20.37, p = 0.30), and the 4CV basal ROI (100.05, 41.11 vs 92.84, 42.54, p = 0.37). Visual assessment of endocardial delineation on end-diastolic and end-systolic frames revealed no statistical difference between the contrast agents. Conclusion In patients with normal LV function no clinically relevant differences were found between the two contrast agents regarding LV opacification and endocardial border delineation.

Improved quantification of immune-gold labeling and its use to compare the distribution of cellular factors among sub-chloroplast compartments

Quantification of immuno-gold labeling can provide valuable information on the quantity and localization of a target within a region of interest (ROI). Background subtraction usually requires preparation of material with a deliberately reduced amount of target component often by gene knockout/knockdown. This paper reports a modified method without the need for gene knockout/knockdown, by using a region outside the ROI as a background and non-immune serum to verify the reliability of the data. An optimized parameter for use in image processing was also developed to improve semi-automatic segmentation of gold particles, by using the standard deviation of pixel intensity together with default parameters (size and intensity) to improve specificity. The modified methods were used to quantify the gold labeling of various components within chloroplasts and their 3 sub-organelle compartments (thylakoid, stroma and starch). Rubisco, actin, myosin, β-tubulin, Endoplasmic reticulum-retention signal HDEL, Sterol methyltransferase 1, and double stranded RNA were all effectively and consistently quantified at the level of the different sub-chloroplast compartments. The approach should be applicable more widely for high resolution labelling of samples in which a background requiring gene knockout/knockdown is not a realistic option.

Radiomic Analysis of MRI Images is Instrumental to the Stratification of Ovarian Cysts.

The imaging diagnosis of malignant ovarian cysts relies on their morphological features, which are not always specific to malignancy. The histological analysis of these cysts shows specific fluid characteristics, which cannot be assessed by conventional imaging techniques. This study investigates whether the texture-based radiomics analysis (TA) of magnetic resonance (MRI) images of the fluid content within ovarian cysts can function as a noninvasive tool in differentiating between benign and malignant lesions. Twenty-eight patients with benign (n = 15) and malignant (n = 13) ovarian cysts who underwent MRI examinations were retrospectively included. TA of the fluid component was undertaken on an axial T2-weighted sequence. A comparison of resulted parameters between benign and malignant groups was undertaken using univariate, multivariate, multiple regression, and receiver operating characteristics analyses, with the calculation of the area under the curve (AUC). The standard deviation of pixel intensity was identified as an independent predictor of malignant cysts (AUC = 0.738; sensitivity, 61.54%; specificity, 86.67%). The prediction model was able to identify malignant lesions with 84.62% sensitivity and 80% specificity (AUC = 0.841). TA of the fluid contained within the ovarian cysts can differentiate between malignant and benign lesions and potentially act as a noninvasive tool augmenting the imaging diagnosis of ovarian cystic lesions.

A Convolutional Neural Network Classifier VGG-19 Architecture for Lesion Detection and Grading in Diabetic Retinopathy Based on Deep Learning

Diabetic Retinopathy (DR) is a type of disease in eyes as a result of a diabetic condition that ends up damaging the retina, leading to blindness or loss of vision. Morphological and physiological retinal variations involving slowdown of blood flow in the retina, elevation of leukocyte cohesion, basement membrane dystrophy, and decline of pericyte cells, develop. As DR in its initial stage has no symptoms, early detection and automated diagnosis can prevent further visual damage. In this research, using a Deep Neural Network (DNN), segmentation methods are proposed to detect the retinal defects such as exudates, hemorrhages, microaneurysms from digital fundus images and then the conditions are classified accurately to identify the grades as mild, moderate, severe, no PDR, PDR in DR. Initially, saliency detection is applied on color images to detect maximum salient foreground objects from the background. Next, structure tensor is applied powerfully to enhance the local patterns of edge elements and intensity changes that occur on edges of the object. Finally, active contours approximation is performed using gradient descent to segment the lesions from the images. Afterwards, the output images from the proposed segmentation process are subjected to evaluate the ratio between the total contour area and the total true contour arc length to label the classes as mild, moderate, severe, No PDR and PDR. Based on the computed ratio obtained from segmented images, the severity levels were identified. Meanwhile, statistical parameters like the mean and the standard deviation of pixel intensities, mean of hue, saturation and deviation clustering, are estimated through K-means, which are computed as features from the output images of the proposed segmentation process. Using these derived feature sets as input to the classifier, the classification of DR was performed. Finally, a VGG-19 deep neural network was trained and tested using the derived feature sets from the KAGGLE fundus image dataset containing 35,126 images in total. The VGG-19 is trained with features extracted from 20,000 images and tested with features extracted from 5,000 images to achieve a sensitivity of 82% and an accuracy of 96%. The proposed system was able to label and classify DR grades automatically.

Quality of CT Imaging of Periocular Metallic Foreign Bodies Using Artifact Reduction Software.

CT is the standard of care for assessment of ocular and orbital trauma; however, artifacts from metallic foreign bodies can limit the utility of CT. The authors hypothesize that implementation of metal artifact reduction techniques can improve image quality and diagnostic confidence for a diverse group of interpreters. A case series of ten subjects with retained periocular metallic foreign bodies imaged with CT were identified retrospectively from a large urban trauma center. Postacquisition images were processed with an iterative-based metal streak artifact reduction software. The severity of the metal streak artifact was assessed by clinicians including radiologists (4), ophthalmologists (4), and oculoplastic specialists (3) using a numeric scale to grade images on seven clinically relevant criteria. Each image was also analyzed to measure the size of the artifact and degree of streaking. Overall confidence in diagnosis and severity of metallic streak was improved with metallic artifact reduction (p < 0.001, Wilcoxon signed-rank test). Similarly, confidence in assessing specific features-including extra-ocular muscle, optic nerve, globe rupture, orbital fracture and identification of foreign bodies-was improved after metallic artifact reduction (p < 0.001, Wilcoxon signed-rank test). The standard deviation of pixel intensity for a path surrounding the foreign body as well as the area of the streak artifact decreased in the metallic artifact reduction-processed images (p < 0.001, paired t test). Metal artifact reduction in CT has potential benefits in improving image quality and reader confidence for periocular trauma cases in real-world settings.

Texture analysis of placental MRI: can it aid in the prenatal diagnosis of placenta accreta spectrum?

To determine if texture analysis can differentiate placenta accreta spectrum (PAS) from normal placenta on MRI. We performed retrospective image analysis of 80 patients, comprised of 46 patients with PAS and 34 patients without PAS. Histopathology was used as the reference standard. Sagittal single shot fast spin echo T2-weighted MRI sequences acquired from a single institution were analyzed. Placental heterogeneity was quantified using in-house software on a Matlab platform, including the standard deviation of pixel intensity, coefficient of variation, gray-level co-occurrence matrices (GLCM), histogram-oriented gradients (HOG), and fractal analysis with box sizes from 2 to 512. Two-tailed unpaired Student's t test was used with statistical significance of p < 0.05. PAS was associated with higher values for standard deviation of pixel intensity and fractal analysis at every box size. Fractal analysis at box sizes 256 (p = 0.011) and 32 (p = 0.021), and standard deviation of pixel intensity (p = 0.023) were the most statistically significant. Fractal values at box size 256 for PAS was 0.13 versus 0.090 for patients without PAS, while standard deviation of pixel intensity was 3.7 for PAS versus 2.5 for patients without PAS. No statistically significant association between PAS and GLCM, coefficient of variation, and HOG was found. Statistically significant differences were found between normal and abnormal groups using standard deviation of pixel intensity and fractal analysis.

Locally Statistical Dual-Mode Background Subtraction Approach

Due to the variety of background model in the real world, detecting changes in a video cannot be addressed exhaustively by a simple background subtraction method, especially with several motion detection challenges, such as dynamic background, camera jitter, intermittent object motion, and so on. In this paper, we propose an efficient background subtraction method, namely locally statistical dual-mode (LSD), for detecting moving objects in video-based surveillance systems. The method includes a local intensity pattern comparison algorithm for foreground segmentation by analyzing the homogeneity of intensity patterns of the input frame and the background model, in which the homogeneity is calculated by the mean and standard deviation of pixel intensity. Besides that, a dual-mode scheme is developed to temporally update the background model for the short- and long-term scenarios corresponding to sudden and gradual changes in the background. The advantage of this scheme is the allowance of updating the model in both pixel- and frame-wise manners simultaneously. The parameters used in both the local intensity pattern comparison algorithm and the dual-mode background model updating scheme are estimated for every input frame consecutively based on local and global statistical information of segmentation result. In experiments, the proposed LSD method is extensively evaluated on the Wallflower and CDnet2014 datasets; and remarkable performance demonstrates its preeminence to the many state-of-the-art background subtraction approaches in terms of segmentation accuracy and computational complexity.

Identifying Laser-Induced Plasma Spectra of Particle in Gas-Solid Flow Based on the Standard Deviation of Pixel Intensities

A new method which schemes of conditional data processing named standard deviation (SD) method is present and evaluated for identifying spectral data of gas-solid particle flow based on laser-induced plasma spectroscopy. The SD method is also compared with two conditional analysis methods called the SNR method and the absolute intensity method. First, the effect of three methods identifying the spectral data of the same fly ash sample is compared. Then, the threshold stability of the three methods under different conditions was observed by screening the spectral data of a set of 12 coal samples. The rejection rate, error rejection rate (or error rejection) and missing rejection rate (or missing rejection) were used to evaluate these three different approaches. And probability of false hits is determined by evaluating various conditional processing thresholds. The characteristic peaks used for the analysis of fly ash and coal samples are selected as Si 288.16 nm and C 247.86 nm, respectively. The result shows that for all approaches the SD method has the minimum rejection rate and error rejection (both 0%) when the SD threshold value is 120, the total rejection rate of the SD method is also the lowest of the three under this condition, which is 37.67%. It means that true data and false data could be completely and accurately identified by using the SD method. Moreover, for different samples and experimental conditions, the thresholds of absolute intensity method and SNR method fluctuate in the range of 83 - 135 counts and 2.05 - 4.00, respectively, while the threshold of the SD method has been stable at 55. Compared with other two methods, the SD method is more universal when faced with variable detection conditions. So it has more advantages in the rapid detection and analysis of power plants.

Classification of Acute Myelogenous Leukemia (AML M2 and AML M3) using Momentum Back Propagation from Watershed Distance Transform Segmented Images

This study uses image processing to analyze white blood cell with leukemia indicated that includes the identification, analysis of shapes and sizes, as well as white blood cell count indicated the symptoms of leukemia. A case study in this research was blood cells, from the type of leukemia Acute Myelogenous Leukemia (AML), M2 and M3 in particular. Image processing operations used for segmentation by utilizing the color conversion from RGB (Red, Green dab Blue) to obtain white blood cell candidates. Furthermore, the white blood cells candidates are separated by other cells with active contour without edge. WBC (White Blood Cell) results still have intersected or overlap condition. Watershed distance transform method can separate overlap of WBC. Furthermore, the separation of the nucleus from the cytoplasm using the HSI (Hue Saturation Intensity). The further characteristic extraction process is done by calculating the area WBC, WBC edge, roundness, the ratio of the nucleus, the mean and standard deviation of pixel intensities. The feature extraction results are used for training and testing in determining the classification of AML: M2 and M3 by using the momentum backpropagation algorithm. The classification process is done by testing the numeric data input from the feature extraction results that have been entered in the database. K-Fold validation is used to divide the amount of training data and to test the classification of AML M2 and M3. The experiment results of eight images trials, the result, was 94.285% per cell accuracy and 75% per image accuracy

WE‐D‐18A‐07: Development and Evaluation of Automated Noise Measurement Technique in CT Images

Purpose:To develop and evaluate an automated technique which measures the noise level in CT images.Methods:Candidate regions of interests (ROIs) with circular shape were selected with a criteria where the pixel intensities on Gaussian filtered image lie within a predefined range. The ROIs were further refined with the sum of absolute gradients within each candidate ROI being used as a ROI feature to distinguish homogeneous ROIs. A histogram of ROI feature was created, and the ROIs with feature values less than the mode of histogram were selected as homogeneous ROIs. Connected component analysis was then used to remove the non‐clustered ROIs to restrict the location of ROI in desired organ. Standard deviation of pixel intensities was calculated for each cluster of homogeneous ROIs to represent the local noise level, and its mode value was used to represent the noise level. Fifty abdomen CT images were collected, and five observers participated to manually measure the noise level with a guidance to draw same size ROIs. Manual results were then averaged to establish a reference value, and a correlation coefficient(r) between a reference value and our automatic result. Intra‐ and inter‐rater agreement were compared to demonstrate the performance of our proposed technique.Results:We confirmed successful operation of automatic homogeneous region localization and noise level measurement. Intra‐ and inter‐rater agreement(r) ranged from 0.70 to 0.95, and from 0.79 to 0.94, respectively. Agreements between reference and manual measurements by raters ranged from 0.85 to 0.95, and those between a reference and an automated result by our proposed method was 0.90.Conclusion:Our developed technique has successfully performed the noise measurement task in all the test images by showing an agreement of 0.90 with the reference measurement. Our proposed method enables the automated assessment of CT image noise.

Computer-aided detection system for masses in automated whole breast ultrasonography: development and evaluation of the effectiveness

Purpose:The aim of this study was to evaluate the performance of a proposed computer-aided detection (CAD) system in automated breast ultrasonography (ABUS).Methods:Eighty-nine two-dimensional images (20 cysts, 42 benign lesions, and 27 malignant lesions) were obtained from 47 patients who underwent ABUS (ACUSON S2000). After boundary detection and removal, we detected mass candidates by using the proposed adjusted Otsu's threshold; the threshold was adaptive to the variations of pixel intensities in an image. Then, the detected candidates were segmented. Features of the segmented objects were extracted and used for training/testing in the classification. In our study, a support vector machine classifier was adopted. Eighteen features were used to determine whether the candidates were true lesions or not. A five-fold cross validation was repeated 20 times for the performance evaluation. The sensitivity and the false positive rate per image were calculated, and the classification accuracy was evaluated for each feature.Results:In the classification step, the sensitivity of the proposed CAD system was 82.67% (SD, 0.02%). The false positive rate was 0.26 per image. In the detection/segmentation step, the sensitivities for benign and malignant mass detection were 90.47% (38/42) and 92.59% (25/27), respectively. In the five-fold cross-validation, the standard deviation of pixel intensities for the mass candidates was the most frequently selected feature, followed by the vertical position of the centroids. In the univariate analysis, each feature had 50% or higher accuracy.Conclusion:The proposed CAD system can be used for lesion detection in ABUS and may be useful in improving the screening efficiency.

Short-term sandbar variability based on video imagery: Comparison between Time–Average and Time–Variance techniques

Time–exposure intensity (averaged) images are commonly used to locate the nearshore sandbar position ( x b ), based on the cross-shore locations of maximum pixel intensity ( x i ) of the bright bands in the images. It is not known, however, how the breaking patterns seen in Variance images (i.e. those created through standard deviation of pixel intensity over time) are related to the sandbar locations. We investigated the suitability of both Time–exposure and Variance images for sandbar detection within a multiple bar system on the southern coast of Brazil, and verified the relation between wave breaking patterns, observed as bands of high intensity in these images and cross-shore profiles of modeled wave energy dissipation ( x D ). Not only is Time–exposure maximum pixel intensity location ( x i-Ti ) well related to x b , but also to the maximum pixel intensity location of Variance images ( x i-Va ), although the latter was typically located 15 m offshore of the former. In addition, x i-Va was observed to be better associated with x D even though x i-Ti is commonly assumed as maximum wave energy dissipation. Significant wave height (Hs) and water level (η) were observed to affect the two types of images in a similar way, with an increase in both Hs and η resulting in x i shifting offshore. This η-induced x i variability has an opposite behavior to what is described in the literature, and is likely an indirect effect of higher waves breaking farther offshore during periods of storm surges. Multiple regression models performed on x i , Hs and η allowed the reduction of the residual errors between x b and x i , yielding accurate estimates with most residuals less than 10 m. Additionally, it was found that the sandbar position was best estimated using x i-Ti ( x i-Va ) when x b was located shoreward (seaward) of its mean position, for both the first and the second bar. Although it is unknown whether this is an indirect hydrodynamic effect or is indeed related to the morphology, we found that this behavior can be explored to optimize sandbar estimation using video imagery, even in the absence of hydrodynamic data.

Respiratory gating in positron emission tomography: A quantitative comparison of different gating schemes

Respiratory gating is used for reducing the effects of breathing motion in a wide range of applications from radiotherapy treatment to diagnostical imaging. Different methods are feasible for respiratory gating. In this study seven gating methods were developed and tested on positron emission tomography (PET) listmode data. The results of seven patient studies were compared quantitatively with respect to motion and noise. (1) Equal and (2) variable time-based gating methods use only the time information of the breathing cycle to define respiratory gates. (3) Equal and (4) variable amplitude-based gating approaches utilize the amplitude of the respiratory signal. (5) Cycle-based amplitude gating is a combination of time and amplitude-based techniques. A baseline correction was applied to methods (3) and (4) resulting in two new approaches: Baseline corrected (6) equal and (7) variable amplitude-based gating. Listmode PET data from seven patients were acquired together with a respiratory signal. Images were reconstructed applying the seven gating methods. Two parameters were used to quantify the results: Motion was measured as the displacement of the heart due to respiration and noise was defined as the standard deviation of pixel intensities in a background region. The amplitude-based approaches (3) and (4) were superior to the time-based methods (1) and (2). The improvement in capturing the motion was more than 30% (up to 130%) in all subjects. The variable time (2) and amplitude (4) methods had a more uniform noise distribution among all respiratory gates compared to equal time (1) and amplitude (3) methods. Baseline correction did not improve the results. Out of seven different respiratory gating approaches, the variable amplitude method (4) captures the respiratory motion best while keeping a constant noise level among all respiratory phases.

TU‐C‐330A‐03: Sulci Density Map to Aid in the Use of Apparent Diffusion Coefficient for Therapy Evaluation

Purpose: To quantify the density and spatial variation of sulci in the human brain so as to more accurately calculate Apparent Diffusion Coefficients (ADCs) for use in radiotherapy evaluation. Methods and Materials: ADCs are calculated for Volumes Of Interest (VOI) using Diffusion Weighted Magnetic Resonance Imaging (DWMRI).In the brain, the sulci are the narrow fissures separating convolutions and are filled with CerbroSpinal Fluid (CSF). Since CSF is free fluid, a VOI that contains a high density of sulci should, in principal, have a higher ADC.Using ImageJ (http://rsb.info.nih.gov/ij/), we have analyzed sagittal T1 weighted MR images of a number of patients. The images sets were acquired on one of two different GE MRI machines: 1.5T and 3.0T field strength. We have plotted the normalized pixel standard deviation, as a function of distance from a medial point. We have concentrated on the cerebral hemispheres, superior to the corpus callosum so as to focus on sulci and avoid voxel dissimilarities due to variations in other brain anatomy. Results: Relative to the 25% most medial slices, we see a decrease in the normalized standard deviation of pixel intensity of 13.7% ± 5.6% (SD) of the next 25% of the slices. The most lateral 50% of the slices (25% left and right) had about the same normalized standard deviation as the most medial (increase of 1.0% ± 7.1% (SD)). These results are consistent with the fact that near the periphery of each brain hemisphere, there are more sulci. Conclusion: Sulci density, as measured by normalized standard deviation of pixel intensity, has a substantial variation across the hemispheres of the brain. This fact should be considered when assessing variations in ADCs used for radiotherapy evaluation.

Image non-uniformity in magnetic resonance imaging: its magnitude and methods for its correction.

Image non-uniformity in magnetic resonance imaging (MRI) is a recognised problem when using surface coils but is rarely mentioned with respect to standard head coils, yet it can be a source of error in clinical interpretation of images. Figure 1 demonstrates the type of artefacts due to image non-uniformity for a large, homogeneous solution of water and copper sulphate. The image was acquired in the coronal section using a standard head coil, and vertical (i.e. read direction) and horizontal (i.e. phase encode direction) pixel intensity profiles through the centre of the image are illustrated. The standard deviation of pixel intensities, expressed as a percentage of the mean value, is greater than ±27% along the 23 cm length of this phantom. Figure 2 demonstrates how these image nonuniformities can affect the sagittal image of a normal volunteer. The display window width and level have been chosen to produce optimum visual contrast at the level of the cerebellum and brain stem but, as a result to image no...