Quantitative Measure Of Quality Research Articles

Automated orientation of water molecules in neutron crystallographic structures of proteins.

The structure and function of proteins are strongly affected by the surrounding solvent water, for example through hydrogen bonds and the hydrophobic effect. These interactions depend not only on the position, but also on the orientation, of the water molecules around the protein. Therefore, it is often vital to know the detailed orientations of the surrounding ordered water molecules. Such information can be obtained by neutron crystallography. However, it is tedious and time-consuming to determine the correct orientation of every water molecule in a structure (there are typically several hundred of them), which is presently performed by manual evaluation. Here, a method has been developed that reliably automates the orientation of a water molecules in a simple and relatively fast way. Firstly, a quantitative quality measure, the real-space correlation coefficient, was selected, together with a threshold that allows the identification of water molecules that are oriented. Secondly, the refinement procedure was optimized by varying the refinement method and parameters, thus finding settings that yielded the best results in terms of time and performance. It turned out to be favourable to employ only the neutron data and a fixed protein structure when reorienting the water molecules. Thirdly, a method has been developed that identifies and reorients inadequately oriented water molecules systematically and automatically. The method has been tested on three proteins, galectin-3C, rubredoxin and inorganic pyrophosphatase, and it is shown that it yields improved orientations of the water molecules for all three proteins in a shorter time than manual model building. It also led to an increased number of hydrogen bonds involving water molecules for all proteins.

Utilization of Descriptive Sensory Analysis and Volatile Analysis to Determine Quality Indicators of Aquacultured Whiteleg Shrimp (Litopanaeus vannemei) during Refrigerated Storage

ABSTRACT Traditionally, shrimp meat quality has been evaluated using subjective qualitative (pass/fail grading) sensory methods. This study aims to develop quantitative sensory indicators that contribute to a more objective and reliable evaluation of shrimp quality. A headspace solid-phase microextraction (HS-SPME) method was optimized and coupled with gas chromatography-mass spectrometry (GC-MS) to identify and semi-quantify 35 volatiles accumulated by the end of storage at 4°C. A trained descriptive sensory panel developed eight aroma descriptors associated with quality changes of shrimp. Five out of the eight aroma descriptors are potential quality indicators due to significant correlations with the storage time and good alignment with presumed shrimp grading cutoffs, including one indicator of freshness (“salty water”) and four indicators of spoilage of shrimp (“oxidized oil,” “natto water,” “sour milk,” and “dirty sock”). The intensity ratings of these sensory indicators significantly correlated with the concentration changes of volatiles that were identified to be important chemical spoilage indicators of shrimp. Overall, the aroma indicators determined in this study can lead to quantitative sensory measurements of shrimp quality that correspond well with the results of objective analytical methods.

ISleep

The quality of sleep is an important factor in maintaining a healthy life style. A great deal of work has been done for designing sleep monitoring systems. However, most of existing solutions bring invasion to users more or less due to the exploration of the accelerometer sensor inside the device. This article presents iSleep—a practical system to monitor people’s sleep quality using off-the-shelf smartphone. iSleep uses the built-in microphone of the smartphone to detect the events that are closely related to sleep quality, and infers quantitative measures of sleep quality. iSleep adopts a lightweight decision-tree-based algorithm to classify various events. For two-user scenario, iSleep differentiates the events of two users either when two phones can collaborate with each other or when two phones cannot communicate with each other. The experimental results show that iSleep achieves consistently above 90% accuracy for event classification in a variety of different settings in one-user scenario and above 92% accuracy for distinguishing users in two-user scenario. By providing a fine-grained sleep profile that depicts details of sleep-related events, iSleep allows the user to track the sleep efficiency over time and relate irregular sleep patterns to possible causes.

Regularization-based modulation transfer function compensation for optical satellite image restoration using joint statistical model in curvelet domain

The heavy-tailed properties of modulation transfer function (MTF) typically introduce noise and an aliasing effect in the MTF compensation (MTFC) effort to improve the spatial quality of optical satellite images. These degradative effects compromise the image’s signal-to-noise ratio (SNR). Consequently, users must evaluate the relative importance of image sharpness versus SNR for their applications to decide whether MTFC processing is appropriate. We propose a high-fidelity MTFC method that executes an optimal trade-off between noise regularization and detail preservation in the image restoration process to address this problem. To this end, we exploit the merit of image prior characteristics in both local smoothness and nonlocal self-similarity properties of an image in a hybrid domain (viz., space spatial and frequency) to design effective regularization terms that reflect these image properties. Furthermore, we establish a simple joint statistical model in the curvelet domain to combine these two properties. To make this regularization-based MTFC method tractable and robust, we employ the multiobjective bilevel optimization approach to solve the severely ill-posed inverse problem of MTFC. We conduct extensive experiments to evaluate the proposed regularization-based MTFC method using synthetically blurred images simulated from the level 2A product of IKONOS and real satellite images with unknown blur. Quantitative measurements of image quality reveal that the proposed method produces competitive restoration results with minimum computational complexity and exhibits a good convergence property. These experimental results show that the proposed method can find a compromise between regularizing noise and preserving image fidelity.

Neural networks-based regularization for large-scale medical image reconstruction

In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction. Recently, Deep Learning methods using iterative neural networks (NNs) and cascaded NNs have been reported to achieve state-of-the-art results with respect to various quantitative quality measures as PSNR, NRMSE and SSIM across different imaging modalities. However, the fact that these approaches employ the application of the forward and adjoint operators repeatedly in the network architecture requires the network to process the whole images or volumes at once, which for some applications is computationally infeasible. In this work, we follow a different reconstruction strategy by strictly separating the application of the NN, the regularization of the solution and the consistency with the measured data. The regularization is given in the form of an image prior obtained by the output of a previously trained NN which is used in a Tikhonov regularization framework. By doing so, more complex and sophisticated network architectures can be used for the removal of the artefacts or noise than it is usually the case in iterative NNs. Due to the large scale of the considered problems and the resulting computational complexity of the employed networks, the priors are obtained by processing the images or volumes as patches or slices. We evaluated the method for the cases of 3D cone-beam low dose CT and undersampled 2D radial cine MRI and compared it to a total variation-minimization-based reconstruction algorithm as well as to a method with regularization based on learned overcomplete dictionaries. The proposed method outperformed all the reported methods with respect to all chosen quantitative measures and further accelerates the regularization step in the reconstruction by several orders of magnitude.

Effects of Color Quantization on JPEG Compression

This paper analyzes the effects of color quantization on standard JPEG compression. Optimized color palettes were used to quantize natural images, using dithering and chroma subsampling as optional. The resulting variations on file size and quantitative quality measures were analyzed. Preliminary results, using a small image database, show that file size suffered an average 20% increase and a concomitant loss in quality was perceived ([Formula: see text]6dB PSNR, [Formula: see text]0.16 SSIM and [Formula: see text]9.6 Butteraugli). Color quantization present itself as an ineffective tool on JPEG compression but if necessarily imposed, on high quality compressed images, it might lead to a negligible increase in data size and quality loss. In addition dithering seems to always decrease JPEG compression ratio.

Internal control and real earnings management in the French context

Purpose This study aims to examine whether the internal control system quality in the French context improve the information quality having been reflected by the level of real earnings management (REM) measured by inventory overproduction, discretionary expenses reduction and sales manipulation. Design/methodology/approach The research uses a multiple regression analysis to examine the association between internal control and REM. The years 2010-2015 are used as analysis period by focusing on the French context. Three panel data are applied to the companies belonging to the Cotation Assistée en Continu (CAC) 40 index for the entire study period. Findings The results show that high internal control index has a negative impact on the REM and that better internal control indeed makes financial reporting more credible to investors. Further, the results demonstrate that control environment, risk assessment, control activities and monitor are the components that mainly affect REM. Originality/value The results contribute to the literature dealing with the relationship between internal control quality and REM by shedding light on the importance of internal control quality in improving information quality in the French context. Moreover, this study is using a quantitative measure of the internal control quality while much of the prior literature uses material weaknesses to estimate the effectiveness of internal control system.

A Preliminary Study on Quantitative Quality Measurements of the Urethral Rhabdosphincter Muscle by Supersonic Shear Wave Imaging in Women With Stress Urinary Incontinence.

To quantitatively assess the quality of the urethral rhabdosphincter muscle by measuring its shear wave velocity (Vs ) and calculating the Young modulus (E) with supersonic shear wave imaging (SSI). This was a prospective study of 43 women with SUI and 52 female control participants who underwent a transperineal US examination with SSI. Supersonic shear wave imaging was performed at rest with a linear transducer and a specialized-preset procedure. The stability and validity of the shear waves were automatically assessed by the SSI procedure. The SSI images were visualized in a color-coded elastographic image. In the postprocessing analysis, the ventral part of the urethral rhabdosphincter muscle was manually outlined. The mean Vs and the mean E of the muscle were measured by the SSI procedure. The relationship between the mean Vs , mean E, and SUI was evaluated. The SSI examination was successfully performed in 40 patients with SUI (93.0%) and 40 female control participants (76.9%). No significant differences between the groups in age, body mass index, and parity were identified. For the SUI and control groups, the mean Vs values were 2.54 and 2.73 m/s, respectively, and the mean E values were was 19.7 and 22.7kPa. Significant correlations were found between SUI and the mean Vs as well as the mean E (Spearman correlation coefficients, -0.41 and -0.43; P < .05). The mechanical properties of the urethral sphincter can be quantitatively assessed by SSI. The stiffness of the urethral rhabdosphincter muscle was significantly lower in women with SUI.

A novel quantitative measure of image quality in fluoroscopy

Assessment of fluoroscopic image quality has not kept pace with technological developments in interventional imaging equipment. Access to ‘for presentation’ data on these systems has motivated this investigation into a novel quantitative method of measuring image quality. We have developed a statistical algorithm as an alternative to subjective assessment using threshold contrast detail detectability techniques. Using sets of uniformity exposed fluoroscopy frames, the algorithm estimates the minimum contrast necessary for conspicuity of a range of virtual target object areas A. Pixel mean value distributions in a central image region are Gaussian, with standard deviation σ Pixel binning produces background distributions with area A. For 95% confidence of conspicuity a target object must exhibit a minimum contrast of 3.29σ. A range of threshold contrasts are calculated for a range of virtual areas. Analysis on a few seconds of fluoroscopy data is performed remotely and no test object is required. In this study Threshold Index and Contrast Detail curves were calculated for different incident air kerma rates at the detector, different levels of electronic magnification and different types of image processing. A limited number of direct comparisons were made with subjective assessments using the Leeds TO.10 test object. Results obtained indicate that the statistical algorithm is not only more sensitive to changes in levels of detector dose rate and magnification, but also to levels of image processing, including edge-enhancement. Threshold Index curves thus produced could be used as an interventional system optimisation tool and to objectively compare image quality between vendor systems.

“An understanding, a way of life”: An exploration of learning disability professionals’ experiences of compassion

Accessible summary Compassion is described as a sensitivity to suffering in oneself and others, with an intent to alleviate and prevent it (Gilbert, Advances in Psychiatric Treatment, 2009, 15, 199). A population within health care that has limited research on compassionate care is that of learning disabilities. The current study aimed to explore the experiences of compassion of professionals within learning disability services. Attention was given to the flow of compassion between professionals and service users, as well as the growth of compassion, over time. The findings suggested that compassion is difficult to foster in learning disability services that also value time‐pressured, target‐driven outputs. More needs to be done to support services to encourage compassion in care, for example by considering candidates’ compassion at recruitment and through supportive supervision and group reflection, for both people with learning disabilities and the healthcare professionals employed. AbstractBackgroundCompassion is one of the six core principles that the NHS means to promote within health care. Research concerning compassion within learning disability services is scarce; therefore, the current study aimed to explore the experiences of compassion of professionals within learning disability services.Materials and MethodsTwelve professionals from learning disability services completed a quantitative measure of professional quality of life and answered a series of qualitative questions regarding their experiences of compassion, in either an online survey or interview.ResultsNarrative analysis was used to interpret participants’ experiences of compassion. Three chapters were generated based on thematic and linguistic connections. In chapter 1, participants explored their compassionate identities. In chapter 2, participants detailed the flow of compassion, experienced interpersonally and intrapersonally. In chapter 3, participants described their compassionate growth, owing to significant life events over time.ConclusionsRecommendations for improving compassionate care have been made, including encouraging self‐compassion for professionals through training screening for compassion during recruitment and including people with learning disabilities on interview panels.

Dimensional connectomics of anxious misery, a human connectome study related to human disease: Overview of protocol and data quality

Disparate diagnostic categories from the Diagnostic and Statistical Manual of Mental Disorders (DSM), including generalized anxiety disorder, major depressive disorder and post-traumatic stress disorder, share common behavioral and phenomenological dysfunctions. While high levels of comorbidity and common features across these disorders suggest shared mechanisms, past research in psychopathology has largely proceeded based on the syndromal taxonomy established by the DSM rather than on a biologically-informed framework of neural, cognitive and behavioral dysfunctions. In line with the National Institute of Mental Health’s Research Domain Criteria (RDoC) framework, we present a Human Connectome Study Related to Human Disease that is intentionally designed to generate and test novel, biologically-motivated dimensions of psychopathology. The Dimensional Connectomics of Anxious Misery study is collecting neuroimaging, cognitive and behavioral data from a heterogeneous population of adults with varying degrees of depression, anxiety and trauma, as well as a set of healthy comparators (to date, n = 97 and n = 24, respectively). This sample constitutes a dataset uniquely situated to elucidate relationships between brain circuitry and dysfunctions of the Negative Valence construct of the RDoC framework. We present a comprehensive overview of the eligibility criteria, clinical procedures and neuroimaging methods of our project. After describing our protocol, we present group-level activation maps from task fMRI data and independent components maps from resting state data. Finally, using quantitative measures of neuroimaging data quality, we demonstrate excellent data quality relative to a subset of the Human Connectome Project of Young Adults (n = 97), as well as comparable profiles of cortical thickness from T1-weighted imaging and generalized fractional anisotropy from diffusion weighted imaging. This manuscript presents results from the first 121 participants of our full target 250 participant dataset, timed with the release of this data to the National Institute of Mental Health Data Archive in fall 2020, with the remaining half of the dataset to be released in 2021.

A Survey and Quantitative Study on Map Inference Algorithms from GPS Trajectories

Map inference algorithm aims to construct a digital map from other data sources automatically. Due to the labour intensity of traditional map creation and the frequent road change nowadays, map inference is deemed to be a promising solution to automatic map construction and update. However, existing map inference from GPS trajectories suffers from low GPS data quality, which makes the quality of the constructed map unsatisfactory. In this paper, we study the existing map inference algorithms using GPS trajectories. Different from previous surveys, we (1) include the most recent solutions and propose a new categorisation of method; (2) study how different types of GPS errors affect the quality of inference results; (3) evaluate the existing map inference quality measures regarding their ability to identify map quality issues. To achieve these goals, we conduct a comprehensive experimental study on several representative algorithms using both real-world datasets and synthetic datasets, which are generated from our proposed synthetic trajectory generator and artificial map generator. Overall, our study provides insightful observations regarding (1) which inference method performs better in each working scenario, (2) the general data quality requirements for map inference, (3) the direction of future works for quantitative map quality measures.

A novel quantitative measure of image quality in fluoroscopy

The aim of this work is to develop a quantitative algorithm for the assessment of image quality in fluoroscopy as an alternative to the subjective Leeds Test Object TO-10. Current possible quantitative measurements such as Detective Quantum Efficiency and Modulation Transfer Function do not assess the effects of imaging processing, such as edge-enhancement and noise reduction, on the final displayed image. A standard statistical algorithm used to calculate the contrast needed to observe an object having area size (A) against background. The algorithm was developed to produce sets of Contrast-Detail and Threshold Contrast curves. Three flat panel fluoroscopy systems in our Cardiology were examined. Sequences of uniform fluoroscopy images, obtained using 1 mm of copper as an attenuator, were acquired and then analysed remotely. For each system curves were generated for (a) different dose rates at the detector, (b) different settings of magnification, & (c) different levels of edge-enhancement. For one system different levels of noise reduction were examined. Areas under contrast-detail and threshold-contrast curves reflect changes in dose rate at the detector. The algorithm is sensitive to changes in applied edge-enhancement and noise reduction. Both sets of curves for each system exhibit characteristic spatial frequency responses. This new efficient and objective algorithm measures fluoroscopic image quality using the standard Threshold Detection Index. It tracks quality changes that depend not only on input dose rate, but also the level of image processing applied. It only requires the acquisition of a few seconds of fluoroscopy to produce images for remote analysis.

Size Projection Algorithm: Optimal Thresholding Value Selection for Image Segmentation of Electrical Impedance Tomography

Thresholding is an efficient step to extract quantitative information since the potential artefacts are often introduced by the point-spread effect of tomographic imaging. The thresholding value was previously selected only relying on engineering experience or histogram of tomographic image, which often presents a great challenge to determine an accurate thresholding value for various applications. As the tomographic image features often do not provide sufficient information to choose the best thresholding value, the information implicit in the measured boundary data is introduced into the thresholding process in this paper. A projection error, the relative difference between the computed boundary data of current segmentation and the measured boundary data, is proposed as a quantitative measure of such image segmentation quality. Then, a new multistep image segmentation process, called size projection algorithm (SPA), is proposed to automatically determine an optimal thresholding value by minimising the projection error. Results of simulation and experiment demonstrate the improved performance of the SPA-based tomographic image segmentation. An application of size projection algorithm for gas-water two-phase flow visualisation is also reported in this paper.

Corrigendum to: The Therapeutic Environment Screening Survey for Nursing Homes (TESS-NH): An Observational Instrument for Assessing the Physical Environment of Institutional Settings for Persons With Dementia.

Objective. To develop an observational instrument that describes the ability of physical environments of institutional settings to address therapeutic goals for persons with dementia. Methods. A National Institute on Aging workgroup identified and subsequently revised items that evaluated exit control, maintenance, cleanliness, safety, orientation/cueing, privacy, unit autonomy, outdoor access, lighting, noise, visual/tactile stimulation, space/seating, and familiarity/homelikeness. The final instrument contains 84 discrete items and one global rating. A summary scale, the Special Care Unit Environmental Quality Scale (SCUEQS), consists of 18 items. Lighting items were validated using portable light meters. Concurrent criterion validation compared SCUEQS scores with the Professional Environmental Assessment Protocol (PEAP). Results. Interrater kappa statistics for 74% of items were above .60. For another 10% of items, kappas could not be calculated due to empty cells, but interrater agreement was above 80%. The SCUEQS demonstrated an interrater reliability of .93, a test-retest reliability of .88, and an internal consistency of .81-.83. Light meter ratings correlated significantly with the Therapeutic Environment Screening Survey for Nursing Homes (TESS-NH) lighting items (r = .29-.38, p = .01-.04), and the SCUEQS correlated significantly with global PEAP ratings (r = .52, p <.01). Discussion. The TESS-NH efficiently assesses discrete elements of the physical environment and has strong reliability and validity. The SCUEQS provides a quantitative measure of environmental quality in institutional settings.

Correlation of Algorithmic and Visual Assessment of Lesion Detection in Clinical Images

Clinically-relevant quantitative measures of task-based image quality play key roles in effective optimization of medical imaging systems. Conventional phantom-based measures do not adequately reflect the real-world image quality of clinical Computed Tomography (CT) series which is most relevant for diagnostic decision-making. The assessment of detectability index which incorporates measurements of essential image quality metrics on patient CT images can overcome this limitation. Our current investigation extends and validates the technique on standard-of-care clinical cases. We obtained a clinical CT image dataset from an Institutional Review Board-approved prospective study on colorectal adenocarcinoma patients for detecting hepatic metastasis. For this study, both perceptual image quality and lesion detection performance of same-patient CT image series with standard and low dose acquisitions in the same breath hold and four processing algorithms applied to each acquisition were assessed and ranked by expert radiologists. The clinical CT image dataset was processed using the previously validated method to estimate a detectability index for each known lesion size in the size distribution of hepatic lesions relevant for the imaging task and for each slice of a CT series. We then combined these lesion-size-specific and slice-specific detectability indexes with the size distribution of hepatic lesions relevant for the imaging task to compute an effective detectability index for a clinical CT imaging condition of a patient. The assessed effective detectability indexes were used to rank task-based image quality of different imaging conditions on the same patient for all patients. We compared the assessments to those by expert radiologists in the prospective study in terms of rank order agreement between the rankings of algorithmic and visual assessment of lesion detection and perceptual quality. Our investigation indicated that algorithmic assessment of lesion detection and perceptual quality can predict observer assessment for detecting hepatic metastasis. The algorithmic and visual assessment of lesion detection and perceptual quality are strongly correlated using both the Kendall's Tau and Spearman's Rho methods (perfect agreement has value 1): for assessment of lesion detection, 95% of the patients have rank correlation coefficients values exceeding 0.87 and 0.94, respectively, and for assessment of perceptual quality, 0.85 and 0.94, respectively. This study used algorithmic detectability index to assess task-based image equality for detecting hepatic lesions and validated it against observer rankings on standard-of-care clinical CT cases. Our study indicates that detectability index provides a robust reflection of overall image quality for detecting hepatic lesions under clinical CT imaging conditions. This demonstrates the concept of utilizing the measure to quantitatively assess the quality of the information content that different imaging conditions can provide for the same clinical imaging task, which enables targeted optimization of clinical CT systems to minimize clinical and patient risks.

Noise Removal of Tracheal Sound Recorded During CPET to Determine Respiratory Rate.

This study aimed to extract respiratory signal from tracheal sound recordings during cardio-pulmonary exercise test (CPET), which is the state-of-the-art diagnosis of cardiovascular and respiratory diseases. However, the availability of CPET is limited due to its expense. Physiological signal analysis is a convenient alternative to measure clinical parameters. We collected data from 30 healthy adults and applied wavelet transform thresholding (WTT), empirical mode decomposition (EMD), and Synchrosqueezing transform filtering (SST) to de-noise the raw data. Signal to noise ratio (SNR) was calculated as a quantitative measure of signal quality. We observed that SST yielded the highest SNR and introduced lowest signal distortion by visual-auditory inspection. Respiratory rate was then determined using the signal extracted by SST. Discrepancy of respiratory rate determined from the signal and the gold standard CPET was within 1.2 breaths per minute. In conclusion, SST effectively removed noises in tracheal sound recorded during CPET and provided analyzable respiratory signal for clinical parameter determination.

Assessment of PET and SPECT phantom image quality through automated binary classification of cold rod arrays.

Evaluation of positron emission tomography (PET) or single photon emission computed tomography (SPECT) performance often involves qualitative assessment of phantom images, namely the visibility of hot or cold structures in a warm background. Structure-to-background contrast is a quantitative measure of scanner performance; however, contrast measurements do not account for image noise and its effect on task performance. Although task-based performance could be evaluated over an ensemble of phantom scans, a more practical approach is desired. Repeated structures, such as the cold rod arrays of the American College of Radiography (ACR) PET and SPECT phantoms, offer an opportunity to evaluate image quality based on statistical decision theory. Images are co-registered to a digital template for placement of numerous regions of interest (ROIs) in multiple thick slices centered on each cold rod and on each midpoint between rods. The assumption is made that each ROI corresponds to a statistically independent measurement, known to be of either a cold rod or the background. A receiver operating characteristic (ROC) curve then is generated for each cold rod sector of the phantom. The area under the ROC curve (AUC) provides a quantitative measure of cold rod visibility. In addition, signal-to-noise ratio (SNR) is calculated, under the assumption that the rod and background ROI measurements are distributed normally. Using this approach, data from PET and SPECT phantom studies from prior annual physics surveys were analyzed retrospectively. Rod and background ROI measurements had nearly normal (PET) or approximately normal (SPECT) distributions. Resultant ROC curves illustrated the varying degrees of overlap between rod and background histograms vs cold rod diameter. Both AUC and SNR correlated well with visual image assessment and had high consistency between phantom studies, demonstrating the quantitative accuracy of the automated analysis. AUC reliably quantified cases where cold rods were partially or mostly resolved, while SNR provided further characterization when cold rods were completely resolved. In comparison, contrast measurements often mirrored AUC and SNR but were inconsistent in cases of varying noise. Automated analysis of phantom cold rod arrays using binary classification provides informative quantitative measures of image quality and is practical for routine use. This approach avoids observer dependencies associated with visual assessment and potentially can benefit quality assurance, physics surveys, laboratory accreditation, and harmonization of multicenter image quality.

Exploring elderly patients' experiences of recovery following complex proximal humerus fracture: A qualitative study.

This study explores the experiences and perceptions of recovery in elderly patients who had sustained a proximal humerus fracture. In-depth semi-structured interviews were conducted with 15 patients over the age of 65. Thematic analysis identified aspects of care that impacted upon patient experience and quality of life. Seven main patient-reported themes were identified, including pain, sleep, shoulder function, emotional state, social support, relationship with their professional and experience of healthcare institution. These themes offer insight into the experiences of adults receiving care for proximal humerus fracture and highlight that existing quantitative measures of quality of life do not measure domains that are important to patients.

Bi-dimensional Empirical Mode Decomposition Based Contrast Enhancement Technique for UAV Thermal Images

In this paper, a new contrast enhancement technique based on bi-dimensional empirical mode decomposition (BEMD) is proposed to enhance the target details in low contrast thermal images captured by unmanned aerial vehicle (UAV). Initially, thermal image acquired from UAV is separated into intrinsic mode functions (IMFs) and residue using BEMD. Then, local standard deviation based method is employed to the IMFs to improve the details of the image and histogram modification function is employed to the residue to enhance the contrast. The processed IMFs and residue are reconstructed to obtain the enhanced image using inverse BEMD. The performance of the proposed technique is analysed using quantitative and visual quality measures with the well-known histogram equalization, dynamic range partitioning and wavelet-based enhancement methods. Experimental results demonstrate that the proposed technique effectively enhances the contrast and details in the image with less noise and visual artefacts than other methods.