Digital Image Quality Research Articles

Defining of the Minimum Wavelet Filter Coefficients Bit-Width for 3D Medical Imaging

Medical imaging uses a variety of modalities to provide visual information about a patient. Various methods are used to process this data. Many of them are based on discrete wavelet transform (DWT). Its use will allow effective denoising and compression of 2D and 3D images. This paper proposes a new approach to linear time-invariant wavelet filtering using quantized filter coefficients when using which the computational errors have different signs and allow to partially compensate each other as a result of which the processed image is of high quality. The analysis of the quantization noise of the direct multilevel DWT filter coefficients is carried out. The derived formulas demonstrate the relationship between the quantization accuracy of these coefficients and the processing quality of digital 3D images. The derived formulas for calculating the minimum accuracy of the wavelet filter coefficients representation in the computing devices memory allow minimizing the effect of quantization noise on the result of 3D images processing. Modelling of 3D medical tomographic images DWT processing showed that a decrease in the ratio of the average voxel brightness to the maximum allowable value with increasing color depth of images leads to faster achievement of high quality compared to the results of theoretical analysis with an increase in the value of the scaling degree of the wavelet filter coefficients. The obtained theoretical and practical results open up the possibility for reducing the computational complexity of software and hardware implementation of wavelet processing of 3D medical visual data on modern microelectronic devices (field-programmable gate arrays, application-specific integrated circuits, etc.).

No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features.

The goal of no-reference image quality assessment (NR-IQA) is to evaluate their perceptual quality of digital images without using the distortion-free, pristine counterparts. NR-IQA is an important part of multimedia signal processing since digital images can undergo a wide variety of distortions during storage, compression, and transmission. In this paper, we propose a novel architecture that extracts deep features from the input image at multiple scales to improve the effectiveness of feature extraction for NR-IQA using convolutional neural networks. Specifically, the proposed method extracts deep activations for local patches at multiple scales and maps them onto perceptual quality scores with the help of trained Gaussian process regressors. Extensive experiments demonstrate that the introduced algorithm performs favorably against the state-of-the-art methods on three large benchmark datasets with authentic distortions (LIVE In the Wild, KonIQ-10k, and SPAQ).

Meta-analysis of distortions effects on quality of digital images using standard IQA datasets

The research on image quality assessment has been an area of concern for the researchers from last many years. Images, during their life cycle, passes through several phases including acquisition, compression and restoration, for ease of transmission and storage, encryption and decryption for better security. During their processing, the quality of images is affected by many distortions, including compression artifacts, noise, bandwidth loss, etc., that are not present in the original images. The main emphasis of this research work is to analyze the effect of such artifacts on the quality of images. Further, a brief description of available image quality datasets, along with the popular quality evaluation metrics, is also presented. From experimental analysis in the present work, it is observed that when the images are degraded by impurities during their processing phases, then their quality degenerates by a relatively high degree, varying from minimum 1% to maximum of 55% under different sets of distortions, when measured on standard IQA metrics such as MSE, PSNR, PFE, SSIM, etc. This study will motivate the future researchers to have an elaborate quality assessment before employing these images for practical applications like medical sciences, remote sensing, etc.

Human emotion recognition based on facial expressions via deep learning on high-resolution images

Detecting human emotion based on facial expression is considered a hard task for the computer vision community because of many challenges such as the difference of face shape from a person to another, difficulty of recognition of dynamic facial features, low quality of digital images, etc. In this paper, we propose a face-sensitive convolutional neural network (FS-CNN) for human emotion recognition. The proposed FS-CNN is used to detect faces on large scale images then analyzing face landmarks to predict expressions for emotion recognition. The FS-CNN is composed form two stages, patch cropping, and convolutional neural networks. The first stage is used to detect faces in high-resolution images and crop the face for further processing. The second stage is a convolutional neural network used to predict facial expression based on landmarks analytics, it was applied on pyramid images to process scale invariance. The proposed FS-CNN was trained and evaluated on the UMD Faces dataset. High performance was achieved with a mean average precision of about 95%.

Independent evaluation and comparison of digital radiography image quality in nine major imaging centers affiliated to Shiraz University of Medical Sciences

Independent evaluation and comparison of digital radiography image quality in nine major imaging centers affiliated to Shiraz University of Medical Sciences

Chinese experts' consensus on digital pathological image acquisition and quality control of cervical liquid based cytology

Chinese experts' consensus on digital pathological image acquisition and quality control of cervical liquid based cytology

Quality Control of Digital Animation Image in the Era of Interactive Media

As the scope of interactive media applications continues to expand, people's exploration of animation technology continues to deepen, and digital animation is a perfect combination of technology and art. Digital animation in the era of interactive media is an animation technology that uses corresponding control commands or functions to achieve interactive feedback actions, animation input and output, and two-way feedback from audiences during the gradual improvement of animation works. More and more viewers and investors are beginning to pay attention to the creation and development of digital animation images. Therefore, the production of high-quality and high-level digital animation images has become an urgent need for the market and audiences. This research analysed the production elements of digital animation image quality control in the era of interactive media after analysing the process flow and production technology of digital animation in the era of interactive media, and analysed the interaction between light and shadow, sound, audience and objects in animation, Characters and the expressions of the audience's eyes are used to study the quality control of digital animation images in the era of interactive media. After analysing the relevant factors that affect the creation quality and artistic level of digital animation images, we believe that only by ensuring that the film strives for excellence in all production links can we finally create excellent digital animation images.

Independent evaluation and comparison of digital radiography image quality in nine major imaging centers affiliated to Shiraz University of Medical Sciences

Independent evaluation and comparison of digital radiography image quality in nine major imaging centers affiliated to Shiraz University of Medical Sciences

An Improved SPSIM Index for Image Quality Assessment

Objective image quality assessment (IQA) measures are playing an increasingly important role in the evaluation of digital image quality. New IQA indices are expected to be strongly correlated with subjective observer evaluations expressed by Mean Opinion Score (MOS) or Difference Mean Opinion Score (DMOS). One such recently proposed index is the SuperPixel-based SIMilarity (SPSIM) index, which uses superpixel patches instead of a rectangular pixel grid. The authors of this paper have proposed three modifications to the SPSIM index. For this purpose, the color space used by SPSIM was changed and the way SPSIM determines similarity maps was modified using methods derived from an algorithm for computing the Mean Deviation Similarity Index (MDSI). The third modification was a combination of the first two. These three new quality indices were used in the assessment process. The experimental results obtained for many color images from five image databases demonstrated the advantages of the proposed SPSIM modifications.

No-Reference Image Quality Assessment with Global Statistical Features.

The perceptual quality of digital images is often deteriorated during storage, compression, and transmission. The most reliable way of assessing image quality is to ask people to provide their opinions on a number of test images. However, this is an expensive and time-consuming process which cannot be applied in real-time systems. In this study, a novel no-reference image quality assessment method is proposed. The introduced method uses a set of novel quality-aware features which globally characterizes the statistics of a given test image, such as extended local fractal dimension distribution feature, extended first digit distribution features using different domains, Bilaplacian features, image moments, and a wide variety of perceptual features. Experimental results are demonstrated on five publicly available benchmark image quality assessment databases: CSIQ, MDID, KADID-10k, LIVE In the Wild, and KonIQ-10k.

PIQI: perceptual image quality index based on ensemble of Gaussian process regression

Digital images contain a lot of redundancies, therefore, compression techniques are applied to reduce the image size without loss of reasonable image quality. Same become more prominent in the case of videos which contains image sequences and higher compression ratios are achieved in low throughput networks. Assessment of quality of images in such scenarios has become of particular interest. Subjective evaluation in most of the scenarios is infeasible so objective evaluation is preferred. Among the three objective quality measures, full-reference and reduced-reference methods require an original image in some form to calculate the image quality which is unfeasible in scenarios such as broadcasting, acquisition or enhancement. Therefore, a no-reference Perceptual Image Quality Index (PIQI) is proposed in this paper to assess the quality of digital images which calculates luminance and gradient statistics along with mean subtracted contrast normalized products in multiple scales and color spaces. These extracted features are provided to a stacked ensemble of Gaussian Process Regression (GPR) to perform the perceptual quality evaluation. The performance of the PIQI is checked on six benchmark databases and compared with twelve state-of-the-art methods and competitive results are achieved. The comparison is made based on RMSE, Pearson and Spearman’s correlation coefficients between ground truth and predicted quality scores. The scores of 0.0552, 0.9802 and 0.9776 are achieved respectively for these metrics on CSIQ database. Two cross-dataset evaluation experiments are performed to check the generalization of PIQI.

Faktor Eksposi terhadap Kualitas Citra Radiografi dan Dosis Pasien Menggunakan Parameter Penilaian Signal to Noise Ratio (SNR) pada Pemeriksaan Thorax Posteroanterior dengan Menggunakan Pesawat Computed Radiografi

Background: The research was analyzing digital image quality and estimation dose patient by using Signal to Noise Ratio (SNR) on Computed Radiography. SNR can be used for analyzing digital image spatial resolution and estimation dose accurately. The aims of this study to determine the influence of exposure factors on image quality and estimation dose patient.Methods: This type of research is a quantitative method with an experimental study. Direct experiments in August 2020 assessment with a sample of 9 adults posteroanterior chest photo with the average age of 20-50 years old with an average body weight of 50-69 kilograms. Results: The measurement results showed that the digital images will be analyzed by SNR, so it can be determined the optimum exposed factor of the highest SNR value and dose radiation. From the analysis, the highest SNR value at 121 kV, current 1 mAs, the lower dose radiation at 121 kV, current 0,9 mAs.Conclusions: There was an influence variation of an exposed factor on the quality of the image and dose to the patient.

Исследование точности определения геометрии железнодорожного пути по цифровым снимкам

Digital infrastructure as an element of the «Digital Railway» of JSC «Russian Railways» involves the provision of objective, complete and up-to-date information about infrastructure facilities in digital form to solve a wide range of tasks - from engineering surveys carried out as part of the construction, repair and reconstruction of railway infrastructure facilities to the operation of autonomous (unmanned) rolling stock. One of the methods of obtaining such information is digital photogrammetry. At the same time, the highest measurement accuracy is required when determining the geometry of the railway track. When determining the geometry of the railway track with high accuracy through digital images, it becomes necessary to take into account not only the parameters of aerial photography and the quality of digital images, but also the features of image processing programs. The article presents the results of experimental studies of the accuracy of determining the geometry of the railway track using digital images as a result of processing them on the digital photogrammetric systems (DFS) PHOTOMOD and Agisoft Metashape (PhotoScan). The technical characteristics (CFS) of PHOTOMOD and Agisoft Metashape (PhotoScan) are given, the goals, objectives and content of the experimental studies performed are considered, the features of the experimental object, the characteristics of the aerial survey equipment and the methods of control determinations are described.

Implementation of Collodion Bag Protocol to Improve Whole-slide Imaging of Scant Gynecologic Curettage Specimens

Background: Digital pathology has been increasingly implemented for primary surgical pathology diagnosis. In our institution, digital pathology was recently deployed in the gynecologic (GYN) pathology practice. A notable challenge encountered in the digital evaluation of GYN specimens was high rates of scanning failure of specimens with fragmented as well as scant tissue. To improve tissue detection failure rates, we implemented a novel use of the collodion bag cell block preparation method. Materials and Methods: In this study, we reviewed 108 endocervical curettage (ECC) specimens, representing specimens processed with and without the collodion bag cell block method (n = 56 without collodion bag, n = 52 with collodion bag). Results: Tissue detection failure rates were reduced from 77% (43/56) in noncollodion bag cases to 23/52 (44%) of collodion bag cases, representing a 42% reduction. The median total area of tissue detection failure per level was 0.35 mm2 (interquartile range [IQR]: 0.14, 0.70 mm2) for noncollodion bag cases and 0.08 mm2 (IQR: 0.03, 0.20 mm2) for collodion bag cases. This represents a greater than fourfold reduction in the total area of tissue detection failure per level (P < 0.001). In addition, there were no out-of-focus levels among collodion bag cases, compared to 6/56 (11%) of noncollodion bag cases (median total area = 4.9 mm2). Conclusions: The collodion bag method significantly improved the digital image quality of fragmented/scant GYN curettage specimens, increased efficiency and accuracy of diagnostic evaluation, and enhanced identification of tissue contamination during processing. The logistical challenges and labor cost of deploying the collodion bag protocol are important considerations for feasibility assessment at an institutional level.

Orthogonal projections for image quality analyses applied to MRI

AbstractMeasuring digital image quality is of importance in numerous applied research fields, such as image acquisition, image processing or image reconstruction. Subjective evaluation, i.e. assessment by human beings, is often too time‐consuming, expensive and inconsistent in complicated tasks such as medical imaging. In order to better understand the relation between objective image quality assessment measures, we compare a selection in an experimental setup. It is well known that L2 error estimation, i.e. computing the mean squared error (MSE) between two images, does not correspond well to the perceptual evaluation by the human visual system. Here, we illustrate on the example of MRI data the interaction of the MSE with the signal to noise ratio (SNR), the variance and subjective evaluation. We use random coverings of the space of orthogonal projections for coil combination to enable a comprehensive correlation analysis.

Improved digital chest tomosynthesis image quality by use of a projection-based dual-energy virtual monochromatic convolutional neural network with super resolution.

We developed a novel dual-energy (DE) virtual monochromatic (VM) very-deep super-resolution (VDSR) method with an unsharp masking reconstruction algorithm (DE-VM-VDSR) that uses projection data to improve the nodule contrast and reduce ripple artifacts during chest digital tomosynthesis (DT). For estimating the residual errors from high-resolution and multiscale VM images from the projection space, the DE-VM-VDSR algorithm employs a training network (mini-batch stochastic gradient-descent algorithm with momentum) and a hybrid super-resolution (SR) image [simultaneous algebraic reconstruction technique (SART) total-variation (TV) first-iterative shrinkage-thresholding algorithm (FISTA); SART-TV-FISTA] that involves subjective reconstruction with bilateral filtering (BF) [DE-VM-VDSR with BF]. DE-DT imaging was accomplished by pulsed X-ray exposures rapidly switched between low (60 kV, 37 projection) and high (120 kV, 37 projection) tube-potential kVp by employing a 40° swing angle. This was followed by comparison of images obtained employing the conventional polychromatic filtered backprojection (FBP), SART, SART-TV-FISTA, and DE-VM-SART-TV-FISTA algorithms. The improvements in contrast, ripple artifacts, and resolution were compared using the signal-difference-to-noise ratio (SDNR), Gumbel distribution of the largest variations, radial modulation transfer function (radial MTF) for a chest phantom with simulated ground-glass opacity (GGO) nodules, and noise power spectrum (NPS) for uniform water phantom. The novel DE-VM-VDSR with BF improved the overall performance in terms of SDNR (DE-VM-VDSR with BF: 0.1603, without BF: 0.1517; FBP: 0.0521; SART: 0.0645; SART-TV-FISTA: 0.0984; and DE-VM-SART-TV-FISTA: 0.1004), obtained a Gumbel distribution that yielded good images showing the type of simulated GGO nodules used in the chest phantom, and reduced the ripple artifacts. The NPS of DE-VM-VDSR with BF showed the lowest noise characteristics in the high-frequency region (~0.8 cycles/mm). The DE-VM-VDSR without BF yielded an improved resolution relative to that of the conventional reconstruction algorithms for radial MTF analysis (0.2-0.3 cycles/mm). Finally, based on the overall image quality, DE-VM-VDSR with BF improved the contrast and reduced the high-frequency ripple artifacts and noise.

Retinal Digital Image Quality Improvement as A Diabetes Retinopatic Disease Detection Effort

Image processing is a technical term useful for modifying images in various ways. In medicine, image processing has a vital role. One example of images in the medical world, namely retinal images, can be obtained from a fundus camera. The retina image is useful in the detection of diabetic retinopathy. In general, direct observation of diabetic retinopathy is conducted by a doctor on the retinal image. The weakness of this method is the slow handling of the disease. For this reason, a computer system is required to help doctors detect diabetes retinopathy quickly and accurately. This system involves a series of digital image processing techniques that can process retinal images into good quality images. In this research, a method to improve the quality of retinal images was designed by comparing the methods for adjusting histogram equalization, contrast stretching, and increasing brightness. The performance of the three methods was evaluated using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Signal to Noise Ratio (SNR). Low MSE values and high PSNR and SNR values indicated that the image had good quality. The results of the study revealed that the image was the best to use, as evidenced by the lowest MSE values and the highest SNR and PSNR values compared to other techniques. It indicated that adaptive histogram equalization techniques could improve image quality while maintaining its information.

Effect of target/filter combination on the mean glandular dose and contrast-detail threshold: A phantom study

IntroductionIn mammography it is essential to combine aspects of image quality with compliance with glandular dose levels. The objective of this study was to combine the assessment of the contrast-detail threshold and the mean glandular dose for different target/filter combinations in digital mammography and to indicate the optimal combination through a figure of merit (FOM). MethodsIn a direct digital mammograph, performance and image quality standards were evaluated using a solid-state multidetector capable of measuring various parameters including kerma and standardized phantoms for image quality. After verifying the proper functioning of the mammograph, the contrast-detail detection threshold and the mean glandular dose values for thicknesses ranging from 20 mm to 70 mm in polymethylmethacrylate (PMMA) were evaluated. Combining these values, an FOM was defined to determine the optimal combination. ResultsThe results indicated that the Mo/Rh and W/Rh combinations present mean glandular doses below the limit for all thicknesses. In contrast, using the Mo/Mo combination, the mean glandular doses were below the limit only up to 45 mm. However, when considering the FOM that combines dosimetric aspects and the contrast-detail threshold, the use of W/Rh is the most optimal option. ConclusionThe results obtained in this study demonstrate that the W/Rh combination in direct digital mammography is the option that presents the best trade-off between the mean glandular dose and the contrast-detail detection threshold. Implications for practiceThe results demonstrate that the application of the W/Rh combination in direct digital mammography offers an optimized and applicable option, regardless of the thickness of the breast that will be radiographed.

A Novel Gray Image Enhancement Using the Regional Similarity Transformation Function and Dragonfly Algorithm

Image enhancement is a necessary and indispensable technique for increasing the quality of digital images. The main task is to generate a new intensity value for each pixel in the image using a transformation function after the input image receives the intensity value of each pixel. The proposed transfer function in this study is called the Regional Similarity Transfer Function (RSTF) that considers the density distribution similarity between adjoining pixels. Dragonfly Algorithm (DA) intuitive optimization technique, which is preferred in engineering applications, has been used to optimize the parameter values of the proposed transfer function. Image quality evaluation is performed with six criteria between the improved and original images. Our experimental results show that the intensity distribution between adjoining pixels show an increase in contrast and brightness over the similarity degree. Excessive brightness, blur, and deterioration in the images is resolved with the proposed method.

Preprocessing method of microstructure image of geotechnical materials

Abstract The engineering properties of geotechnical materials are primarily controlled by the system state and overall behavior of their internal microstructures, and the complex physical and mechanical properties are the concentrated reflections of their microstructural characteristics. Therefore, it is necessary to study the microstructures of geotechnical materials. The development level of microstructural research is closely related to observation technology. In order to reveal the intrinsic relationship between the engineering properties of geotechnical materials and microstructures, the observation technology of microstructures must be continuously updated. At present, most of the test methods cannot continuously observe the changes of microstructures of geotechnical materials under loading, and cannot obtain the actual deformation and displacement information of particles and pore. Therefore, it is difficult to establish the soil stress model and constitutive relationship on the basis of the actual evolution of microstructures, which restricts the further development of relevant theories. In order to overcome the above shortcomings, in order to improve the accuracy and completeness of extracting the characteristic microstructural parameters of geotechnical materials, the clear parts of the same image sequence are extracted and reconstructed, and then high-definition images are obtained. Subsequently, the applicability of two commonly used threshold segmentation methods in the pretreatment of the micro-structure image of geotechnical materials is compared and analyzed. The results show that the multi-resolution technology of wavelet transform can effectively increase the signal-to-noise ratio, reduce gray variance and optimize the quality of the micro-structure digital image. The average gray level method and the maximum variance automatic threshold method are used to process the microstructure image of geotechnical materials, which can effectively segment the image and ensure the integrity of feature parameters extraction.