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In this paper, we propose a novel first-order variational model for image restoration. The main feature of this model lies in the fact that it helps preserve image contrasts during the image restoration process. To achieve this, we design a new regularizer that presents a lower growth rate than any power function with a positive exponent for large image gradient. Augmented Lagrangian method is employed to minimize this variational model and convergence analysis is established for the proposed algorithm. Numerical experiments are presented to demonstrate the features of the proposed model and also show the efficiency of the proposed numerical method. [ABSTRACT FROM AUTHOR]

The image contrast is a feature capturing the variation of the image signal across the space. Such a feature is very useful to describe the local image structure at different scales and thus it is relevant to many computer vision applications, like image/texture retrieval and object recognition. In this work, we present MiRCo, a novel measure of image contrast derived from the Retinex theory. MiRCo is robust against in-plane rotations and light changes at multiple scales. Thanks to these properties, MiRCo enables an accurate and robust description of the local image structure. Here we describe and discuss the mathematical insights of MiRCo also in comparison with other popular contrast measures. [ABSTRACT FROM AUTHOR]

This study proposes a novel method for dog identification using nose-print recognition, with applications in controlling stray dogs, locating lost pets, and pet insurance verification. Nose prints offer a unique and safer means of recognition than implanted chips. Accurate positioning of the dog's nose print (DNP) region and comparing its features can enhance recognition accuracy. The two-stage segmentation method effectively segments dog nostrils and nose boundaries in the DNP image. It combines brightness and texture enhancement with the U-Net model to generate a dog nose-print mask (DNPMask) and extracts scale-like features using a genetic algorithm. Experiments comparing one-stage and two-stage segmentation methods demonstrate the latter's superiority, with higher recall, precision, accuracy, and F1 score values. The DNPMask method achieves an average recognition rate of 94.93%, 97.10%, and 97.10% for Top1, Top2, and Top3, respectively, significantly improving dog recognition accuracy. However, its performance may be affected by poor-quality images. Nevertheless, the proposed dog recognition method effectively identifies most dog identities and holds promise for real-life applications, enhancing overall accuracy. The research findings provide a valuable reference for developing dog recognition systems and offer new ideas and directions for related research fields. The improved performance of dog nose-print recognition based on scale-like shape and spatial features (DNPISSFS) supports its superiority over Texture Feature-based Dog Noseprint Image Matching (TFDNPIM), suggesting enhanced dog recognition capabilities with broader implications in animal tracking, pet management, and wildlife conservation. Further analysis and experimentation on a larger dataset will ascertain the method's generalizability and robustness, addressing potential challenges and limitations for future research and practical implementation in various domains. The source code and trained models are publicly available at: https://github.com/Chuen-HorngLin/Texture-Feature-based-Dog-Noseprint-Image-Matching. [ABSTRACT FROM AUTHOR]

In order to detect the deformation and strain of materials accurately, the key is to obtain the phase information caused by dynamic loading in digital speckle pattern interferometry (DSPI). In this paper, the evaluation method of quality of the speckle pattern in DSPI system is proposed, and the influence of the size of speckle grain on the stability and contrast of speckle pattern is discussed. And then, the strain detection experiments of inactive and bioactive materials are provided with different aperture slit size under the same detection conditions. The size of speckle grain has an important influence on the quality of speckle pattern. For strain detection of inactive materials, using the small size of speckles can obtain higher quality speckle pattern under the condition of satisfying the Nyquist theorem and spectral separation. For active biomaterials, non-structural factors easily induce the instability of speckle pattern, which leads to the de-correlation of between pre-deformation and post-deformation speckle pattern. So the compromise between the stability and the information capacity of speckle images should be considered in the selection of speckle size. Experiments show that the optimum size of speckles used for strain detection active biomaterials is larger than that of inactive biomaterials under the same conditions in the same DSPI system.

Subject of Research. We study the approach to the underlying surface interpretation automation for satellite images obtained by the onboard optical-electronic equipment of the Earth remote sensing systems. The topicality of research is determined by the necessity of introduction of computer vision methods aimed at solving the search problem of the earth’s surface state changes through multi-temporal satellite monitoring data. The goal of research is reducing of the time spent on processing of large area satellite images. Method. The method is based on the idea of comparing the contrast of different-time satellite images. For method implementation, a mathematical apparatus is formed for calculating the contrast values of the analyzed images in the normalized interval from 0 to 1. The effectiveness of automated processing of satellite images is ensured by their pre- segmentation and zoning. Segmentation parameters are selected taking into account the size of the objects to be detected. The efficiency of the proposed method is confirmed by the high correlation of the automated processing results with the results of visual analysis of test satellite images. Main Results. The results of calculating the contrast of test images using the formulated mathematical apparatus are presented. The necessity of image segmentation is proved to solve the problem of detecting changes in the terrain on the example of processing images consisting of different number of fragments. An approach is developed for reducing the redundancy of data on terrain changes by performing a preliminary zoning procedure. The essence of this procedure is to determine the researched area boundaries in order to limit the zones for search of changes. Practical Relevance. The proposed method of data processing on the Earth remote sensing provides interpretation of the underlying surface images in an automated mode without operator participation. At that, the interpretation of images, when observing large areas, can be accelerated.

The traditional image quality assessments, such as the mean squared error (MSE), the signal-to-noise ratio (SNR), and the Peak signal-to-noise ratio (PSNR), are all based on the absolute error of images. Structural similarity (SSIM) index is another important image quality assessment which has been shown to be more effective in the human vision system (HVS). Although there are many essential differences between MSE and SSIM, some important associations exist between them. In this paper, the associations between MSE and SSIM as cost functions in linear decomposition are investigated. Based on the associations, a bit-allocation algorithm for sparse coding is proposed by considering both the reconstructed image quality and the reconstructed image contrast. In the proposed algorithm, the space occupied by a linear coefficient of a basis in sparse coding is reduced to only 9 to 10 bits, in which 1 bit is used to save the sign of linear coefficient, 3 bits are used to save the number of powers of 10 in scientific notation, and only 5 to 6 bits are used to save the significance digits. The experimental results show that the proposed bit-allocation algorithm for sparse coding can maintain both the image quality and the image contrast well. [ABSTRACT FROM AUTHOR]

Brain tumour identification with traditional magnetic resonance imaging (MRI) tends to be time-consuming and in most cases, reading of the resulting images by human agents is prone to error, making it desirable to use automated image segmentation. This is a multi-step process involving: (a) collecting data in the form of raw processed or raw images, (b) removing bias by using pre-processing, (c) processing the image and locating the brain tumour, and (d) showing the tumour affected areas on a computer screen or projector. Several systems have been proposed for medical image segmentation but have not been evaluated in the field. This may be due to ongoing issues of image clarity, grey and white matter present in a scan image, lack of knowledge of the end user and constraints arising from MRI imaging systems. This makes it imperative to develop a comprehensive technique for the accurate diagnosis of brain tumors in MRI images. In this paper, we introduce a taxonomy consisting of 'Data, Image segmentation processing, and View' (DIV) which are the major components required to develop a high-end system for brain tumour diagnosis based on deep learning neural networks. The DIV taxonomy is evaluated based on system completeness and acceptance. The utility of the DIV taxonomy is demonstrated by classifying 30 state-of-the-art publications in the domain of medFical image segmentation systems based on deep neural networks. The results demonstrate that few components of medical image segmentation systems have been validated although several have been evaluated by identifying role and efficiency of the components in this domain. [ABSTRACT FROM AUTHOR]

Image regularised features play a critical role in image processing domain, by integrating regularised feature and structural similarity, a new full‐reference image assessment method (IRF_SSIM) is proposed in this study. As well known, the gradient operator always be used to capture the edge information of the image, while the total variational regularised features can be adopted to calculate the detailed change information of image contrast and texture, as well as noise removal and edge retention. Therefore, the IRF_SSIM method extends the gradient features into the image regularised features to measure the structural changes in the image. In addition, image quality is also affected by variations of luminance and contrast. For a more comprehensive image quality assessment, the IRF_SSIM method considers the changes in structure, luminance and contrast simultaneously. In other words, the total image quality is estimated by structural similarity calculated by integrating the effects of image structure, luminance and contrast changes. Comparing with the representative methods, the experimental results illustrate that the IRF_SSIM method is highly consistent with the subjective assessment results. [ABSTRACT FROM AUTHOR]

PurposeWe present the advantages of using dual-energy CT (DECT) for radiation therapy (RT) planning based on our clinical experience.MethodsDECT data acquired for 20 representative patients of different tumor sites and/or clinical situations with dual-source simultaneous scanning (Drive, Siemens) and single-source sequential scanning (Definition, Siemens) using 80 and 140-kVp X-ray beams were analyzed. The data were used to derive iodine maps, fat maps, and mono-energetic images (MEIs) from 40 to 190 keV to exploit the energy dependence of X-ray attenuation. The advantages of using these DECT-derived images for RT planning were investigated.ResultsWhen comparing 40 keV MEIs to conventional 120-kVp CT, soft tissue contrast between the duodenum and pancreatic head was enhanced by a factor of 2.8. For a cholangiocarcinoma patient, contrast between tumor and surrounding tissue was increased by 96 HU and contrast-to-noise ratio was increased by up to 60% for 40 keV MEIs compared to conventional CT. Simultaneous dual-source DECT also preserved spatial resolution in comparison to sequential DECT as evidenced by the identification of vasculature in a pancreas patient. Volume of artifacts for five patients with titanium implants was reduced by over 95% for 190 keV MEIs compared to 120-kVp CT images. A 367-cm3 region of photon starvation was identified by low CT numbers in the soft tissue of a mantle patient in a conventional CT scan but was eliminated in a 190 keV MEI. Fat maps enhanced image contrast as demonstrated by a meningioma patient.ConclusionThe use of DECT for RT simulation offers clinically meaningful advantages through improved simulation workflow and enhanced structure delineation for RT planning.

We report the use of Time Multiplexing Super Resolution (TMSR) to reduce significantly speckle noise in spectral imaging microscopy of unstained thin blood smear samples of malaria-infected blood. The method is based on combining speckle illumination with a moving array serving as an encoding mask. We propose the use of a new encoding mask to improve the performance of the conventional TMSR method. The new mask is a two-dimensional generalisation of the one-dimensional Ipatov code. The mask is projected on the object and 13 low-resolution images captured and subsequently decoded properly using the same array. The low contrast images are added and extracted from the resulting reconstruction, giving a super-resolved, high-contrast image. The Ipatov filter used in this work performs better than the Barker filter.

In autonomous imaging and video systems, data measurements can be extracted based on the presence or absence of system specific attributes of interest. These measurements may then be used to make critical system decisions. Therefore, it is imperative that the quality of the image used for extracting important measurements is of the highest fidelity. To achieve this, image enhancement algorithms are used to improve the quality of the image as a preprocessing procedure. Currently, most image enhancement processes require parameter selection and parameter optimizations, where the results typically require assessment by a human observer. To perform the image enhancement without human intervention, an image quality metric needs to be used to automatically optimize the enhancement algorithm's parameters. An additional complexity is that the performance of an image quality measure depends on the attributes an image possesses and the types of distortions affecting the image. Although there are many image quality metrics available in the literature, very few are designed for color images. Furthermore, most color image quality measures require a reference image as a basis, on which all other results are compared too, or require parameter adjustment before the measures can be used. Finally, most available measures can only evaluate the image quality for images that are affected by a small set of distortions. In this paper, we will show a new no-reference noparameter transform-domain image quality metric, TDMEC, which can successfully evaluate images that are affected by ten different distortion types in the TID2008 image database. This measure enables vision-based measurement systems to automatically select optimal operating parameters that will produce the best quality images for analysis.

Tissue intensity distributions in medical images can have varying degrees of statistical dispersion, which is referred to as heteroscedasticity. This can influence image contrast and gradients, but can also negatively affect the performance of general-purpose distance metrics. Numerous methods to preprocess heteroscedastic images have already been proposed, though most are application-specific and rely on either manual input or certain heuristics. We therefore propose a more general and data-driven approach that relies on the notion of intensity variance around each specific intensity value, simply referred to as intensityspecific variances. First, we introduce a method for estimating these variances from an image (or a collection of images) directly, which is followed by an illustration of how they can be used to define intensity-specific distance measures. Next, we evaluate the proposed concepts through various applications using both homoand heteroscedastic CT and MR images. Finally, we present results from both qualitative and quantitative analyses that confirm the working of the proposed approaches, and support the presented concepts as valid and effective tools for (pre)processing heteroscedastic medical images.

When a black room (a room painted black and filled with objects painted black) is viewed through a veiling luminance, how does it appear? Prior work on black rooms and white rooms suggests the room will appear white because mutual illumination in the high-reflectance white room lowers image contrast, and the veil also lowers image contrast. Other work reporting high lightness constancy for three-dimensional scenes viewed through a veil suggests the veil will not make the room appear lighter. Because mutual illumination also modifies the pattern of luminance gradients across the room while the veil does not, we were able to tease apart local luminance gradients from overall luminance contrast by presenting observers with a black room viewed through a veiling luminance. The room appeared white, and no veil was perceived. This suggests that lightness judgments in a room of one reflectance depend on overall luminance contrast only. [ABSTRACT FROM AUTHOR]

Images often suffer from low visual quality due to poor imaging conditions such as low light or hazy weather. The haze imaging model is widely used in contrast enhancement in daylight condition with haze, while the retinex model is universal for low‐light conditions. Although their forms and applications are different, they can be unified into a more general form through the proposed observation. Based on this model, the authors can estimate the reflection of the scene more accurately in more complex imaging conditions. In this study, the authors propose a simple but effective method for estimating the reflection and enhancing the image contrast based on a general imaging model. To preserve the image details and control contrast, the authors introduce dark boundary and bright boundary to handle the high‐light and low‐light conditions, and a guided structure‐preserving optimization algorithm is proposed to estimate them. After obtaining the dark and bright boundaries, the reflection is calculated and the image is enhanced accordingly. Different from previous approaches, which were designed for specific applications, the proposed method can be used for more diverse imaging conditions. Experiments show that the proposed method can be applied to many poor imaging conditions and maintain good performance. [ABSTRACT FROM AUTHOR]

In order to solve the problem of low contrast in the process of digital image research and application, this paper proposed a new definition of double interval entropy. Compared with traditional image entropy, it has better symmetry and can more accurately describe the spatial distribution of image information. The experimental results show that for low contrast image, the method can achieve effective contrast enhancement, while for high contrast image, the enhancement transformation of the method is relatively smooth. Therefore, the enhanced image has a good visual effect. At the same time, this study tried to use the adaptive parameter tuning, and combine the method with Gabor filter to further improve the effect of image enhancement. The results provides a new theoretical and experimental method for the related research. [ABSTRACT FROM AUTHOR]

Objectives: Single-photon emission computed tomography/computed tomography (SPECT/CT) hybrid systems have the advantage of performing various scans using the same imaging setting. Absorption and scattering of the gamma rays by the patient's body significantly affect images obtained from scintigraphy, especially in myocardial perfusion imaging. An important parameter for image quality in SPECT is image contrast which is defined as the difference in density between regions of the image corresponding to different levels of radioactive uptake in the patient. We objective of the study was to evaluate the influence of applying different correction methods on image contrast of myocardial SPECT/CT images. Material and Methods: A total of 114 patients, 43 females and 71 males, patient's raw data were processed and analyzed using attenuation correction (AC), scatter correction (SC), both attenuation and scatter correction together (ACSC), and no correction (NC). We short axis (coronal) slices resulted from the raw data reconstruction were chosen to perform the processing for hot and cold spheres for contrast values measurement. Statistical analysis was made for the measured contrast values for AC, SC, ACSC, and NC to determine the best image contrast. Results: When applying SC alone, it yields better contrast value (0.834), compared to AC (0.677) and ACSC (0.739). Both ACSC and AC had better image contrast compared to NC (0.592). Conclusion: We intercomparison study between the correction conditions indicates that the counts in SPECT/ CT are highly affected by all correction methods. We image contrast has been significantly improved by using SC, AC, and ACSC when compared with the NC image. Furthermore, SC is superior in the image contrast than the other correction conditions in the reconstruction of SPECT/CT MPI. [ABSTRACT FROM AUTHOR]

Purpose: We present the advantages of using dual-energy CT (DECT) for radiation therapy (RT) planning based on our clinical experience. Methods: DECT data acquired for 20 representative patients of different tumor sites and/or clinical situations with dual-source simultaneous scanning (Drive, Siemens) and single-source sequential scanning (Definition, Siemens) using 80 and 140-kVp X-ray beams were analyzed. The data were used to derive iodine maps, fat maps, and mono-energetic images (MEIs) from 40 to 190 keV to exploit the energy dependence of X-ray attenuation. The advantages of using these DECT-derived images for RT planning were investigated. Results: When comparing 40 keV MEIs to conventional 120-kVp CT, soft tissue contrast between the duodenum and pancreatic head was enhanced by a factor of 2.8. For a cholangiocarcinoma patient, contrast between tumor and surrounding tissue was increased by 96 HU and contrast-to-noise ratio was increased by up to 60% for 40 keV MEIs compared to conventional CT. Simultaneous dual-source DECT also preserved spatial resolution in comparison to sequential DECT as evidenced by the identification of vasculature in a pancreas patient. Volume of artifacts for five patients with titanium implants was reduced by over 95% for 190 keV MEIs compared to 120-kVp CT images. A 367-cm3 region of photon starvation was identified by low CT numbers in the soft tissue of a mantle patient in a conventional CT scan but was eliminated in a 190 keV MEI. Fat maps enhanced image contrast as demonstrated by a meningioma patient. Conclusion: The use of DECT for RT simulation offers clinically meaningful advantages through improved simulation workflow and enhanced structure delineation for RT planning. [ABSTRACT FROM AUTHOR]

In this study, the authors investigate content based image retrieval (CBIR) using ordered‐dither block truncation coding (ODBTC) and phase congruency feature (PCF). Relevant feature extraction plays a vital role for retrieving the image in CBIR. The unique reason to choose PCF with ODBTC is that it detects the edges and corners during variation of image while preserving image brightness and contrast. Combining the PCF and ODBTC features improves CBIR system usage in various visual data processing domains. Thus, yields a better CBIR system which assists in the reduction of storage space, decreases retrieval time and increases accuracy of the system. The precision and recall are used as performance metrics to evaluate the proposed method based on retrieval of relevant images. Extensive experimental results with Corel 1 K (1000 images), Corel 10 K (10000 images) and CALTECH 256 (30144 images) proves that the proposed method is more desirable than antecedent proposed CBIR system in terms of accuracy, precision and recall. [ABSTRACT FROM AUTHOR]

The ultimate goal of neuroscience is to explain how complex behaviour arises from neuronal activity. A comparable level of complexity also emerges in deep neural networks (DNNs) while exhibiting human-level performance in demanding visual tasks. Unlike in biological systems, all parameters and operations of DNNs are accessible. Therefore, in theory, it should be possible to decipher the exact mechanisms learnt by these artificial networks. Here, we investigate the concept of contrast invariance within the framework of DNNs. We start by discussing how a network can achieve robustness to changes in local and global image contrast. We used a technique from neuroscience-"kernel lesion"-to measure the degree of performance degradation when individual kernels are eliminated from a network. We further compared contrast normalisation, a mechanism used in biological systems, to the strategies that DNNs learn to cope with changes of contrast. The results of our analysis suggest that (i) contrast is a low-level feature for these networks, and it is encoded in the shallow layers; (ii) a handful of kernels appear to have a greater impact on this feature, and their removal causes a substantially larger accuracy loss for low-contrast images; (iii) edges are a distinct visual feature within the internal representation of object classification DNNs. [ABSTRACT FROM AUTHOR]

The fusion of infrared and visible images has been playing an important role in various scenarios all over the world. For the fusion results from most of the existing techniques in this area, some features, such as the image contrast and edge details, are still needed to be improved. In this Letter, a new fusion method of the infrared and visible image is rendered. In this method, the infrared image is preprocessed to improve the contrast. Then, the two source images are decomposed based on non-subsampled shearlet transform (NSST). The fusion rules based on regional energy and co-occurrence filtering are proposed for the low-frequency and high-frequency NSST coefficients, respectively. Experimental results show that the proposed method can effectively retain the details of the source image, meanwhile improve the contrast of the fused image. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have facilitated substantial progress on various problems in computer vision and image processing. However, applying them to image fusion has remained challenging due to the lack of the labelled data for supervised learning. This paper introduces a deep image fusion network (DIF-Net), an unsupervised deep learning framework for image fusion. The DIF-Net parameterizes the entire processes of image fusion, comprising of feature extraction, feature fusion, and image reconstruction, using a CNN. The purpose of DIF-Net is to generate an output image which has an identical contrast to high-dimensional input images. To realize this, we propose an unsupervised loss function using the structure tensor representation of the multi-channel image contrasts. Different from traditional fusion methods that involve time-consuming optimization or iterative procedures to obtain the results, our loss function is minimized by a stochastic deep learning solver with large-scale examples. Consequently, the proposed method can produce fused images that preserve source image details through a single forward network trained without reference ground-truth labels. The proposed method has broad applicability to various image fusion problems, including multi-spectral, multi-focus, and multi-exposure image fusions. Quantitative and qualitative evaluations show that the proposed technique outperforms existing state-of-the-art approaches for various applications. [ABSTRACT FROM AUTHOR]

Contrast enhancement of an image can be performed by using a simple histogram equalisation (HE) technique. However, there are some drawbacks of HE like immense brightness change, artificial effects, over‐enhancement, which make it unsuitable to be used in many applications. To resolve these issues a new adaptive heuristic HE approach is proposed in this study. First, probability distribution function (PDF) of the image is calculated. Second, an adaptive parameter is calculated based on the mean and maximum values of that PDF. Thereafter, PDF and cumulative distribution function (CDF) are modified by applying a threshold limit to that adaptive parameter. Finally, another adaptive parameter is finding out by using modified CDF and a new CDF is obtained by using this second adaptive parameter. Traditional HE is then applied with the new CDF to getting the enhanced image. The visual and quantitative results of the proposed method outperform all other state‐of‐the‐art papers and works well both for low and bright contrast images simultaneously. After rigorous experiment, it is concluded that the authors' method enhances the image contrast very well with no over‐enhancement or artificial effects in the images and also preserves the original characteristics of the input images. [ABSTRACT FROM AUTHOR]

Using kilovoltage cone-beam computed tomography (kV-CBCT) and heavy-atom radiosensitizers in image-guided radiotherapy (IGRT) can provide numerous benefits, such as image contrast enhancement in radiation dose delivery. However, the increased use of kV-CBCT for daily imaging procedures may inevitably deposit certain amount of radiation dose to the patient, especially when nanoparticles used as radiosensitizers are involved. In this study, we use Monte Carlo simulation to evaluate the imaging dose escalation due to nanoparticle addition with varying nanoparticle material, nanoparticle concentration and photon beam energy. A phantom was used to determine the relationships between the imaging dose enhancement ratios (IDERs) and different concentrations (3–40 mg/ml) of gold (Au), platinum (Pt), iodine (I), silver (Ag) and iron oxide (Fe2O3) nanoparticles, under the delivery of 120–140 kVp photon beams from the CBCT. It is found that gold and platinum nanoparticles of 40 mg/ml concentration had the highest IDER (~1.6) under the 120 kVp photon beam. This nanoparticle addition resulted in a 0.63% increase of imaging dose based on a typical dose prescription of 200 cGy per fraction in radiotherapy, and is within the standard uncertainty of ±5% in radiation dose delivery. This study proves that the incorporation of higher concentration nanoparticles under lower photon beam energy could increase the imaging dose. The results from this study can enable us to understand more about the incorporation of heavy-atom nanoparticles in IGRT systems. [ABSTRACT FROM AUTHOR]

Background: Gold nanoparticles with high atomic number and density have good potential to be used as contrast media in computed tomography. Objectives: In this study, we aimed to assess radiation dose and contrast enhancement performance of gold nanoparticles by measuring contrast to noise ratio compared to clinically used iodinated contrast agents at same concentrations. Contrast enhancement was evaluated in different tube voltages and currents. Methods: First, polyethylene glycol coated gold nanoparticles were synthesized with concentrations of 0.5 mM, 0.6 mM, and 0.7 mM. Gold nanoparticles and iodinated contrast media were scanned with CT imaging system at different tube voltages and time current product. CT dose index (CTDI) value was measured by special phantom and electrometer. Improving in image contrast was assessed by contrast to noise ratio. Results: Results showed that gold nanoparticles in all concentrations and energies from 80 to 130kVp display highe rimage contrast-to-noise ratio (CNR) than iodinated contrast media. Image CNR was increased by increasing kVp and mAs. The CNR value was maximum at the voltage of 80 and 130 kVp for iodinated compounds and gold nanoparticles, respectively. The CNR value for gold nanoparticles at 130 kVp and 200 mAs was approximately five times higher than that of iodinated compounds. Conclusion: Gold nanoparticles could be a good candidate for optimizing CT imaging by lowering radiation dose as low as possible while enhancing the image contrast. [ABSTRACT FROM AUTHOR]

Image enhancement is an essential procedure in machine vision-based inspection. In practical applications, image enhancement is usually a part of image pre-processing, intended to make the following inspection more effective. The image enhancement method is usually selected by trial-and-error or on the basis of experience. This paper presents an automatic procedure for fast and effective image enhancement. The procedure uses multivariate analysis to automatically construct an optimal image enhancement model. First, an optimally enhanced image was selected from the literature as a basis for the model. Then, the image features were identified and Wilks' statistic was used for feature selection. Next, discriminate functions were built to select the optimal image enhancement method. To verify the model, 53 training images from the literature and 12 test images from a local company were used in an experimental analysis. The model achieved 98.11% accuracy in selecting the most suitable image enhancement method, and the average increase in contrast was 98% for the 53 training images. The enhancement method selection results for the 12 test images were also in agreement with the 53 training images from the literature. The results show that the proposed method is effective and appropriate for quickly improving image contrast. [ABSTRACT FROM AUTHOR]

[Display omitted] • MR microscopy with spin-echo (SE) and its DEFT variant (DE-SE) was compared. • The signal dependence on RF pulse flip angles and on diffusion was studied. • DE-SE was found considerably more sensitive to flip angle deviations than SE. • Diffusion signal attenuation in MR microscopy with DE-SE may be significant. • The water signal ratio between DE-SE and SE images was 10:1 with short TRs. Rapid MR imaging of slowly relaxing samples is often challenging. The most commonly used solutions are found in multi spin-echo (RARE) sequences or gradient-echo (GE) sequences, which allow faster imaging of such samples with multiple acquisitions of k -space lines per excitation or imaging with very short repetition times (TR s). Another solution is the use of a spin-echo (SE) sequence superimposed with a driven equilibrium Fourier transform (DEFT) method. Such a (DE-SE) imaging sequence has two refocusing RF pulses that produce two spin-echoes. In the first echo, the signal is acquired from the k -space line, and in the second echo, a 90° RF pulse is applied, typically 180° out of phase with respect to the excitation RF pulse. This last RF pulse allows almost complete magnetization reversal back to the longitudinal orientation with minimal magnetization loss. The DE-SE sequence and its RARE variant are widely used in clinical imaging, but its use in MR microscopy has some peculiarities related to the usually less perfect RF pulse flip angles and diffusion. In this study, their effects are first theoretically analyzed and later verified by experiments on test samples performed on a 9.4 T system for MR microscopy. Experiments on a water-filled tube for TE = 3.4 ms and TR = 25–200 ms showed that the DE-SE sequence produces about 10 times more signal than the SE sequence in this TR range. Finally, the performance of the DE-SE sequence compared to the SE sequence was demonstrated on a biological sample. The presented DE-SE sequence has been shown to be effective for rapid imaging of samples with long T 1 relaxation times in MR microscopy and can also be considered as a suitable method for rapid proton density weighed imaging of materials. [ABSTRACT FROM AUTHOR]

Ultrasound echoscopy technologies are continuously evolving towards new modalities including quantitative parameter imaging, elastography, 3D scanning, and others. The development and analysis of new methods and algorithms require an adequate digital simulation of radiofrequency (RF) signal transformations. The purpose of this paper is the quantitative evaluation of RF signal simulation uncertainties in resolution and contrast reproduction with the model of a phased array transducer. The method is based on three types of standard physical phantoms. Digital 3D models of those phantoms are composed of point scatterers representing the weak backscattering of the background material and stronger backscattering from inclusions. The simulation results of echoscopy with sector scanning transducer by Field II software are compared with the RF output of the Ultrasonix scanner after scanning standard phantoms with 2.5 MHz phased array. The quantitative comparison of axial, lateral, and elevation resolutions have shown uncertainties from 9 to 22% correspondingly. The echoscopy simulation with two densities of scatterers is compared with contrast phantom imaging on the backscattered RF signals and B-scan reconstructed image, showing that the main sources of uncertainties limiting the echoscopy RF signal simulation adequacy are an insufficient knowledge of the scanner and phantom’s parameters. The attempt made for the quantitative evaluation of simulation uncertainties shows both problems and the potential of echoscopy simulation in imaging technology developments. The analysis presented could be interesting for researchers developing quantitative ultrasound imaging and elastography technologies looking for simulated raw RF signals comparable to those obtained from real ultrasonic scanning.

In JPEG 2000, two fundamental steps in image compression are wavelet transform and bit-planes encoding. In this method, first the wavelet transform of the image is provided, then, depending on the desired compression rate, the number of bit-planes of the wavelet coefficients are coded from most significant bit to the least significant bit. After achieving the desired compression rate, the other less significant bit-planes of wavelet coefficients are disregarded. In applying this method for low contrast image, wavelet coefficients of high frequency regions have small amounts, so these values are reflected in low bit-planes. These low bit-planes are removed during compression in encoder. Hence, JPEG 2000 has limited performance especially in low contrast image compression. In this paper, to improve the performance of JPEG 2000 a preprocessing is performed on the image to increase its contrast. With increasing image contrast, high frequency regions would have higher values in wavelet coefficients. As a result, information of these coefficients is largely preserved at the bit-planes encoding stage. The results show that the proposed preprocessing method improves the performance of JPEG 2000 in terms of compression rate and retrieved image quality. In more detail, for an equal retrieved image quality in the proposed method and JPEG 2000, the proposed method improves the compression rate of JPEG 2000 with an average of about 3.5%.

Electrical impedance tomography (EIT) is relatively new. It is a verypromising technique to be developed especially in the medical field. The advantages of EIT are that it is non-ionizing, simple, and portable and that it produces a high contrast image. Unfortunately, this modality does not have the capability to generate a highresolution image. Almost all imaging modalities has both advantages and disadvantages. Combining one modality with another is hence expected to cover the weaknesses of each other. The problem is how to develop the concepts, measurement systems and algorithm of dual modalities, particularly electrical and acoustical. The electrical modality can produce high contrast and the acoustical modality can produce high resolution. Combination of these will enhance the image resolution of EIT. High image resolution from the ultrasound reflection tomography is used as the prior information to improve the image resolution of the EIT. Finite Element Model (FEM) can be arranged by non-uniform elements, which are adapted to the boundary. Element models with higher density are arranged at the boundaries to obtain improvements of resolution and the model elements with lower density arranged at other locations to reduce the computational cost. The dual modality EIT with Ultrasound Reflection (EIT-UR) can produce high resolution and contrast image. The resolution improvement can also accelerate the convergence of the Newton-Raphson reconstruction methods.

Calcium phosphate (CaP) bioceramics closely resemble the natural human bone, which is a main reason for their popularity as bone substitutes. However, this compositional similarity makes it difficult to distinguish CaPs, especially in particulate form, from native bone by imaging modalities such as X-ray radiography, computed tomography (CT), and magnetic resonance imaging (MRI) to monitor the healing progress. External contrast agents can improve the imaging contrast of CaPs but can affect their physicochemical properties and can produce artifacts. In this work, we have attempted to improve the contrast of CaP nanoparticles via ion substitutions for multimodal imaging. Calcium-deficient hydroxyapatite (CDHA) nanoparticles with silver (Ag), gadolinium (Gd), and iron (Fe) substitution were prepared by a microwave-accelerated wet chemical process to improve the contrast in CT, T1 (spin–lattice), and T2 (spin–spin) MRI relaxation modes, respectively. Ag, Gd, and Fe were substituted at 0.25, 0.5, and 0.25 at.%, respectively. The ion-substituted CDHA (ICDHA) was found to be phase pure by X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FT-IR). Transmission electron microscopy (TEM) images showed that the ICDHA nanoparticles were platelet shaped and of 52 ± 2 nm length and 6 ± 1 nm width. The ICDHA showed high contrast in X-ray and CT compared to CDHA. The vibrating sample magnetometry (VSM) studies showed the ICDHA to exhibit paramagnetic behavior compared to diamagnetic CDHA, which was further confirmed by improved contrast in T1 and T2 MRI mode. In addition, the in vitro tetracycline drug loading and release was studied to investigate the capability of these nanoparticles for antibiotic drug delivery. It was found that a burst release profile was observed for 24 h with 47–52% tetracycline drug release. The ICDHA nanoparticles also showed in vitro antibacterial activity against Staphylococcus aureus and Escherichia coli due to Ag, which was further enhanced by antibiotic loading. In vitro biocompatibility studies showed that the triple-ion-substituted ICDHA nanoparticles were cytocompatible. Thus, the ion-substituted CDHA nanoparticles can have potential theranostic applications due to their multimodal image contrast, antibacterial activity, and drug delivery potential. Future work will be conducted with actual bone samples in vitro or in animal models.

In imaging of Yttrium-90 patients treated hepatic primary and metastatic cancers, bremsstrahlung photons produced in a wide energy range is used. However, the image quality depends on acquisition energy window. This research aimed energy window optimization for Yttrium-90 bremsstrahlung imaging and 48 patients with various types of cancer received radioembolization therapy were investigated. Patients were imaged using a GE Healthcare Optima NM/CT 640 series gamma camera system with a medium energy general-purpose (MEGP) collimator and planar images were acquired with 8 different energy windows in the 55-400 keV energy range. The data set, formed with the % FOV, contrast, and spatial resolution of image quality parameters calculated from these images, was statistically examined with ANOVA and Tukey tests. According to the visual evaluations and ANOVA/Tukey test results, it was statistically concluded that energy window of 90-110 keV is the optimal energy window while 60-400 keV energy ranges show the lowest image quality for Y-90 bremsstrahlung imaging., (Creative Commons Attribution license.)

Welding defect detection in a radiographic image is an important topic in the field of industrial non‐destructive testing. To improve the accuracy of welding defect segmentation, a local image enhancement approach is proposed. In this algorithm, the requirement of contrast enhancement is considered when extracting the weld seam and segmenting the weld defect. The whole defect detection is conducted by three procedures: image enhancement, welding seam extraction, and defect segmentation. Firstly, a method for determining the Localised Pixel Inhomogeneity Factor (LPIF) is proposed. Then, based on the results of LPIF, the Otsu method is applied to segment the welding seam and defects are, identified by region growing algorithm. The authors compared LPIF with histogram equalisation, adaptive histogram equalisation, and contrast‐limited adaptive histogram equalisation algorithms and assessed its performance by using indicators such as image contrast, image definition, and edge intensity. Moreover, the authors compared the segmentation results of the enhanced defect images with the original image to further study the method's effect on weld defect segmentation. More than 70 images containing various types of defects are tested. The experimental results demonstrate that the quality of enhanced defect images is improved significantly, and has a high relative segmentation accuracy of more than 92%. [ABSTRACT FROM AUTHOR]

Proper contrast usually makes an object distinguishable, while contrast degradation will affect our understanding of the object. Many experiments found that the human visual system is more sensitive to contrast than to luminance in distinguishing an object. However, few studies have been conducted to assess the quality of contrast change effectively. In our study, we first reveal some characteristics of contrast-distorted images by analyzing image data. Based on these characteristics, we then present two novel and fast full-reference image quality metrics: FC-SVD for a full-reference model and RC-SVD for a reduced-reference image quality assessment model, both using singular value decomposition (SVD). The proposed methods are evaluated through four public contrast-related databases (TID2013, TID2008, CCID2014, and CSIQ). The results show that they have excellent performance in prediction accuracy and much lower computation complexity than other state-of-the-art image quality assessment methods. • A comprehensive analysis of the characteristics of contrast-distorted images. • Effective features designed for the quality measurement of contrast-distorted images. • A contrast distortion evaluation model with good performance. • A promising candidate for real-time quality assessment of contrast distortion. [ABSTRACT FROM AUTHOR]

Latent fingerprints represent valuable information-storage platforms, which play a key role in the forensic practice and health assessment. Recent works have explored novel image-enhancement methods in latent fingerprinting, which are associated with fluorescence dyes. In this study we improve the quality of fingerprint images, using a combination of fluorescence dyes and bimetallic nanoparticles. We find that Rhodamine 6G can serve as a kind of 'glue' adhering well onto Au-Pd core-shell nanorods. This system which employs molecules like α-amylase can be successfully used for recognition of artificial latent fingerprints. The appropriate mechanism can be coupling of localized surface plasmon resonance in the nanorods mentioned above and the emission of Rhodamine 6G. As a result, we observe notable enhancement of the images of artificial latent fingerprints. [ABSTRACT FROM AUTHOR]

BACKGROUND: Recently, the treatment efficiency of Yttrium 90 (Y-90) and providing reliable estimates of activity by single photon emission computed tomography (SPECT) imaging of bremsstrahlung radiation released during beta therapy have been evaluated. In the Y-90 bremsstrahlung SPECT imaging, the resulting energy spectrum is very complex and continuous, which creates many difficulties in the imaging protocol and image reconstruction. Furthermore, image quality and quantitative accuracy in the bremsstrahlung SPECT imaging are affected by collimator penetration and scatter. So, the collimator type and its geometry have impressive effects on the spatial resolution, system sensitivity and image contrast. MATERIAL AND METHODS: Hereby, in this paper, we evaluated the effect of the energy window (three energy windows: 60 to 160 keV, 160 to 400 keV, and 60 to 400 keV) and the commercial parallel-hole collimators with different geometric parameters on the Y-90 bremsstrahlung spectrum and the image quality of the liver tumors based on criteria such as system sensitivity and image contrast. SIMIND Monte Carlo simulation code was used to generate the Y-90 bremsstrahlung SPECT images of the liver tumor with different diameters: 1.36, 2.04, 2.72, 3.4, 4.08, and 4.76 cm by use of the digital Zubal phantom. Furthermore, the tumor size was estimated by evaluating pixel intensity profile on the line drawn through the activity distribution image. RESULTS: Our results showed that the collimator choice and energy window setting in the bremsstrahlung SPECT imaging have significant effects on the image quality and tumor size estimation. Optimal image quality could be acquired by the energy window of 60 to 400 keV and the SPECT system equipped with a Medium-Energy General-Purpose (MEGP) collimator of Millennium VG Kameran (GV) Company. Moreover, the estimation of distribution size was close to the actual value for tumor sizes larger than 2.04 cm, especially by using the SPECT system equipped with the GV-MEGP collimator in the wide energy window. CONCLUSIONS: We found an optimal collimator to be more appropriate for improving the imaging quality of Y-90 bremsstrahlung photons, which can be used for reliable activity distribution estimates after radiation therapy. [ABSTRACT FROM AUTHOR]

Detail information on objects of interest plays a vital role in current medical diagnosis. However, the existing multimodal sensor fusion methods cause problems of low contrast and color distortion during the process of integration. Therefore, the preservation of detail information in high contrast is worthy of investment in the field of medical image fusion. This paper presents a new multiscale fusion-based framework using the local Laplacian pyramid transform (LLP) and adaptive cloud model (ACM). The proposed framework, LLP+ACM, includes three key modules. First, the input images are decomposed into detail-enhanced approximate and residual images using LLP. Second, ACM is adopted to fuse the approximate images. A salience match tool is used to fuse the residual images. Third, the fused image is reconstructed using the inversed LLP. Experiments show that the proposed LLP+ACM significantly improves detail information with high contrast and reduces the color distortion of the fused images in both subjective and objective evaluations. [ABSTRACT FROM AUTHOR]

Abstract The purpose of this study was to investigate the effect of image quality under various imaging parameters (60, 70, 80, 90, 100, 110, and 120 kV at 200 mA and 10 ms/63, 80, 100, 160, 200, 250, and 320 mA at 120 kV and 10 ms) and the diameter of the fiducial marker (0.25, 0.50, 0.75, and 1.10 mm) on the correlation modeling error for dynamic tumor tracking (DTT) in the Vero4DRT system. Each fiducial marker was inserted into the center of the 30 × 30 × 10 cm3 water-equivalent phantom. A programmable respiratory motion table was used to simulate breathing-induced organ motion, with an amplitude of ±20 mm and a breathing cycle of 4 s. The correlation modeling error was calculated from the absolute difference between the detected and predicted target positions in the cranio-caudal direction. The image contrast of the fiducial marker was enhanced with increasing kV and mA. Increasing the diameter of the fiducial marker also enhanced the image contrast. Correlation-modeling error does not depend on the image quality and fiducial marker diameter. A lower kV setting did not generate a 4D model due to poor image contrast. All fiducial marker diameters were identified as good candidates for DTT in the Vero4DRT system. [ABSTRACT FROM AUTHOR]

Abstract Many methods based on the histogram equalization have been introduced for the use in contrast enhancement. However, a technique that can produce proper natural enhancement simultaneously in images with brightness ranges of dark, medium, and bright, has been less presented. For this purpose, a powerful contrast enhancement algorithm based on the histogram equalization, called triple dynamic clipped histogram equalization (TDCHE) method has been proposed in this paper. In the proposed method, the histogram of the input image is first partitioned based on the mean or median into three portions. Then, the histogram clipping process is performed in each sub-histogram. Finally, prior to performing the equalization process of each sub-histogram independently, each sub-histogram is mapped to a new dynamic range. The proposed method is introduced to achieve multiple objectives of maximum average information content (entropy), enhancement rate control and reasonable brightness preservation. In addition, this method leads to natural enhancement by producing clear images and preserving maximum details. Performance assessment of the proposed method in terms of the entropy, structural similarity index as well as visual quality based on mean opinion score (MOS) demonstrates perceived superiority of the proposed algorithm in comparison with the previously presented techniques. [ABSTRACT FROM AUTHOR]

X-ray computed tomography (CT) requires an optimal compromise between image quality and patient dose. While high image quality is an important requirement in CT, the radiation dose must be kept minimal to protect the patients from ionizing radiation-associated risks. The use of probes based on gold nanoparticles (AuNPs) along with active targeting ligands for specific recognition of cancer cells may be one of the balanced solutions. Herein, we report the effect of folic acid (FA)-modified AuNP as a targeted nanoprobe on the contrast enhancement of CT images as well as its potential for patient dose reduction. For this purpose, nasopharyngeal KB cancer cells overexpressing FA receptors were incubated with AuNPs with and without FA modification and imaged in a CT scanner with the following X-ray tube parameters: peak tube voltage of 130 KVp, and tube current-time products of 60, 90, 120, 160 and 250 mAs. Moreover, in order to estimate the radiation dose to which the patient was exposed during a head CT protocol, the CT dose index (CTDI) value was measured by an X-ray electrometer by changing the tube current-time product. Raising the tube current-time product from 60 to 250 mAs significantly increased the absorbed dose from 18 mGy to 75 mGy. This increase was not associated with a significant enhancement of the image quality of the KB cells. However, an obvious increase in image brightness and CT signal intensity (quantified by Hounsfield units [HU]) were observed in cells exposed to nanoparticles without any increase in the mAs product or radiation dose. Under the same Au concentration, KB cells exposed to FA-modified AuNPs had significantly higher HU and brighter CT images than those of the cells exposed to AuNPs without FA modification. In conclusion, FA-modified AuNP can be considered as a targeted CT nanoprobe with the potential for dose reduction by keeping the required mAs product as low as possible while enhancing image contrast. [ABSTRACT FROM AUTHOR]

• Plant-based electroporation models were analyzed by morphologic and quantitative MRI. • Potato was suitable for MRI, showing distinct contrast changes post electroporation. • FLAIR yielded highest image contrast in potatoes enabling semi-automatic segmentation. • T1 relaxation time was the main driver for MRI contrast changes in potatoes. • Our findings help to further optimize MRI of electroporated plant tissues. Plant-based models can reduce the number of animal studies for electroporation research in medical cancer treatment modalities like irreversible electroporation. Magnetic resonance imaging (MRI) provides volumetric visualisation of electroporated animal or plant tissues; however, contrast behaviour is complex, depending on tissue and sequence parameters. This study numerically analysed contrast between electroporated and non-electroporated tissue at 1.5 T in various MRI sequences (DWI, T1W, T2W, T2*W, PDW, FLAIR) performed 4 h after electroporation in apples (N = 4) and potatoes (N = 8). Sequence parameters (inversion time [TI], echo time [TE], b-value) for optimal contrast and electroporation-mediated changes in T1 and T2 relaxation times and apparent diffusion coefficient (ADC) were determined for potato (N = 4) using quantitative parameter mapping. FLAIR showed the electroporated zone in potatoes with best contrast, whereas no sequence yielded clear visibility in apples. After electroporation, T1 and T2 in potato decreased by 29% ([1245 ± 54 to 886 ± 119] ms) and 12% ([249 ± 17 to 217 ± 12] ms), respectively. ADC increased by 11% ([1303 ± 25 to 1449 ± 28] × 10–6 mm2/s). Optimal contrast was found for TI = 1000 ms, low TE and high b-value. T1 was most sensitive to EP-mediated tissue changes. Future research could use this methodology and findings to obtain high-contrast MR images of electroporated and non-electroporated biological tissues. [ABSTRACT FROM AUTHOR]

[Display omitted] • Overcoming the diffraction limit in a direct terahertz imaging system in transmission mode. • An entirely new use of the Terajet effect for resolution enhancement of terahertz imaging. • Experimental demonstration of the feasibility of overcoming the diffraction limit. • A special pinhole structure was designated and characterised to validate results. We report on resolution enhancement of sub-terahertz (THz) images by using the terajet effect. A mesoscale cuboid dielectric particle, used to establish the terajet, was placed in front of an object located at the focus of the THz beam. The object under study was based on a printed circuit board (PCB) perforated with different holes with diameters ranging from 1.8 to 3.0 mm and separated from each other by a distance that varies from 0.25 to 4 mm. The sample was illuminated by a continuous wave source at a frequency of 0.3 THz and the image was obtained using a sensor based on a strained-Si Field-Effect Transistor. The image was formed pixel-by-pixel in a transmission mode configuration. A clearer image with enhanced resolution was obtained when the mesoscale cube was introduced in the optical path. The terajet effect made possible to resolve a separation between holes of around 0.5 mm (lower than the wavelength, 1 mm), that is, below the diffraction limit. The method described is easy to implement, cost effective and could be used to improve the resolution of any real imaging system. [ABSTRACT FROM AUTHOR]

Laser lights have been used by dermatologists for tattoo removal through photothermal interactions. However, most clinical studies used a visual scoring method to evaluate the tattoo removal process less objectively, leading to unnecessary treatments. This study aimed to develop a simple and quantitative imaging method to monitor the degree of tattoo removal in in vivo skin models. Sprague Dawley rat models were tattooed with four different concentrations of black inks. Laser treatment was performed weekly on the tattoos using a wavelength of 755 nm over six weeks. Images of non-treated and treated samples were captured using the same method after each treatment. The intensities of the tattoos were measured to estimate the contrast for quantitative comparison. The results demonstrated that the proposed monitoring method quantified the variations in tattoo contrast after the laser treatment. Histological analysis validated the significant removal of tattoo inks, no thermal injury to adjacent tissue, and uniform remodeling of epidermal and dermal layers after multiple treatments. This study demonstrated the potential of the quantitative monitoring technique in assessing the degree of clearance level objectively during laser treatments in clinics.

Many functional materials are difficult to analyse by scanning transmission electron microscopy (STEM) on account of their beam sensitivity and low contrast between different phases. The problem becomes even more severe when thick specimens need to be investigated, a situation that is common for materials that are ordered from the nanometre to micrometre length scales or when performing dynamic experiments in a TEM liquid cell. Here we report a method to optimize annular dark-field (ADF) STEM imaging conditions and detector geometries for a thick and beam-sensitive low-contrast specimen using the example of a carbon nanotube/polymer nanocomposite. We carried out Monte Carlo simulations as well as quantitative ADF-STEM imaging experiments to predict and verify optimum contrast conditions. The presented method is general, can be easily adapted to other beam-sensitive and/or low-contrast materials, as shown for a polymer vesicle within a TEM liquid cell, and can act as an expert guide on whether an experiment is feasible and to determine the best imaging conditions.