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The human hippocampus is difficult to image given its small size, location, shape, and complex internal architecture. Structural magnetic resonance imaging (MRI) has shown age-related hippocampal volume changes that vary along the anterior-posterior axis. Diffusion tensor imaging (DTI) provides complementary measures related to microstructure, but there are few hippocampus DTI studies investigating change with age in healthy participants, and all have been limited by low spatial resolution. The current study uses high resolution 1 mm isotropic DTI of 153 healthy volunteers aged 5-74 years to investigate diffusion and volume trajectories of the hippocampus (whole, head, body, and tail) and correlations with memory. Hippocampal volume showed age-related changes that differed between head (peaking at midlife), body (no changes), and tail (decreasing across the age span). Fractional anisotropy (FA) and mean, axial, and radial diffusivities (MD, AD, RD) yielded peaks or minima, respectively, at ~30-35 years in all three subregions of the hippocampus. Greater magnitude changes were observed during development than in aging. Age trajectories for both volume and DTI were similar between males and females. Correlations between tests of memory and FA and/or volume were significant in younger subjects (5-17 years), but not in 18-49 year olds or 50-74 year olds. MD was significantly correlated with memory performance in 18-49 year olds, but not in other age groups. Given the diffusion-weighted image contrast and resolution, head digitations could be examined revealing that the majority of subjects had 3-4 (48%) or 2 (32%) bilaterally with no effect of age. One millimeter isotropic DTI yielded high quality diffusion-weighted maps of the human hippocampus that showed regionally specific age effects and cognitive correlations along the anterior-posterior axis from 5 to 74 years.

Metal-based nanoparticles (NPs) have been extensively studied for dose enhancement applications in radiation therapy. This study investigated the utility of such NPs for image-guided radiation therapy (IGRT). Phantom images of gold NPs (AuNPs) and titanium peroxide NPs (TiOxNPs) with different concentrations were acquired using IGRT modalities, including cone-beam computed tomography (CBCT). AuNPs induced strong contrast enhancement in kV energy CBCT images, whereas TiOxNPs at high concentrations showed weak but detectable changes. The results indicated that these NPs can be used to enhance IGRT images as well as dose enhancement for treatment purposes.

Advances in dual-energy CT (DECT) technology and spectral techniques are catalyzing the widespread implementation of this technology across multiple radiology subspecialties. The inclusion of energy- and material-specific datasets has ushered overall improvements in CT image contrast and noise as well as artifacts reduction, leading to considerable progress in radiologists' ability to detect and characterize pathologies in the abdomen. The scope of this article is to provide an overview of various quantitative clinical DECT applications in the abdomen and pelvis. Several of the reviewed applications have not reached mainstream clinical use and are considered investigational. Nonetheless awareness of such applications is critical to having a fully comprehensive knowledge base to DECT and fostering future clinical implementation.

In this report, we applied annular bright-field and annular dark-field low-energy (30 keV) scanning transmission electron microscopy imaging to a vitreous ice-embedded biological macromolecule, T4 phage, to investigate the applicability of these methods for morphological investigation and sample screening. Multiple camera lengths were examined to find the optimal acceptance angle for both modes. Image clarity differed substantially between the modes, with the presence of ice also strongly influencing the quality of acquired micrographs. In annular dark-field mode, the proper discrimination of electrons scattered by the specimen from those scattered by the background ice was found to be difficult due to the severe overlap of the scattered electrons. The resulting micrographs lacked clarity, and the ice-embedded phage particles could only be discerned after post-processing image adjustment. However, in annular bright-field mode, despite similar overlapping of the scattered electrons, it was possible to assess the morphology and intactness of the specimen in the embedding ice, suggesting that this mode may find utility in low-energy cryo-scanning transmission electron microscopy imaging methods.

The protection of information against electromagnetic penetration is one of the most important aspects related to the protection of information against its non-invasive acquisition. Compared to the activities of cybercriminals, the use of electromagnetic emissions in the electromagnetic infiltration process does not leave any traces of activity, and the owner of the information is not aware of its loss. The most common activities of electromagnetic eavesdropping are related to the infiltration of emission sources, graphically revealing the processing of information using both analog and digital methods. This allows for the presentation of reconstructed data in the form of images. Correct display of the acquired information requires knowledge of raster parameters such as line length and the number of lines building the reconstructed image. Due to the lack of direct access to the intercepted device, knowledge in this field does not allow for the correct determination of the aforementioned parameters, and thus, for recreating an image that would contain legible and understandable data. Additionally, incorrect values of the parameters result in failure of further processing of the obtained image, e.g., by using a coherent summation of images. Therefore, it is necessary to propose a solution that will allow not so much to roughly define the raster parameters but to estimate them precisely. Moreover, it should enable the automation of the process after the implementation of an appropriate algorithm. The article proposes an algorithm for estimating the line length of the reconstructed image. The raster parameter estimated with the use of the algorithm allows for summarizing images several dozen times with a significant improvement in the image quality and readability of the data contained in it. The image summation algorithm is very often used as one of the main image processing methods in the electromagnetic infiltration process. Incorrect raster parameters often make coherent summation useless. The proposed algorithm for estimating the line length of the reconstructed image uses three methods of determining the line length of the image for a given accuracy. At the same time, criteria were indicated that must be met to determine the correct length of the image line for the assumed accuracy of estimation. Obtained results confirmed that the proposed methods and criteria are effective in the process of electromagnetic infiltration. These methods allow us to determine the line length of reconstructed images with accuracy up to 10−5.

Raman imaging has transcended in recent times from being an analytical tool to a molecular profiling technique. Biomedical applications of this technique often rely on singular-value decomposition (SVD), principal component analysis (PCA), etc. for data analysis. These methods, however, obliterate the molecular information contained in the original Raman data leading to speculative interpretations based on relative intensities. In the present study, SVD analysis of the Raman images from Penicillium chrysogenum resulted in 11 spectral components and corresponding images with highly distorted spectral features and complex image contrast, respectively. To interpret the SVD results in molecular terms, we have developed a combined multivariate approach. By applying this methodology, we have successfully extracted the contribution of five biomolecular constituents of the P. chrysogenum filamentous cell to the SVD vectors. Molecular interpretability will help SVD/PCA surpass the realm of variance-based classification to a more meaningful molecular domain.

Visual-analytics-guided systems are replacing human efforts today. In many applications, movement in off-road terrain is required. Considering the need to negotiate various soft ground and desertic conditions, the beaten tracks of leading vehicles considered to be safe and suitable for guiding are used in such operations. During night, often, these tracks pass through low-contrast conditions posing difficulty in their identification. The maximization of track contrast is therefore desired. Many contrast enhancement techniques exist but their effectiveness varies as per the surrounding. Other than conventional techniques, the role of texture too becomes important for enhancing the differentiable track contrast. Gray-level co-occurrence matrix (GLCM)-based statistic measures are used here to evaluate the track texture. These measures are seen to improve the contrast of vehicle tracks significantly. A track-index-based technique is proposed to sort various images as per their effectiveness in increasing the track contrast. Different forms of track indices are proposed and compared. The proposed track index is seen as effective in sorting 88.8% of contrast images correctly. The proposed technique of creating and sorting images based on the contrast level is seen as a useful tool for improved fidelity in many difficult situations for making the off-road operations sustainable.

Medical image fusion has been carried out to obtain information benefits from multi-modalities of medical images. The purpose of this study is to improve the image contrast of fusion image with adaptive method. The median filter was implemented to the images before registration to remove noise for obtaining good image fusion. Geometric transformation-based image registration was used to automatically align two images of computed tomography (CT) scanner and magnetic resonance imaging (MRI) to a common coordinate system. After that, the image contrast was improved with adaptive method. Finally, the fused image was assessed using the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). From this study, it was found that the average SNR value in the image fusion before contrast improvement is 0.09 and after that is 0.73. While the average CNR value in image fusion before contrast improvement is 1.54 and after that is 1.79. It means that the CNR increases 14.02%.

Crack detection is one of the important contents of the nuclear fuel pellet quality inspection. Aiming at the problem of high crack false detection rate caused by low image contrast of nuclear fuel...

Most of the melanoma cases of skin cancer are the life-threatening form of cancer. It is prevalent among the Caucasian group of people due to their light skin tone. Melanoma is the second most common cancer that hits the age group of 15–29 years. The high number of cases has increased the importance of automated systems for diagnosing. The diagnosis should be fast and accurate for the early treatment of melanoma. It should remove the need for biopsies and provide stable diagnostic results. Automation requires large quantities of images. Skin lesion datasets contain various kinds of dermoscopic images for the detection of melanoma. Three publicly available benchmark skin lesion datasets, ISIC 2017, ISBI 2016, and PH2, are used for the experiments. Currently, the ISIC archive and PH2 are the most challenging and demanding dermoscopic datasets. These datasets’ pre-analysis is necessary to overcome contrast variations, under or over segmented images boundary extraction, and accurate skin lesion classification. In this paper, we proposed the statistical histogram-based method for the pre-categorization of skin lesion datasets. The image histogram properties are utilized to check the image contrast variations and categorized these images into high and low contrast images. The two performance measures, processing time and efficiency, are computed for evaluation of the proposed method. Our results showed that the proposed methodology improves the pre-processing efficiency of 77% of ISIC 2017, 67% of ISBI 2016, and 92.5% of PH2 datasets.

Magnetic resonance imaging (MRI) is a powerful tool for non-invasive, high-resolution three-dimensional medical imaging of anatomical structures such as organs and tissues. The use of contrast agents based on gadolinium chelates started in 1988 to improve the quality of the image, since researchers and industry focused their attention on the development of more efficient and stable structures. This review is about the state of the art of MRI contrast agents based on cyclodextrin scaffolds. Chemical engineering strategies are herein reported including host–guest inclusion complexation and covalent linkages. It also offers descriptions of the MRI properties and in vitro and in vivo biomedical applications of these emerging macrostructures. It highlights that these supramolecular associations can improve the image contrast, the sensitivity, and the efficiency of MRI diagnosis by targeting cancer tumors and other diseases with success proving the great potential of this natural macrocycle.

Computed tomography (CT) plays a pivotal role as a diagnostic tool in many diagnostic and diffuse pancreatic diseases. One of the major limits of CT is related to the radiation exposure of young patients undergoing repeated examinations. Besides the standard CT protocol, the most recent technological advances, such as low-voltage acquisitions with high performance X-ray tubes and iterative reconstructions, allow for significant optimization of the protocol with dose reduction. The variety of CT tools are further expanded by the introduction of dual energy: the production of energy-selective images (i.e., virtual monochromatic images) improves the image contrast and lesion detection while the material-selective images (e.g., iodine maps or virtual unenhanced images) are valuable for lesion detection and dose reduction. The perfusion techniques provide diagnostic and prognostic information lesion and parenchymal vascularization and interstitium. Both dual energy and perfusion CT have the potential for pushing the limits of conventional CT from morphological evaluation to quantitative imaging applied to inflammatory and oncological diseases. Advances in post-processing of CT images, such as pancreatic volumetry, texture analysis and radiomics provide relevant information for pancreatic function but also for the diagnosis, management and prognosis of pancreatic neoplasms. Artificial intelligence is promising for optimization of the workflow in qualitative and quantitative analyses. Finally, basic concepts on the role of imaging on screening of pancreatic diseases will be provided.

Objective-coupled surface plasmon microscopy (SPM) shows extremely high similarity with conventional optical microscopy (OM) in both configuration and theoretical model. And there is a common misunderstanding in SPM that all the three polarization components Ex, Ey, and Ez of the focused beam contribute to excitation of SPs, similar as the situation in OM that all the polarization components generate image contrast. Actually, this is not the case and some literatures have suggested `only Ez excites SPs'. However, so far there has been no related theory to support this issue, and the common misunderstanding and corresponding errors are still applied in both academia and industry. The present work clarifies this significant issue for the first time. We theoretically prove that `only Ez excites SPs' and further give two related phenomena as evidences.

Ultraviolet photoacoustic microscopy (UV-PAM) has been investigated to provide label-free and registration-free volumetric histological images for whole organs, offering new insights into complex biological organs. However, because of the high UV absorption of lipids and pigments in tissue, UV-PAM suffers from low image contrast and shallow image depth, hindering its capability for revealing various microstructures in organs. To improve the UV-PAM imaging contrast and imaging depth, here we propose to implement a state-of-the-art optical clearing technique, CUBIC (clear, unobstructed brain/body imaging cocktails and computational analysis), to wash out the lipids and pigments from tissues. Our results show that the UV-PAM imaging contrast and quality can be significantly improved after tissue clearing. With the cleared tissue, multilayers of cell nuclei can also be extracted from time-resolved PA signals. Tissue clearing-enhanced UV-PAM can provide fine details for organ imaging.

Objective: High b-value diffusion-weighted imaging (hDWI) with a b-value of 2000 s/mm2 provides insufficient image contrast between benign and malignant tissues and an overlap of apparent diffusion coefficient (ADC) between Gleason grades (GG) in prostate cancer (PC). We compared image quality, PC detectability, and discrimination ability for PC aggressiveness between ultra-high b-value DWI (uhDWI) of 3000 s/mm2 and hDWI. Methods: The subjects were 49 patients with PC who underwent 3T multiparametric MRI. Single-shot echo-planar DWI was acquired with b-values of 0, 2000, and 3000 s/mm2. Anatomical distortion of prostate (AD), signal intensity of benign prostate (PSI), and lesion conspicuity score (LCS) were assessed using a 4-point scale; and signal-to-noise ratio, contrast-to-noise ratio, and mean ADC (×10–3 mm2/s) of lesion (lADC) and surrounding benign region (bADC) were measured. Results: PSI was significantly lower in uhDWI than in hDWI (p < 0.001). AD, LCS, signal-to-noise ratio, and contrast-to-noise ratio were comparable between uhDWI and hDWI (all p > 0.05). In contrast, lADC was significantly lower than bADC in both uhDWI and hDWI (both p < 0.001). In comparison of lADC between tumors of ≤GG2 and those of ≥GG3, both uhDWI and hDWI showed significant difference (p = 0.007 and p = 0.021, respectively). AUC for separating tumors of ≤GG2 from those of ≥GG3 was 0.731 in hDWI and 0.699 in uhDWI (p = 0.161). Conclusion: uhDWI suppressed background signal better than hDWI, but did not contribute to increased diagnostic performance in PC. Advances in knowledge: Compared with hDWI, uhDWI could not contribute to increased diagnostic performance in PC.

This work introduces an improved ant lion optimizer (ALO), called BIALO, for industrial images. BIALO employs three strategies to improve the performance of the original ALO. First, a novel inertial weight is used to modify the ALO to better balance exploration and exploitation during the process of searching the best solutions. Second, the local search part of the bat algorithm plays an important role in accelerating the algorithm convergence rate. Additionally, the ALO is integrated with invasive weed optimization algorithm to further improve the searching precision. The proposed BIALO is applied to industrial image enhancement, where it acts as an efficient tool that searches for the best parameters in a local/global enhancement transformation. To test the performance of BIALO, we compare it with other metaheuristic algorithms, such as the genetic algorithm, particle swarm optimization, flower pollination algorithm, grasshopper optimization algorithm and the original ALO, on some benchmark industrial images. The experimental results establish that BIALO is able to achieve better outcomes than those of the other algorithms.

To achieve manufacturing and logistics informatization management for steelworks, it is of crucial importance to automatically recognize the slab management numbers (SMNs) sprayed on the steel slabs. However, due to the poor quality of spraying and various interferences, SMN detection is a major challenge for subsequent recognition in the steel-slab product line. This paper proposes a corner-clustering method, which can extract the SMN from a changeable background precisely and promptly. In our method, the FAST algorithm is modified to extract the image corners by adaptively adjusting the local threshold of corner detecting with the change of image contrast. Then, the DBSCAN algorithm is implemented to group the corners into several clusters, which includes the SMN regions and interference regions. Finally, a classifier based on HOG features and SVM is applied to discriminate SMN and non-SMN regions. For experimental validation, the proposed method was implemented to a substantial amount of acquired images. A good performance has been achieved as the detection accuracy can reach as high as 98.96% for SMN on the steel slabs.

The photoacoustic effect relies on optical transmission, which causes thermal expansion and generates acoustic signals. Coherence-based photoacoustic signal processing is often preferred over more traditional signal processing methods due to improved signal-to-noise ratios, imaging depth, and resolution in applications such as cell tracking, blood flow estimation, and imaging. However, these applications lack a theoretical spatial coherence model to support their implementation. In this article, the photoacoustic spatial coherence theory is derived to generate theoretical spatial coherence functions. These theoretical spatial coherence functions are compared with k-Wave simulated data and experimental data from point and circular targets (0.1-12 mm in diameter) with generally good agreement, particularly in the shorter spatial lag region. The derived theory was used to hypothesize and test previously unexplored principles for optimizing photoacoustic short-lag spatial coherence (SLSC) images, including the influence of the incident light profile on photoacoustic spatial coherence functions and associated SLSC image contrast and resolution. Results also confirm previous trends from experimental observations, including changes in SLSC image resolution and contrast as a function of the first M lags summed to create SLSC images. For example, small targets (e.g.,1-4 mm in diameter) can be imaged with larger M values to boost target contrast and resolution, and contrast can be further improved by reducing the illuminating beam to a size that is smaller than the target size. Overall, the presented theory provides a promising foundation to support a variety of coherence-based photoacoustic signal processing methods, and the associated theory-based simulation methods are more straightforward than the existing k-Wave simulation methods for SLSC images.

Представлены результаты исследования зависимостей контраста изображений тестового объекта от оптической плотности различных типов аэрозолей дыма при поляризационной фильтрации излучения подсветки объекта, рассеянного частицами аэрозоля в направлении назад, и в отсутствие фильтрации. Установлено, что формирование изображения объекта с применением метода поляризационной фильтрации излучения помехи обратного рассеяния (ПОР) позволяет снизить скорость уменьшения контраста изображения с увеличением оптической плотности дыма в сравнении с регистрацией изображения без фильтрации ПОР. Полученные результаты могут быть использованы при разработке оптических принадлежностей пожарного-спасателя для наблюдения объектов в неблагоприятных условиях видения: при задымлении, парообразовании, тумане. The relevance of the work is due to the need to develop effective and affordable for mass use technical means of equipping for a firefighter-rescuer, improving the conditions of his work in a smoke-filled environment in a fire. The aim of the work was to study the contrast of the image of a flat black-and-white object with a sharp border of the black-and-white transition formed through various types of smoke aerosols during polarizing filtration of the illumination radiation of the object scattered by aerosol particles in the “back” direction, and in the absence of filtration. The results of the research are presented in the form of dependencies of the object image contrast on the optical density of the smoke aerosol in two image registration schemes, when the receiving optical system is located near the object illumination source at a distance of ~150 mm and when it is located at a distance of ~800 mm from the object illumination source. It is established that the method of forming the image of the object using polarization filtering of radiation backscattering (RBS) reduces the rate of image contrast reduction with an increase in optical smoke density compared with image registration without filtering RBS. The results obtained can be used in the development of optical accessories for firefighter-rescuer to improve the conditions of object observation in adverse conditions of vision: smoke, vaporization, fog.

Purpose To develop a fat‐water imaging method that allows reliable separation of the two tissues, uses established robust reconstruction methods, and requires only one single‐echo acquisition. Theory and Methods The proposed method uses spectrally selective dual‐band excitation in combination with CAIPIRINHA to generate separate images of fat and water simultaneously. Spatially selective excitation without cross‐contamination is made possible by the use of spatial‐spectral pulses. Fat and water images can either be visualized separately, or the fat images can be corrected for chemical shift displacement and, in gradient echo imaging, for chemical shift‐related phase discrepancy, and recombined with water images, generating fat‐water images free of chemical shift effects. Gradient echo and turbo spin echo sequences were developed based on this Simultaneous Multiple Resonance Frequency imaging (SMURF) approach and their performance was assessed at 3Tesla in imaging of the knee, breasts, and abdomen. Results The proposed method generated well‐separated fat and water images with minimal unaliasing artefacts or cross‐excitation, evidenced by the near absence of water signal attributed to the fat image and vice versa. The separation achieved was similar to or better than that using separate acquisitions with water‐ and fat‐saturation or Dixon methods. The recombined fat‐water images provided similar image contrast to conventional images, but the chemical shift effects were eliminated. Conclusion Simultaneous Multiple Resonance Frequency imaging is a robust fat‐water imaging technique that offers a solution to imaging of body regions with significant amounts of fat.

The aim of the work was to study the contrasts of the images of an object observed in a smoky environment, using polarizing filtering of radiation scattered by smoke particles towards the observer, and without filtering. Prospects for developing optical accessories for firefighters to improve the observation of objects in smoke were evaluated by comparing image contrasts.The goal was achieved by experimentally simulating the process of transmitting images of a blackandwhite object with a sharp black/white transition boundary through various types of smoke aerosols using polarizing filtering of radiation scattered by smoke particles, and without filtering and evaluating image contrasts.Studies of image contrasts for different optical densities of smoke in two registration schemes were performed, when the receiving optical system is located near the illumination source of the object at a distance of ≈ 150 mm from it, and when it is located at a distance from the illumination source of the object at a distance of ≈ 800 mm.It is established that the method of forming the image of the object using polarization filtering of radiation backscattering (RBS) reduces the rate of image contrast reduction with an increase in optical smoke density compared to image registration without filtering (RBS).A significant difference in the contrasts of images recorded with filtration (RBS) and in the absence of it is observed for "light" fumes (smoldering of wood, cotton) at average optical densities of smoke.The results obtained can be used in the development of optical accessories for firefighter-rescuer to improve the conditions of observation of objects in adverse conditions of vision: smoke, vaporization, fog.

Image captured in bad weather conditions confines scene prominence, appears grey and diminishes image contrast. This usually happens due to atmospheric dispersing phenomenon that affects the qualit...