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In the process of digital image acquisition, color inconsistency is a common issue due to the influence of photographic conditions. The inconsistency of color affects the quality of digital orthophoto products and subsequent applications significantly, and this issue remains challenging both in the academia and industry. This paper proposes a color equalization method for aerial image based on block adjustment and an improved color balance model based on the analysis of causes for color deviation. Two nonlinear functions are used to simulate the process of color deviation and correct the color of images. Due to the existence of error, the color corrected images still have minor deviations in the color of the overlapped regions. Therefore, this research introduces an error equation to address this issue and uses the least square principle to calculate the adjustment. The simulation experiments and real experiments are carried out on several groups of aerial image data respectively. The results show that the method not only achieves better visual effect but also has a higher adjustment precision. Finally, the rationality and parameters of the model proposed in this paper are discussed.

Background In recent years, the development of digital imaging technology has had a significant influence in liver surgery. The ability to obtain a 3-dimensional (3D) visualization of the liver anatomy has provided surgery with virtual reality of simulation 3D computer models, 3D printing models and more recently holograms and augmented reality (when virtual reality knowledge is superimposed onto reality). In addition, the utilization of real-time fluorescent imaging techniques based on indocyanine green (ICG) uptake allows clinicians to precisely delineate the liver anatomy and/or tumors within the parenchyma, applying the knowledge obtained preoperatively through digital imaging. The combination of both has transformed the abstract thinking until now based on 2D imaging into a 3D preoperative conception (virtual reality), enhanced with real-time visualization of the fluorescent liver structures, effectively facilitating intraoperative navigated liver surgery (augmented reality). Data sources A literature search was performed from inception until January 2021 in MEDLINE (PubMed), Embase, Cochrane library and database for systematic reviews (CDSR), Google Scholar, and National Institute for Health and Clinical Excellence (NICE) databases. Results Fifty-one pertinent articles were retrieved and included. The different types of digital imaging technologies and the real-time navigated liver surgery were estimated and compared. Conclusions ICG fluorescent imaging techniques can contribute essentially to the real-time definition of liver segments; as a result, precise hepatic resection can be guided by the presence of fluorescence. Furthermore, 3D models can help essentially to further advancing of precision in hepatic surgery by permitting estimation of liver volume and functional liver remnant, delineation of resection lines along the liver segments and evaluation of tumor margins. In liver transplantation and especially in living donor liver transplantation (LDLT), 3D printed models of the donor's liver and models of the recipient's hilar anatomy can contribute further to improving the results. In particular, pediatric LDLT abdominal cavity models can help to manage the largest challenge of this procedure, namely large-for-size syndrome.

Objectives: The present study aimed to assess the accuracy of linear measurements performed with two different CBCT software .(Romexis® software and ONDEMAND 3 D software). Methods: The sample consisted of 15 human mandibles marked with radiopaque markers of gutta-percha balls (size 80, 1.5 mm long) which were glued on the marked anatomical landmarks with cyanoacrylate gel, ten linear measurements were conducted on the dry mandibles using digital caliper to provide gold standard meaurments.The mandibles were scanned by using Planmeca ProMax 3D Mid and images exported into digital imaging and communication in medicine (DICOM) format to a DVD then imported in to two software programs.The same ten linear measurements were conducted on the scanned images by using measure length tool of both romexis and Ondemand software programs. Results: Intra-examiner reliability of gold standard and image measurements were found to be excellent, Inter-examiner reliability of physical and Romexis® software was found to be excellent (ICC>0.9) and the Inter-examiner reliability of physical and ONDEMAND 3 D software was found to be poor (ICC

In this study, an image-based measurement system was developed for human facial skin colour, involving the development of a digital imaging system, collection of facial skin colour from 60 human subjects, generation of different colour characterization models, and performance evaluation. The factors that affect facial skin colour characterization, including different training datasets (two colour charts and the collected facial skin colour dataset), mathematical mapping methods (linear transformation, polynomial regression, root-polynomial regression and neural network) and camera image formats (JPG and RAW), were investigated and quantified not only by the conventional method of CIELAB colour difference, but also two newly introduced measures, facial colour contrast and skin colour gamut. The results indicate that the RAW image format for camera digital signals gave a more stable performance than the JPG format images, and the higher order polynomial regression with good predictive accuracy in terms of CIELAB colour difference did not perform well for the whole facial image. It is suggested to evaluate the model performance using the colour of both specific facial positions and the overall facial skin colour. Our comparative analysis in this study provides useful guidance for determining the colour characterization model for facial skin.

Blur detection (BD) is an important and challenging task in digital imaging and computer vision applications. Accurate segmentation of homogenous smooth and blur regions, low-contrast focal regions, missing patches, and background clutter, without having any prior information about the blur, are the fundamental challenges of BD. Previous work on BD has emphasized much effort on designing local sharpness metric maps from the images. However, the smooth/blurred regions having the same patterns as sharp regions make them problematic. This paper presents a robust novel method to extract the local metric map for blurred and nonblurred regions based on multisequential deviated patterns (MSDPs). Unlike the preceding, MSDP extracts the local sharpness metric map on the images at multiple scales using different adaptive thresholds to overcome the problems of smooth/blur regions and missing patches. By using the integral values of the image along with image masking and Otsu thresholding, highly accurate segmented regions of the images are acquired. We argue/hypothesize that the local sharpness map extraction by using direct integral information of the image is highly affected by the threshold selected for distinction between the regions, whereas MSDP feature extraction overcomes the limitations substantially by using automatic threshold computation over multiple scales of the images. Moreover, the proposed method extracts the relatively accurate sharp regions from the high-dense blur and noisy images. Experiments are conducted on two commonly used SHI and DUT datasets for blur and sharp region classifications. The results indicate the effectiveness of the proposed method in terms of sharp segmented regions. Experimental results of qualitative and quantitative comparisons of the proposed method with ten comparative methods demonstrate the superiority of our method. Moreover, the proposed method is also computationally efficient over state-of-the-art methods.

Presentamos un acercamiento a los escenarios educativos con TIC en la actualidad, en concreto tecnologías portables y modelos pedagógicos de colaboración complementarios a ellas, propios del constructivismo. La capacidad de aprendizaje que estos pueden llegar a suscitar, en cualquier momento y en cualquier lugar, refleja una realidad tangible en nuestra sociedad móvil. Nos referimos a los PLE Entornos Personales de Aprendizaje, BYOD Bring Your Own Device, la Gamificación, Flipped Classroom y la Realidad Aumentada y la Realidad Virtual. Se realiza una revisión de literatura de propuestas pedagógicas destacables, a través de algunos de los principales actores que las abordan en España. En cada apartado describimos las características definitorias, atendiendo a los aspectos fuertes y débiles de su praxis educativa. Una propuesta para reflexionar y una invitación a la implementación de la tecnología móvil en las aulas del siglo XXI, donde las imágenes son protagonistas., We present an approach to educational scenarios with ICT today, specifically portable technologies and complementary collaborative pedagogical models, typical of constructivism. The learning capacity they bring, at any time and in any place, reflects a tangible reality in our mobile society. We refer to PLE Personal Learning Environments, BYOD Bring Your Own Device, Gamification, Flipped Classroom and Augmented Reality and Virtual Reality. A literature review of pedagogical proposals is carried out, through some of the main actors who address them in Spain. In each section we describe the defining characteristics, focusing on the strengths and weaknesses of their educational practice. A proposal for reflection and an invitation to implement mobile technology in 21st century classrooms, where images are the protagonists., Grupo de Investigación HUM-672 AREA (Análisis de la Realidad EducativA)

The digital imaging and vision applications in science (DIVAS) program was built to improve the computational self-efficacy and skill of first- and second-year college students majoring in biological and chemical sciences. Our three-year pilot study showed that the program could be successful in both fronts. The scholars, faculty, and staff who participated formed a community of practice that became the heart of the DIVAS program. Through this community, we expanded access to the image processing workshop in collaboration with The Carpentries, supported faculty and secondary educators in developing computing modules for their classrooms, and created and staffed a “writing center for computing” on the host campus. Overall, the DIVAS program has sparked a local computing culture. DIVAS interventions and resources are freely available for adoption by other institutions. We hope to grow the community in a way that builds student access and opportunities and supports educators in the process.

We introduce a novel discrete cosine transform-based feature (DCTF) descriptor designed for both robustly matching features in aerial video and tracking features across wide-baseline oblique views in aerial wide area motion imagery (WAMI). Our DCTF descriptor preserves local structure more compactly in the frequency domain by utilizing the mathematical properties of the discrete cosine transform (DCT) and outperforms widely used the spatial-domain feature extraction methods, such as speeded up robust features (SURF) and scale-invariant feature transform (SIFT). The DCTF descriptor can be used in combination with other feature detectors, such as SURF and features from accelerated segment test (FAST), for which we provide experimental results. The performance of DCTF for image matching and feature tracking is evaluated on two city-scale aerial WAMI data sets (ABQ-215 and LA-351) and a synthetic aerial drone video data set digital imaging and remote sensing image generation (Rochester Institute of Technology (RIT)-DIRSIG). DCTF is a compact 120-D descriptor that is less than half the dimensionality of state-of-the-art deep learning-based approaches, such as SuperPoint, LF-Net, and DeepCompare, which requires no learning and is domain-independent. Despite its small size, the DCTF descriptor surprisingly produces the highest image matching accuracies ( $F_{1} = 0.76$ and ABQ-215), the longest maximum and average feature track lengths, and the lowest tracking error (0.3 pixel, LA-351) compared with both handcrafted and deep learning features.

Digital Imaging and Communication (Diacom) is a medical image format used in telemedicine. Telemedicine is the core of the modern age. A physician in one area uses a telecommunications device to provide a patient with treatment at a distant site. It's a substitute horizon that the globe publicly pays attention to. Throughout telemedicine sessions, access to personal patient health records and health trackers is on the market, helping you diagnose and efficiently manage patients. It is the main task to reduce each measure of storage and transmission data required for communication. Diacom Image Compression (DIC) is primarily based on the new Efficient Extensible Interchange (EXI) mechanism to realize this goal. Efficient Extensible Interchange is a very lightweight, high-performance XML representation that was designed to function well in a wide variety of applications. It boosts performance while slashing bandwidth requirements, all while preserving other resources including battery life, code size, computing power, and memory. The proposed work focuses on advance compression methodology for patient’s historical data, electronic medical records of clinical information, and diagnostic test record. The results of the simulation recommends that the outcome of the proposed mechanism is better than the current approach. The present work compares the proposed methods with existing methodology and experimental analysis depicts that EXI based compression methodology is far better than traditional SVD compression methodology.

Integrating information collected by different types of sensors observing the same or related phenomenon can lead to more accurate and robust decision making. The purpose of this article is to review sensor fusion approaches to achieve passive radio frequency (RF) and electro-optical (EO) sensor fusion and to present the proposed fusion of EO/RF neural network (FERNN). While research has been conducted to integrate complementary data collected by EO and RF modalities, the processing of RF data usually applies traditional features, such as Doppler. This article explores the viability of using the histogram of I/Q (in-phase and quadrature) data for the purposes of augmenting the detection accuracy that EO input alone is incapable of achieving. Specifically, by processing the histogram of I/Q data via deep learning and enhancing feature input for neural network fusion. Using the simulated data from the Digital Imaging and Remote Sensing Image Generation dataset, FERNN can achieve 95% accuracy in vehicle detection and scenario categorization, which is a 23% improvement over the accuracy achieved by a stand-alone EO sensor.

High Resolution Transmission Electron Microscopy (HRTEM) has been a widely used technique for the analysis of nanostructure of turbostratic carbon. Since the late nineties, digital imaging and image analysis has made it possible to quantify and derive structural properties from HRTEM images. A number of image analysis methodologies have been proposed, based on which numerous studies drew their conclusions about a variety of materials including soot, carbon black, char, black carbon, carbon nano-onions, fullerenes, pyrocarbon, hard carbon, etc. Recognizing the need for standardization in order to make measurements comparable, repeatable and reproducible, this study is a review of published techniques and a critical analysis of a representative set of them. Based on sensitivity analysis using an extensive dataset of micrographs, best practices for image acquisition and image analysis are provided. Measurement uncertainties and sensitivities to image analysis parameters are estimated. A set of improvements to the state-of-the-art is proposed. Computer code for the presented methods is made available.

In development of technology and product, it’s important to make decisions based on technology trend. Some previous studies obtained technology trend based on patent information. In our previous study, we tried to investigate the state of innovation and the emergence of dominant design by using patent information. We analyzed inkjet printers and NC machines with theme codes and F term codes in Japanese patent classification codes. In this study, we tried to know the change of technology of other products other than inkjet printers or NC machines by using patent information. The target product of this study is camera. Camera products changed its key technology of imaging and recording from analogue film to digital imaging sensor and semiconductor memories. We attempted to obtain this change by using patent information.

One of the most important developments in the field of radiology is that the imaging process has started to take place in the digital environment. The advantages of digital radiography increase the interest of dentists to digital radiography, and nowadays digital systems are replacing conventional systems. The most important reasons for the increase of using the digital systems are the fast access to the image, the quality of the image, and the easy storage and transmission of the image. Nowadays, the clinicians can digitize anything and recall it with the patient. This article presents a review about digital imaging and dental record. The history of digital imaging, direct and indirect digital imaging methods, digital sensors and dental record were reviewed.

The objective of this study was to evaluate the performances of the automated digital imaging of Gram-stained slides against manual microscopy. Four hundred forty-three identified Gram-stained slides were included in this study. When both methods agreed, we considered the results as correct, and no further examination was carried out. Whenever the methods gave discrepant results, we reviewed the digital images and the glass slides by manual microscopy to avoid incorrectly read smears. The final result was a consensus of multiple independent reader interpretations. Among the 443 slides analyzed in this study, 101 (22.8%) showed discrepant results between the compared methods. The rates of discrepant results according to the specimen types were 5.7% (9/157) for positive blood cultures, 42% (60/142) for respiratory tract specimens, and 22% (32/144) for sterile site specimens. After a subsequent review of the discrepant slides, the final rate of discrepancies dropped to 7.0% (31/443). The overall agreement between the compared methods and the culture results reached 78% (345/443) and 79% (349/443) for manual microscopy and automated digital imaging, respectively. According to culture results, the specificity for automated digital imaging and manual microscopy were 90.8% and 87.7% respectively. In contrast, sensitivity was 84.1% for the two compared methods. The discrepant results were mostly encountered with microorganism morphologies of rare occurrence. The results reported in this study emphasize that on-screen reading is challenging, since the recognition of morphologies on-screen can appear different as compared to routine manual microscopy. Monitoring of Gram stain errors, which is facilitated by automated digital imaging, remains crucial for the quality control of reported Gram stain results.

Delaying effective antibiotic therapy is a major cause of sepsis-associated mortality. The EUCAST rapid antibiotic susceptibility test (RAST) is performed from positive blood cultures to provide rapid results. Disc diffusion tests inoculated with positive blood culture broth are read at 4, 6, and 8 h and interpreted against species and time-specific criteria. Potential problems are the possibility of missing specific reading times for tests and slower growth in incubators that are frequently opened. The current study aimed to assess if digital visualization by the BD Kiestra™ total laboratory automation system is suitable for reading RASTs by capturing images at the correct times and retaining them for review. Utilizing the Kiestra™ InoqulA, 100 μl of positive blood culture broth was lawn-inoculated onto Mueller-Hinton agar and incubated at 35 °C for automated digital zone measurement at 4, 6, and 8 h. Aliquots from 135 positive blood cultures were tested against EUCAST-recommended and other drugs and assessed for readability of digital images. Microdilution MICs were determined in parallel to RASTs. All isolates except 7/10 enterococci yielded images of suitable quality for zone measurement. Of the 641 digitally read tests for other organisms, 207 (32.3%) were readable in 4 h, 555 (86.6%) in 6 h, and 641 (100%) in 8 h. For tests included in EUCAST criteria, 92.1% provided categorical agreement with microdilution MICs. Digital image reading of RASTs is a potentially viable, inexpensive tool for providing rapid susceptibility results which can help reduce sepsis-associated mortality.

The quality of cashmere, such as color and length, determines its price and application. In the current cashmere inspection system, color and length are tested by visual assessment, which is a subjective, time- and labor-consuming process. Herein, the goal of this research is to develop a new method of testing cashmere color using image analysis, and to study the application of color in length measurement. During the color measurement, cashmere was prepared under two sample placement methods, and color features including RGB, XYZ and Lab obtained by the new method were compared with the standard. The calculation method of optical index used in length testing was determined based on theoretical and experimental analysis. Experiments show that fixed weight and pressure are suited for cashmere color measurement. In RGB space, the correlation coefficients ( R2) between the two devices were calculated and were 0.990, 0.995 and 0.996 for parameters R, G and B, respectively. Good agreement also exhibited in XYZ space, with R2 equal to 0.994, 0.996 and 0.999 for X, Y and Z, respectively. This confirmed the accuracy of the proposed color measurement method in RGB and XYZ space. Finally, an accurate fibrogram was obtained by the proposed conversion model for calculating optical index from color values, which is the key curve to testing cashmere length. This study emphasis on methodological aspects and the results acquired are regarded as preliminary, as the experiments studied compose the first stage of research on the exploration of the application of image analysis on cashmere color measurement.

We discuss computer simulations of a particle that hops forward and backward randomly on a one-dimensional lattice. To track the motion of the particle, we use a data acquisition protocol that mimics a virtual digital optical imaging system. Plots of the data extracted from these images yield a pattern that is similar to the patterns observed in real imaging of a moving particle in a living cell. To explore the physical origin of the observed patterns, we use different spatio-temporal resolutions and show that the characteristics of the observed patterns are determined by the spatial and temporal resolutions of the digital imaging device. The simulations help not only to determine the origin of these patterns in the imaging data, but also to avoid misinterpreting the imaging data in terms of a special property of the particle or its dynamics.

Color and pigment analyses are not required by the majority of olive oil marketing standards. However, it is a basic attribute that is highly associated, by most consumers, with the idea of quality. In this study, we aim at providing a fast non-invasive method for routine analysis that can be used on a large scale in the laboratories of olive oil analysis starting from pigment quantification and color range measurements. A selection of 172 virgin olive oil samples obtained in Tunisia between 2018 and 2019 were used for this purpose. Chlorophyll and carotenoid contents were analyzed using a UV spectrophotometer standard method while color range was measured using digital images taken under controlled conditions. All samples showed high significant differences in chlorophyll and carotenoid contents (p

Microfluidic paper-based analytical devices are emerging as promising options for on-the-spot detection of chemical contaminants in water. The coupling of these devices with digital imaging technol...

Purpose Existing phantom-less quality assurance (QA) platforms does not provide patient-specific QA for helical tomotherapy (HT). A new system, called TomoEQA, is presented to facilitate this using the leaf open time (LOT) of a binary multi-leaf collimator, as measured by an exit detector. Methods TomoEQA was designed to provide measurement-based LOTs based on detector data and to generate a new digital imaging and communication in medicine (DICOM) dataset that includes the measured LOTs for use by secondary check platforms. To evaluate the system, 20 patient-specific QAs were performed using the program in Mobius3D software, and the results were compared to conventional phantom-based QA results. Results From our assessment, most of the differences between the planned and measured (or calculated) data, excluding one case, were within the acceptance criteria comparing with those of conventional QA. Regarding the gamma analysis, all results considered in this study were within the acceptance criteria. In addition, the developed system was performed for a failed case and showed approximately the same trends as the conventional approach. Conclusions TomoEQA could perform patient-specific QAs of HT using Mobius3D and provide reliable patient-specific QAs results by evaluating point dose errors and 3D gamma passing rates. TomoEQA could also distinguish whether an intensity-modulated radiation therapy plan failed or not.

In order for enterprise imaging to be successful across a multitude of specialties, systems, and sites, standards are essential to categorize and classify imaging data. The HIMSS-SIIM Enterprise Imaging Community believes that the Digital Imaging Communications in Medicine (DICOM) Anatomic Region Sequence, or its equivalent in other data standards, is a vital data element for this role, when populated with standard coded values. We believe that labeling images with standard Anatomic Region Sequence codes will enhance the user’s ability to consume data, facilitate interoperability, and allow greater control of privacy. Image consumption—when a user views a patient’s images, he or she often wants to see relevant comparison images of the same lesion or anatomic region for the same patient automatically presented. Relevant comparison images may have been acquired from a variety of modalities and specialties. The Anatomic Region Sequence data element provides a basis to allow for efficient comparison in both instances. Interoperability—as patients move between health care systems, it is important to minimize friction for data transfer. Health care providers and facilities need to be able to consume and review the increasingly large and complex volume of data efficiently. The use of Anatomic Region Sequence, or its equivalent, populated with standard values enables seamless interoperability of imaging data regardless of whether images are used within a site or across different sites and systems. Privacy—as more visible light photographs are integrated into electronic systems, it becomes apparent that some images may need to be sequestered. Although additional work is needed to protect sensitive images, standard coded values in Anatomic Region Sequence support the identification of potentially sensitive images, enable facilities to create access control policies, and can be used as an interim surrogate for more sophisticated rule-based or attribute-based access control mechanisms. To satisfy such use cases, the HIMSS-SIIM Enterprise Imaging Community encourages the use of a pre-existing body part ontology. Through this white paper, we will identify potential challenges in employing this standard and provide potential solutions for these challenges.

Melanoma is the most life threatening type of cancer which contributes to the highest mortality rate. Early detection of melanoma facilitates better prognosis and increases survival rates. High infiltration of melanoma and advanced digital imaging technologies have exhilarated concern among the public, calling for initial screenings. However, melanoma screening is considered as a non trivial problem even by expert medical practitioners, in spite of several diagnostic algorithms. There is an enthralling need for automated melanoma detection systems due to the surge in the melanoma population and lack of trained dermatologists. Computational models for Melanoma detection are based on learning from the Region of Interest (RoI). Nevertheless, identification of RoI itself poses several challenges due to the diverse structural and chromatic features on the surface of the skin. This paper proposes a superpixel region growing based approach for segmentation of the melanoma region for further analysis. It is based on the Gaussian Mixture Model superpixels which segment the candidate image into accurate homogenous regions. The superiority of the system is demonstrated with performance metrics and comparisons on a standard dataset. This system is an impending solution to perform melanoma screenings with ease.

Noise is regarded as an unavoidable component of digital image acquisition. Hence, noise removal has been considered as one of the fundamental tasks in the field of image processing. Accordingly, excellent results have been achieved by using second-order models. However, these outcomes are affected by the staircase effect. To eliminate this anomaly and maintaining the balance of removing noise and preserving edges, a fourth-order model is proposed. The existence and uniqueness of the entropy solution for this model are established. Besides, to to verify the effectiveness of the model in noise removal, we carried out numerical experiment and presented our results. Indeed, the experimental results show that our model is superior to PM model and ROF model in terms of removing noise and preserving edges.

Harvest time is an important factor affecting grain yield and postharvest quality. The estimation of crop maturity greatly supports farmers in the optimization of the harvest time. This study proposed a method for predicting paddy rice maturity (J816 and 5Y4 varieties) using color features and the random forest (RF) regression algorithm. Paddy panicle images were obtained using a flatbed scanner during the maturation period. To estimate paddy rice maturity, 22 color features representing the greenness of crop leaves were extracted from the paddy panicle images. Stepwise regression was used to select superior features as inputs to the RF regression model. The coefficient of determination (R2) and root mean square error (RMSE) values of the model were 0.93 and 1.18% for J816 and 0.94 and 1.60% for 5Y4, respectively. The results indicate that the proposed method in this study is a promising technique for the estimation of paddy rice maturity.

The enormous diffusion of Time-Mode circuits, in particular Time-to-Digital Converter (TDC) time measurement circuits, and at the same time the dizzying increase in parallel channels required by the most recent applications, for example in the automotive and digital imaging fields, brings the problem of electromagnetic interference between channels ever more to the fore. This phenomenon, generally known as Cross-Talk (XT), is particularly critical given the increase in the operating frequency and density of systems components, and its effect on the timing parameters in TDC measurements is investigated. Considering the time measurements, XT creates temporal shift on the physical events from which the timestamps are extracted; in this manner, an error in the measurements is generated. In order to detect the XT phenomena, a methodical analysis based on Code-Density Test (CDT) is performed; in this terms, two different typologies of XTs are investigated, which are correlated and uncorrelated XT. Furthermore, a TDC board is used as case study and all the XT sources are detected and classified. Thus, a classification of the importance of the different sources of XT is achieved and a solution to minimize the different causes is proposed.

Knowing the nitrogen status of crop is essential for agricultural operations management, nevertheless conventional methods are time consuming and expensive. In this research, possibility of using digital aerial images as a remote sensing method to determine the nitrogen content of sugarcane plant was studied. Arial images were captured from 3 sugarcane fields using a 12.9-megapixel digital camera mounted on a Phantom 3 quad-copter from 5 and 10 m heights. At the same time, four healthy top branches of sugarcane plants were cut from imaging points as plant samples. The nitrogen value of the samples was measured using Kjeldahl test at laboratory. Multilayer perceptron (MLP) artificial neural network (ANN) algorithm was used to estimate nitrogen status in the crop from the aerial digital images. Color indices of images were extracted using image processing in MATLAB software and their correlation with the nitrogen value were determined. The indices that had correlation with nitrogen were selected as inputs of the ANNs and the nitrogen value was the output. There was no significant difference between the nitrogen values predicted by ANNs and its actual values. The average errors of the ANNs training were 0.145 and 0.022 and the correlation coefficients of the predicted and actual values of nitrogen were 0.89 and 0.94, for 5 m and 10 m heights respectively. Also, the RMSE values of nitrogen estimation was 0.181 and 0.174, at 5 m and 10 m heights respectively. So, nitrogen estimation of sugarcane fields is possible by aerial digital imaging.

In this study, wet air with ash particles was used to simulate real dryer exhaust from lignite-drying process, and its ash deposition characteristics along horizontal tube bundles were experimental...

Counting mitotic figures (MF) in hematoxylin and eosin–stained histologic sections is an integral part of the diagnostic pathologist’s tumor evaluation. The mitotic count (MC) is used alone or as part of a grading scheme for assessment of prognosis and clinical decisions. Determining MCs is subjective, somewhat laborious, and has interobserver variation. Proposals for standardizing this parameter in the veterinary field are limited to terminology (use of the term MC) and area (MC is counted in an area measuring 2.37 mm2). Digital imaging techniques are now commonplace and widely used among veterinary pathologists, and field of view area can be easily calculated with digital imaging software. In addition to standardizing the methods of counting MF, the morphologic characteristics of MF and distinguishing atypical mitotic figures (AMF) versus mitotic-like figures (MLF) need to be defined. This article provides morphologic criteria for MF identification and for distinguishing normal phases of MF from AMF and MLF. Pertinent features of digital microscopy and application of computational pathology (CPATH) methods are discussed. Correct identification of MF will improve MC consistency, reproducibility, and accuracy obtained from manual (glass slide or whole-slide imaging) and CPATH approaches.

Because of recent advances in digital imaging techniques, the use of these techniques has become popular in unconventional reservoirs. Current images can disclose information at very high resolutio...

Liquid marbles are a promising microreactor platform that recently attracts significant research interest owing to their ability to accommodate a wide range of micro reactions. However, the use of destructive and ex-situ methods to monitor reactions impairs the potential of liquid-marble-based microreactors. This paper proposes a non-destructive, in situ, and cost-effective digital-imaging-based colourimetric monitoring method for transparent liquid marbles, using the enzymatic hydrolysis of starch as an illustrative example. The colourimetric reaction between starch and iodine produces a complex that exhibits a dark blue colour. We found that the absorbance of red channel of digital images showed a linear relationship with starch concentration with high sensitivity and repeatability. This digital-imaging-based colourimetric method was used to study the hydrolysis of starch by α-amylase. The results show high accuracy and applicability of first-order kinetics for this reaction. The demonstration of digital-imaging-based colourimetry indicates the potential of liquid marble-based microreactors.

The explosive growth of digital imaging, especially in the fields of medicine, education, and e-commerce, has made data maintenance and transmission over networks a daunting task. Therefore, the development and use of image compression techniques have become vital for overcoming the problems of storage and transmission of digital image data. Two methods that are extensively used for data compression are Discrete Cosine Transformation and Discrete Wavelet Transform (DWT). In our present study, we have shown the benefits of a DWT-based approach by utilizing the canonical Huffman coding as an entropy encoder. DWT decomposes the image into different sub-bands. These sub bands are known as approximate image and detail images. The approximate image is normalized in the range (0, 1) for obtaining the Canonical Huffman coding bit stream. In a similar way, details coefficients are also normalized in the range (0, 1) for obtaining the canonical Huffman coding bit stream of detail images. Hard thresholding is often used to discard insignificant coefficients of detail images. Our proposed method takes less computing time and has a smaller codebook size than that of conventional Huffman coding. Moreover, the results show an improvement over Wavelet Scalar Quantization often used for image compression of fingerprints. We have applied our method to various popular images and obtained promising PSNR, CR, and BPP that highlight the advantages of our approach and the efficiency of our algorithms.

Raw data acquired by imaging devices are converted into digital images by post-processing algorithms. However, these algorithms are significantly affected by numerous illumination conditions. Particularly, in the case in which illumination conditions depend on canonical light sources, unwanted light sources that locally illuminate the scene or mixed light from several light sources are recognized as the spatially varying illumination conditions. These complex illumination conditions cause several artifacts by affecting the digital image acquisition process. For example, optical aberrations are generated by refraction of complex light, or illumination estimation is significantly affected by the different color temperatures of multiple light sources. To overcome these problems, this study proposes an algorithm that decomposes the complex illumination from several light sources. First, the spatially varying illumination condition is discussed, and the artifacts generated by the conditions, such as false colors and aberrations, are analyzed. Second, mixed light from several canonical light sources is decomposed based on the imaging devices and spectral information of the canonical light sources. The proposed method has low complexity and increased applicability. Furthermore, the improvement scheme based on the proposed method has a parallelized structure and can be easily applied to various types of algorithms, such as color constancy, deconvolution, denoising, and contrast enhancement. The algorithms employing the improvement scheme show the potential of the proposed method for solving the problems associated with multiple light sources.

Because infections with pathogenic bacteria entering the mammary gland through the teat canal are the most common cause of mastitis in dairy cows, sustaining the integrity of the teat canal and its adjacent tissues is critical to resist infection. The ability to monitor teat tissue condition is therefore a key prerequisite for udder health management in dairy cows. However, to date, routine assessment of teat-end condition is limited to cow-side visual inspection, making the evaluation a time-consuming and expensive process. Here, we illustrate and demonstrate a method for assessing teat-end condition of dairy cows through digital images and software. A digital workflow has been designed where images of dairy cow teats are obtained and processed to display individual teats, and the cow and teat images are labeled and displayed through a graphical user interface. The interface then allows an evaluator to assess quarter- and cow-level teat-end condition and store the results for review and future analysis. The digital workflow permits several advantages such as the ability to perform remote teat-end condition assessments, and assess inter- and intrarater variability of teat-end condition scoring. We demonstrate the image-based teat-end condition assessment of 194 dairy cows that also had cow-side teat-end condition assessments by 2 expert evaluators. Weighted Cohen's kappa statistic (κ) was computed to measure the evaluators' concordance of categorical scores of quarter- and cow-level assessments when using cow-side and image-based assessments. Substantial agreement (0.61 ≤ κ ≤ 0.80) was observed between an evaluator's cow-side and image-based assessments at the quarter and cow level. Moderate agreement (0.41 ≤ κ ≤ 0.60) was observed between evaluators when using image-based assessments at the quarter and cow level. Near perfect agreement (κ = 0.89, 95% confidence interval 0.78-1.00) was observed between evaluators when using cow-side assessments at the quarter level, and substantial agreement (κ = 0.66, 95% confidence interval 0.53-0.79) was observed when using cow-side assessments at the cow level. This suggests that image-based teat-end condition classification is possible, and coupled with improvements in image acquisition and image processing, this method can be used to assess teat-end condition in a systematic and convenient manner.

The principle of operation of a detector that belongs to a new class is described. This detector, whose operation is based on recording ultraweak radiation from radioluminescence in an aqueous phantom, is designed for simulating proton therapy sessions at the Prometheus accelerator. The developed new detector is a digital imaging detector featuring a special electronic path. It is intended for applications in nuclear- and particle-physics experiments, together with traditional detectors. The principle for constructing a control scheme for the addition of pixels of the adaptive electronic path of the digital imaging detector is considered. The experimental measurements of the Bragg peak depth with an air ionization camera and the digital imaging detector are compared at the proton energies of 50, 100, and 170 MeV. The Bragg peak depths measured with a certified PPC-40 air camera and the digital imaging detector are found to agree within the error allowed by the IAEA requirements.

Patients' exposure to medical and dental radiographic examination has increased over the years,1 with dental X-ray procedures now accounting for almost one-third of all radiographic examinations.2 Although they only contribute 2-4% towards the collective effective dose of exposure, all efforts should be made to minimize the amount taken and to keep exposure as low as diagnostically achievable.2 When considering radiographic examinations, the potential diagnostic or therapeutic benefits to the individual or society need to be weighed up against the possible risks that the exposure may cause, taking into account the "efficacy, and benefits and risks of alternative techniques that have the same objectives but involve no or less radiation".2,3 To this end the acronym ALARA was coined to stress that all diagnostic radiographs should aim to keep doses as low as reasonably achievable without compromising the diagnosis.1 With the advent of digital imaging there has been a trade-off between image quality and reduced radiation dosage. As such the term has been altered to ALADA, as low as diagnostically acceptable, to reflect this compromise.2

Multiparametric MRI represents the primary imaging modality to assess diffuse liver disease, both in a qualitative and in a quantitative manner. Diffusion-weighted imaging (DWI) is among the imaging techniques that can be used to assess fibrosis due to its unique capability to assess microstructural changes at the tissue level. DWI is based on water mobility patterns and has the potential to become a non-invasive and non-destructive virtual biopsy to assess diffuse liver disease, overcoming sampling bias errors due to its three-dimensional imaging capabilities. Parallel to DWI, another quantitative method called texture analysis may be used to assess early and advanced diffused liver disease through quantifying spatial relationships in a global and local level, applying to any type of digital imaging technique like MRI or CT. Initial results using texture analysis hold great promise. In the current paper, we will review the role of DWI and texture analysis using MR images in assessing diffuse liver disease.

Digital imaging and medical reporting have acquired an essential role in healthcare, but the main challenge is the storage of a high volume of patient data. Although newer technologies are already introduced in the medical sciences to save records size, Big Data provides advancements by storing a large amount of data to improve the efficiency and quality of patient treatment with better care. It provides intelligent automation capabilities to reduce errors than manual inputs. Large numbers of research papers on big data in the medical field are studied and analyzed for their impacts, benefits, and applications. Big data has great potential to support the digitalization of all medical and clinical records and then save the entire data regarding the medical history of an individual or a group. This paper discusses big data usage for various industries and sectors. Finally, 12 significant applications for the medical field by the implementation of big data are identified and studied with a brief description. This technology can be gainfully used to extract useful information from the available data by analyzing and managing them through a combination of hardware and software. With technological advancement, big data provides health-related information for millions of patient-related to life issues such as lab tests reporting, clinical narratives, demographics, prescription, medical diagnosis, and related documentation. Thus, Big Data is essential in developing a better yet efficient analysis and storage healthcare services. The demand for big data applications is increasing due to its capability of handling and analyzing massive data. Not only in the future but even now, Big Data is proving itself as an axiom of storing, developing, analyzing, and providing overall health information to the physicians.

Rapid advances in digital imaging technology offer efficient and cost‐effective methods for measuring seabird abundance, breeding success, phenology, survival and diet. These methods can facilitate understanding of long‐term population trends, and the design and implementation of successful conservation strategies. This paper reviews the suitability of satellites, manned aircraft, unmanned aerial vehicles (UAVs), and fixed‐position, handheld and animal‐borne cameras for recording digital photographs and videos used to measure seabird demographic and behavioural parameters. It considers the disturbance impacts, accuracy of results obtained, cost‐effectiveness and scale of monitoring possible compared with ‘traditional’ fieldworker methods. Given the ease of collecting large amounts of imagery, image processing is an important step in realizing the potential of this technology. The effectiveness of manual, semi‐automated and automated image processing is also reviewed. Satellites, manned aircraft and UAVs have most commonly been used for population counts. Spatial resolution is lowest in satellites, limiting monitoring to large species and those with obvious signs of presence, such as penguins. Conversely, UAVs have the highest spatial resolution, which has allowed fine‐scale measurements of foraging behaviour. Time‐lapse cameras are more cost‐effective for collecting time‐series data such as breeding success and phenology, as human visits are only required infrequently for maintenance. However, the colony of interest must be observable from a single vantage point. Handheld, animal‐borne and motion‐triggered cameras have fewer cost‐effective uses but have provided information on seabird diet, foraging behaviour and nest predation. The last of these has been important for understanding the impact of invasive mammals on seabird breeding success. Advances in automated image analysis are increasing the suitability of digital photography and videography to facilitate and/or replace traditional seabird monitoring methods. Machine‐learning algorithms, such as Pengbot, have allowed rapid identification of birds, although training requires thousands of pre‐annotated photographs. Digital imaging has considerable potential in seabird monitoring, provided that appropriate choices are available for both image capture technology and image processing. These technologies offer opportunities to collect data in remote locations and increase the number of sites monitored. The potential to include such solutions in seabird monitoring and research will develop as the technology evolves, which will be of benefit given funding challenges in monitoring and conservation.

Geometrical description of the egg shape is of a great importance in a variety of studies and can be instrumental in predicting quality traits of table and hatching poultry eggs. Importantly, developments of non-destructive oomorphological models can drive novel insights in engineering and physical science and lead to new egg-related technologies and egg sorting systems for poultry industry. We attempted to test the Hügelschäffer’s egg model according to which an egg profile curve can be transformed from an ellipse using a specific parameter w. For this purpose, two-dimensional digital imaging and follow-up image processing techniques of chicken eggs were employed. The formulae for recalculation of the egg volume and surface area were consequently deduced from the Hügelschäffer’s equation. Eventually, we refined the Hügelschäffer’s egg model and proved its applicability for defining the contours of hen’s eggs. For practical use in poultry industry and food engineering, the proposed non-destructive methodology can be contributory in defining accurately the contour of any avian egg and determining such characteristics of the egg shape as volume, surface area, etc., with an expected potential in designing automated systems in poultry industry and in egg-related applications in biology, physical science, engineering and other areas.

Contrast enhancement has become a vital process in improving the human perception as well as machine perception, due to the enormous application of digital imaging techniques in various fields like medical, aerospace, agriculture, machine vision, automation, surveillance, etc. During contrast enhancement by various existing techniques, the advent of adverse side effects like brightness degradation, intensity saturation and appearance of artifacts in the enhanced images is a critical issue, which needs more research attention. In this paper, an inverted Gaussian histogram specification technique for contrast enhancement of differently skewed images is proposed, in which the pixel concentration on either side of the histogram is increased to overcome the drawbacks in the existing methods. The proposed technique uses parameters like mean, standard deviation and inversion constant to generate various inverted Gaussian specified histograms. Further, transformation and objective functions are used to identify the best desired histogram so as to obtain the best enhanced image for the given image. The performance of the proposed method is compared with other existing techniques on the images taken from various standard databases. The experimental results show that the proposed technique is best suitable for contrast enhancement of images with differently skewed histograms.

Facial weakness is a symptom commonly associated to lack of facial muscle control due to neurological injury. Several diseases are associated with facial weakness such as stroke and Bell's palsy. The use of digital imaging through mobile phones, tablets, personal computers and other devices could provide timely opportunity for detection, which if accurate enough can improve treatment by enabling faster patient triage and recovery progress monitoring. Most of the existing facial weakness detection approaches from static images are based on facial landmarks from which geometric features can be calculated. Landmark-based methods, however, can suffer from inaccuracies in face landmarks localization. In this study, We also experimentally evaluate the performance of several feature extraction methods for measuring facial weakness, including the landmark-based features, as well as intensity-based features on a neurologist-certified dataset that comprises 186 images of normal, 125 images of left facial weakness, and 126 images of right facial weakness. We demonstrate that, for the application of facial weakness detection from single (static) images, approaches that incorporate the Histogram of Oriented Gradients (HoG) features tend to be more accurate.

This study was performed to assess the effectiveness and comfort of two intraoral imaging techniques using respective digital radiograph receptor devices/holder in obtaining digital intraoral images. Methods: A total of 60 patients undergoing anterior intraoral periapical radiographs were single-blindly recruited. The imaging procedure was performed by two calibrated researchers where the novel holder group (Bisector©) was prospectively compared to the paralleling technique group, RINN® by performing thirty radiographic examinations, respectively. All patients were randomly segregated into different groups using block randomization method. The effectiveness of both holders was quantified based on the repeat rate percentage and quality of the images. The comfort study was enumerated using the Horizontal Visual Analogue Scale 100mm (HVAS). The Wilcoxon test (alpha = 0.05) was applied to compare the comfort score of different types of imaging receptor device reported by the patients. Results: The repeat rate percentage for Bisector© and RINN® holder devices were 8.9% and 18.6%, respectively (p

This work presents digital imaging technology as a tool for studying artists’ brushstroke patterns and painting techniques. Digital imaging analysis performed using reflectance transformation imaging (RTI) and three-dimensional (3D) scanning was used to observe the morphological textures of brushstrokes formed on the painted surface; this provided a “digital fingerprint” to indicate an individual artist’s specific characteristics. A model specimen was produced to examine the relationship between paint and painting tools. For painting tools, twelve types of brushes were chosen from the artist’s studio. The results revealed distinctive features among the different types of brushes. The model specimen was then further compared with the artist’s works and a replicated painting was also produced for comparison with one of the original works. The overall result provided information about similar patterns corresponding to the types of brushes the artist used for the model specimen and paintings. Distinctive painting pattern features were detected in the replica, although it was painted using the same material and type of brush.

A colorimetric method based on digital image processing and an image capture device as a detector has been developed for determining methylene blue (MB) in human and veterinary dosage forms. Operational variables, such as capture devices, angle and modes of capture as well as measuring vessel geometry, were evaluated in order to establish the most suitable conditions to obtain accurate measurements. Three different capture devices including a webcam, a compact digital camera and a smartphone camera were compared. Digital images were analyzed using the open source program ImageJ. Suitable results were achieved when using a 20.7 MP back camera of a smartphone and white-LED lighting conditions. Grayscale values were finally employed as response to determine MB concentration. Satisfactory linearity was achieved in the concentration range from 3.5 × 10–7 to 4.7 × 10–6 M, with the determination coefficient R2 > 0.97 and the limit of detection of 1.7 × 10–7 M. The proposed approach was successfully applied to the detection of MB in two commercially available dosage forms for human and veterinary treatment.

Primary goal in successful rehabilitation of a patient with ocular defect is to fabricate a well-fitted ocular prosthesis that mimics the original shape and position of the residual anatomical structures and restores volume of the defective socket. Adequate palpebral opening, iris-scleral visibility, color and size of iris, centering of pupil, shade of sclera, proper fixation of gaze and characterization of prosthesis are the key aspects that deserve meticulous attention. Fixation of gaze and selection of iris size can be achieved by various techniques. Four techniques involving usage of millimeter markings and numerical values with armamentarium such as spectacles with graphic grid, cassette holder with grid, modified graphic grid ruler, RS-3 Ophthalmic pupil distance meter coupled with digital imaging technique for iris selection are highlighted through these case series. Keywords: Gaze, Digital iris, Pupil Distance Meter, Graphic Grid.

Dental health care workers are subjected to various infectious disease agents that are present in patients' saliva and blood which make infection control and universal prevention methods indispensable to the dental practice given that some of these diseases cause loss of work and time for dental staff and patients; some of them cause serious morbidity; and some of them currently have a poor prognosis and no effective treatments. Although dental radiographic procedures are not invasive, and there are no incidents such as injuries caused by dental instruments, they are a potential infection source because of contamination with saliva and blood, and comprehensive infection control procedures also apply to the radiology clinic. In addition, contact with a large number of patients in dental schools and clinics in a short period of time, and the fact that radiographic procedures are performed in the same environment as other dental procedures in dental offices increase the significance of infection control in dental radiology. Major advances in computer technology have enabled digital imaging systems to develop rapidly and to become common in dental practice as an alternative to conventional film-based imaging. The use of digital sensors in dental radiology introduced unique infection control challenges and required the revision and modification of existing infection control techniques. In this review, studies concerning the risk of cross-contamination and challenges of infection control with digital image sensors are comprehensively reviewed and infection control protocols that should be followed in intraoral digital imaging using both direct and indirect systems are thoroughly examined.