Interpretation Of Digital Images Research Articles

Zoom behavior during visual search modulates pupil diameter and reflects adaptive control states.

Adaptive gain theory proposes that the dynamic shifts between exploration and exploitation control states are modulated by the locus coeruleus-norepinephrine system and reflected in tonic and phasic pupil diameter. This study tested predictions of this theory in the context of a societally important visual search task: the review and interpretation of digital whole slide images of breast biopsies by physicians (pathologists). As these medical images are searched, pathologists encounter difficult visual features and intermittently zoom in to examine features of interest. We propose that tonic and phasic pupil diameter changes during image review may correspond to perceived difficulty and dynamic shifts between exploration and exploitation control states. To examine this possibility, we monitored visual search behavior and tonic and phasic pupil diameter while pathologists (N = 89) interpreted 14 digital images of breast biopsy tissue (1,246 total images reviewed). After viewing the images, pathologists provided a diagnosis and rated the level of difficulty of the image. Analyses of tonic pupil diameter examined whether pupil dilation was associated with pathologists' difficulty ratings, diagnostic accuracy, and experience level. To examine phasic pupil diameter, we parsed continuous visual search data into discrete zoom-in and zoom-out events, including shifts from low to high magnification (e.g., 1× to 10×) and the reverse. Analyses examined whether zoom-in and zoom-out events were associated with phasic pupil diameter change. Results demonstrated that tonic pupil diameter was associated with image difficulty ratings and zoom level, and phasic pupil diameter showed constriction upon zoom-in events, and dilation immediately preceding a zoom-out event. Results are interpreted in the context of adaptive gain theory, information gain theory, and the monitoring and assessment of physicians' diagnostic interpretive processes.

Land Cover Classification Improvements by Remote Sensing Data Fusion

Computer processing and analysis of satellite data is an urgent task of the science of remote sensing of the earth. Such processing can range from adjusting the contrast and brightness of the images of an amateur photographer to a group of scientists using neural network classification to determine the types of minerals in a hyperspectral satellite image. This article implements a method of satellite data fusion, which improves the digital image interpretation and image quality for further analysis. For fusion, a multispectral image with a resolution of 30 m Landsat 5 with 6 channels was taken, with three more significant and informative in their composition were used, as well as a panchromatic (monochrome) image with a resolution of 15 m. To evaluate the resolution of the images and the resulting images before and after the image fusion algorithm, image slices along a straight line and intersecting buildings, green mass, roads and industrial areas presented. For testing, test territories taken from Google Earth and the field work results.

A micro-hyperspectral image classification method of gallbladder cancer based on multi-scale fusion attention mechanism

A micro-hyperspectral image classification method of gallbladder cancer based on multi-scale fusion attention mechanism

Biodegradable, three-dimensional colorimetric fliers for environmental monitoring.

Recently reported winged microelectronic systems offer passive flight mechanisms as a dispersal strategy for purposes in environmental monitoring, population surveillance, pathogen tracking, and other applications. Initial studies indicate potential for technologies of this type, but advances in structural and responsive materials and in aerodynamically optimized geometries are necessary to improve the functionality and expand the modes of operation. Here, we introduce environmentally degradable materials as the basis of 3D fliers that allow remote, colorimetric assessments of multiple environmental parameters-pH, heavy metal concentrations, and ultraviolet exposure, along with humidity levels and temperature. Experimental and theoretical investigations of the aerodynamics of these systems reveal design considerations that include not only the geometries of the structures but also their mass distributions across a range of bioinspired designs. Preliminary field studies that rely on drones for deployment and for remote colorimetric analysis by machine learning interpretation of digital images illustrate scenarios for practical use.

Impact of COVID-19 on quality checks of solid tumor molecular diagnostic testing-A surveillance by EQAS provider in India.

BackgroundMolecular tests in solid tumours for targeted therapies call for the need to ensure precision testing. To accomplish this participation in the External Quality Assessment Program (EQAS) is required. This evaluates the consistency of diagnostic testing procedures and offers guidance for improving quality. Outbreak of COVID-19 pandemic led to worldwide lockdown and disruption of healthcare services including participation in EQAS.The present study describes the extended scope of EQAS offered byMPQAP (Molecular Pathology Quality Assurance Program), the first proficiency test provider for solid tumor diagnostics in India. The study surveys the preparedness of molecular testing laboratories in routine diagnostics and participation for quality assessment scheme.MethodsA documented guideline for measures and precautions to be carried by testing laboratories in performing routine diagnostic tests during the lockdown period were charted and distributed to all MPQAP participant centres. A survey was conducted for MPQAP participants to check whether laboratories were involved in COVID-19 testing and to evaluate the impact of lockdown on the operations of diagnostics procedures. From the acquired response of the survey, 2 cycles out of initially proposed 11 cycles were executed with transformed approach using digital tools and image interpretation modules.FindingsOut of 25 solid tumour testing laboratories registered as participants, 15 consented to participate in survey. The summary of survey conveyed the impact of COVID-19onroutine operations of diagnostics tests such as shortcomings in inventory and human resource management. Thirteen participants showed active willingness and consented to participate in EQAS test scheme.InterpretationsThe survey findings and assessment of EQAS cycles endorsed the quality testing procedures carried by participating laboratories throughout the lockdown. It highlighted the utility of EQAS participation during pandemic along with emphasis on safety measures for continual improvement in quality of diagnostic services.

Artificial intelligence and dermatology.

The approach of artificial intelligence (AI) systems can especially support doctors who base their work on the interpretation of digital images. Despite undoubted favorable results, the validation of diagnostic algorithms in clinical practice has not yet been demonstrated. In the dermatological field, a study found the poor performance of AI systems in diagnosing skin lesions that better reflect clinical practice, that is characterized by the presence of "artifacts" such as crusts or ulcerations above the lesion, hair or pen marks, pigmentation. The algorithms also correctly classified the category of images not present in the training dataset in only 11% of cases. In almost half of the cases the images were assigned to the category of neoplasms. In clinical reality, such false positivity predictions would lead to an increase in unnecessary biopsies with a related burden of worry and anxiety for patients and their families. This confirms that the value of the data is not in their breadth but in the validity of the path that led to their use, in order not to arrive at erroneous causal inferences that could lead to poor generalizability of the results and potentially serious diagnostic errors.

Improving Image Interpretation in Digital Forensics

Visual media has grown in importance as a means of communication in the digital space in recent years. The dependability of digital visual information has recently been called into question due to the ease of duplication in both content and origin. Digital image forensics is a new field of study that seeks to validate the authenticity of images by recovering information about their past. Identifying the imaging devices that captured the digital image and detecting forgery are two of the issues addressed. With the publication of study results and an increasing number of applications, digital image interpretation in digital forensics has emerged as an intriguing area for future research. Keywords: Images, Forensic, Capturing, Visual Information, Interpretation. BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security & Forensics. Open Access. Distributed Free Citation: Jonathan Gasokpo Adjorlolo (2022). Improving Image Interpretation in Digital Forensics . SMART-IEEE-Creative Research Publications Book Chapter Series on Research Nexus in IT, Law, Cyber Security & Forensics. Pp 23-28. www.isteams.net/ITlawbookchapter2022. dx.doi.org/

High-Throughput Screening to Examine the Dynamic of Stay-Green by an Imaging System.

The development of RGB (red, green, blue) sensors has opened the way for plant phenotyping. This is relevant because plant phenotyping allows us to visualize the product of the interaction between the plant ontogeny, anatomy, physiology, and biochemistry. Better yet, this can be achieved at any stage of plant development, i.e., from seedling to maturity. Here, we describe the use of phenotyping, based on the stay-green trait, of common bean (Phaseolus vulgaris L.) plant, as a model, stressed by water deficit, to elucidate the result of that interaction. Description is based on interpretation of RGB digital images acquired using a phenomic platform and a specific software. These images allow us to obtain a data group related to the color parameters that quantify the changes and alterations in each plant growth and development.

DIGITAL ROCK ANALYSIS: AN ALTERNATIVE METHOD TO PREDICT PETROPHYSICAL PROPERTIES, CASE STUDY FROM MISHRIF FORMATION

The digital core analysis of petrophysical properties replace the use of conventional core analysis by reducing the required time for investigation. Also, the ability to capture pore geometries and fluid behavior at the pore-scale improves the understanding of complex reservoir structures. In this work, 53 samples of 2D thin section petrographic images were used for analyses from the core plugs taken from the Buzurgan oil field. Each sample was impregnated with blue-dyed epoxy, thin sectioned and then was stained for discrimination of carbonate minerals. Each thin section has been described in detail and illustrated by photomicrographs. The studied samples include a variety of rock types. Packstone is the most common rock type observed followed by grainstone and packstone – wackestone. Floatstone and dolostone are noted rarely in the studied interval. However, the samples of thin section images are processed and digitized, utilizing MATLAB programming and image analysis software. The entire workflow of digital core analysis from image segmentation to petrophysical rock properties determination was performed. A focused has been made on determining effective and total porosity, absolute permeability, and irreducible water saturation. Absolute permeability is estimated with the Kozeny-Carman permeability correlation model and Timur-Coates permeability correlation model. Irreducible water saturation simply is derived from total and effective porosity. Also, some pore void characteristics, such as area and perimeter, were calculated. The results of Digital 2D image analysis have been compared to laboratory core measurements to investigate the reliability and restrictions of the digital image interpretation techniques.

Digital Rock Samples Porosity Analysis by OTSU Thresholding Technique Using MATLAB

Porosity plays an essential role in petroleum engineering. It controls fluid storage in aquifers, connectivity of the pore structure control fluid flow through reservoir formations. To quantify the relationships between porosity, storage, transport and rock properties, however, the pore structure must be measured and quantitatively described. Porosity estimation of digital image utilizing image processing essential for the reservoir rock analysis since the sample 2D porosity briefly described. The regular procedure utilizes the binarization process, which uses the pixel value threshold to convert the color and grayscale images to binary images. The idea is to accommodate the blue regions entirely with pores and transform it to white in resulting binary image. This paper presents the possibilities of using image processing for determining digital 2D rock samples porosity in carbonate reservoir rocks. MATLAB code created which automatically segment and determine the digital rock porosity, based on the OTSU's thresholding algorithm. In this work, twenty-two samples of 2D thin section petrographic image reservoir rocks of one Iraqi oil field are studied. The examples of thin section images are processed and digitized, utilizing MATLAB programming. In the present study, we have focused on determining of micro and macroporosity of the digital image. Also, some pore void characteristics, such as area and perimeter, were calculated. Digital 2D image analysis results are compared to laboratory core investigation results to determine the strength and restrictions of the digital image interpretation techniques. Thin microscopic image porosity determined using OTSU technique showed a moderate match with core porosity.

Pemanfaatan Citra Landsat 8 untuk Identifikasi Sebaran Vegetasi di Kecamatan Pangandaran

This study aims to determine: (1) the density of vegetation in Pangandaran district. (2)the result of comparison of vegetation density from digital image interpretation using the NDVI transformation with the results of validation. This research was done in pangandaran regency, which is located at coordinates 108º 41 - 1090 East Longitude and 07o 41-07 07 50 South Latitude has an area of up to 61 km². This research was conducted in several stages, namely: (1) the preparation of tools and data, (2) digital data processing, (3) field data collection, and (4) data analysis. Thanks to the density of vegetation can be done quickly by way of digital image interpretation using NDVI (Normalized Difference Vegetation Index) transformation. The purpose of this research is to know the land arrangement in Pangandaran District and for vegetation index of residential area in Pangandaran District. The object of this research is residential area in Pangandaran District. The data used are Landsat Image 8 year 2018. The software used is ENVI 5.1 and ArcGIS 10.3 software. The method used is the classification of land use and the calculation of NDVI in ENVI which then classified based on the range of NDVI index values. The results showed that the density of vegetation in Pangandaran District was dominating classified and as quite dense but it needed a lot of updating of its digital image and data so that the accuracy rate increased between imagery and field data. Vegetation in Pangandaran Subdistrict is mostly dominated by teak, waru, coconut, Chinese petai, goat, santigi, and ketapang. Based on the results of the interpretation accuracy test, obtained an accuracy value for the entire sample of 57% so that the results of image interpretation and field checks regarding vegetation density cannot be accepted because the expected level of interpretation accuracy is 85%, while the accuracy test results get a value of 57%. To get a complete picture of the availability of green open space in Pangandaram Regency, fast and relatively more accurate analysis of high spatial resolution is needed. The satellite imagery used in this study is Landsat 8 Imagery which has never been used in previous studies. Analysis of these images uses a vegetation index / NDVI that can directly distinguish plants from non-plants. The results of this image analysis also produce digital data that can be processed quantitatively for further research purposes.

The Dynamics of Coastline and Mangrove Ecosystems in Coastal Area of Mangkang Kulon Subdistrict, Semarang

Mangkang Kulon Subdistrict is one of the coastal areas in Semarang that has relatively dynamic conditions, which seen from the coastline and mangrove ecosystem changes within several years of observation. This study aims to observe coastline changes and the spatial distribution of mangrove ecosystems on the coast of Mangkang Kulon Subdistrict between 2005, 2012 and 2019. Land use n were obtained from visual interpretation of high-resolution digital globe satellite images from 2005, 2012, and 2019 which then tested the accuracy of land use interpretation through field observation and overlaid one another through vector analysis to find out the changes that have occurred. The study showed that the coastline changes were increasingly jutting towards the land in this range of time. This condition is affected by the port construction on the east coast of the Kendal Regency, which directly adjacent to the Mangkang Kulon Subdistrict. Meanwhile, the mangrove ecosystem has increased widely with relatively elongated and grouped spatial patterns. The increase that occurred as a manifestation of various coastal care activities in the form of mangrove planting activities carried out by several government agencies and private parties with a model of coastal community empowerment.

Glacier reconstruction of La Covacha Massif in Sierra de Gredos (central Spain) during the Last Glacial Maximum

A paleoglacier reconstruction during the Last Glacial Maximum (LGM) is presented for a high mountain area of La Covacha massif in Sierra de Gredos (Iberian Central System) in central Spain. The moraines that, according with the previous literature, had formed during the global LGM, were mapped through photo interpretation of digital aerial photographs and 3D images and detailed field surveys. The topography of the paleoglaciers was estimated using a simple steady-state model that assumed a perfectly plastic ice rheology, reconstructing the theoretical ice profiles and obtaining the ice thickness of the glaciers during the LGM. The reconstruction of the glaciers was carried out using automated and semi-automated physically-based models, obtaining more realistic results with semi-automated models. According to our study, the paleoglaciers in the study area covered an area of 34.79 km2 during the global LGM, with a maximum ice thickness of 366 meters in La Vega gorge, a total volume of 34.25 × 108 m3 and a mean paleoELA of 1932 meters. Most of the ice (∼82%) was in paleoglaciers facing north, and the rest was in paleoglaciers with other orientations. This emphasizes the importance of orientation in relation to glacier dynamics and ice accumulation. The results obtained in the calculation of paleoELAs during the global LGM in Gredos are average compared to the Iberian mountains of the Northwest where the values were much lower, and with respect to those of the Southwest, much higher. This demonstrates the importance of the exposure of Mediterranean mountains to the humidity sources coming from the NW during the global LGM, as is the case at present.

컴퓨터 보조 진단 시스템 기반 치아우식증 검출 기법 비교 분석

Dental caries, a gradual bacterial damage to teeth, is one of the most common diseases and is still a major cause of tooth loss. It can be divided into 7 scores based on the ICDAS(International Caries Detection and Assessment System) according to the tooth condition. Early detection and treatment of dental caries can reduce the costs as well as time. As computer performance has been improved, machine learning and image processing technologies are introduced to detect and diagnose dental caries. Computer-aided Diagnosis(CADx) can assist dentists in the interpretation of several oral digital images or X-ray image. Dental caries detection system using CADx has four steps. First is image enhancement to improve the original image. Second is tooth detection which extract the tooth area from the oral structures. Third is caries detection which finds the caries lesion in tooth area. The fourth is classification which analyzes and classifies carious lesions. In this paper, we survey and introduce the various dental caries detection methods based on CADx.

Automatic diagnostics of tuberculosis using convolutional neural networks analysis of MODS digital images.

Tuberculosis is an infectious disease that causes ill health and death in millions of people each year worldwide. Timely diagnosis and treatment is key to full patient recovery. The Microscopic Observed Drug Susceptibility (MODS) is a test to diagnose TB infection and drug susceptibility directly from a sputum sample in 7–10 days with a low cost and high sensitivity and specificity, based on the visual recognition of specific growth cording patterns of M. Tuberculosis in a broth culture. Despite its advantages, MODS is still limited in remote, low resource settings, because it requires permanent and trained technical staff for the image-based diagnostics. Hence, it is important to develop alternative solutions, based on reliable automated analysis and interpretation of MODS cultures. In this study, we trained and evaluated a convolutional neural network (CNN) for automatic interpretation of MODS cultures digital images. The CNN was trained on a dataset of 12,510 MODS positive and negative images obtained from three different laboratories, where it achieved 96.63 +/- 0.35% accuracy, and a sensitivity and specificity ranging from 91% to 99%, when validated across held-out laboratory datasets. The model's learned features resemble visual cues used by expert diagnosticians to interpret MODS cultures, suggesting that our model may have the ability to generalize and scale. It performed robustly when validated across held-out laboratory datasets and can be improved upon with data from new laboratories. This CNN can assist laboratory personnel, in low resource settings, and is a step towards facilitating automated diagnostics access to critical areas in developing countries.

Identification of Groundwater Potential Zones of Visakhapatnam Urban Area using Integrating Multi Criteria Evaluation with Geographical Information System

Groundwater forms very little quantity when compared to the total water available on the earth. Therefore it is very vital for all living beings especially for human beings. Visakhapatnam, one of the fastest growing industrial city, is situated on the East Coast of India between longitudes E83o11’ 30” and 83o 22’ 16” and latitudes. N170 39’ 16” and 170 45’ 58”. The present study is aimed to evaluate the groundwater occurrence using Remote sensing and GIS. Remote sensing data interpretation of visual and digital images gave the immediate information about surface features. From this information the groundwater potential zones are identified. The present study used IRS-IC (March 99) and ID (November 99) LISS-III digital data for comparative land use and land cover categorization and hydrogeomorphological features identification and lineament study. The layers created from Remote sensing data and available ancillary data for index overlay operations for identification of groundwater potential zones in the study area using GIS.

ASSEMBLY OF A REMOTELY PILOTED AIRCRAFT OF LOW COST APPLIED TO AGRICULTURE

ABSTRACT: The remotely piloted aircraft (RPA) have great applicability in agriculture. Although it is a technology that has been developed for many years, in the field of agriculture it passed unnoticed for a long time. The high cost of acquisition is still a limiting factor for small and medium-sized farmers. In this way, this study had as objective to develop a low cost RPA for agriculture application. In the present study, the development and the assembly of an RPA prototype of a quadcopter type is presented, consisting of four engines and of vertical propulsion, capable of moving without human intervention, that is, autonomous flight, applied to agriculture and low cost investment. In it was boarded a data control and processing system collected from sensors and cameras, using hardware and software from a free platform (Open Source). The assembly equipment showed to be robust, with a payload of approximately 1,000 grams, with an acquisition cost (R$ 2,393.16), included the camera module, much lower than those commercially available and suitable for the agricultural application, as acquisition, processing and interpretation of digital images, due to the quality of the images obtained.

A feature selection method based on a neighborhood approach for contending with functional and anatomical variability in fMRI group analysis of cognitive states

The study of cognitive processes performed by the human brain has greatly benefited from new technologies able to infer neuronal activity by means of noninvasive methods. This is the case of functional magnetic resonance imaging. Digital image analysis and interpretation techniques have also contri buted greatly to the exploration and understanding of these brain functions. Among these techniques, the use of machine learning algorithms with the ability to automatically classify cognitive states has been particularly fruitful. In general terms, these techniques identify brain regions involved in specific cognitive processes by correlating experimental stimulation patterns with the magnitude of observed neural activity. An issue with this kind of analyses arises when comparing activation results from different subjects. This occurs due to functional and anatomical variability among individuals, even after this variability is reduced during the registration process performed on the images as part of the preprocessing. In this paper we propose a feature selection method to contend with this variability. The basic idea consists in defining the activity of a voxel (feature) as a weighted vote of the observed activity of its neighbors located at a periphery defined by an isotropic three-dimensional space. Such influence is determined by a Gaussian radial function. This approach allows comparing results among different individuals, assuming that functionally equivalent activities are not necessarily presented in the same spatial position. The results show that this spatial tolerance allows a classification accuracy of 96% (considering a threshold of ± 2 voxels, equivalent to ± 8 mm.) against the 84% obtained by a traditional feature selection method.

Analysing post-earthquake landslide activity using multi-temporal landslide inventories near the epicentral area of the 2008 Wenchuan earthquake

Abstract. Large earthquakes in mountainous regions may trigger thousands of landslides, some active for years. We analysed the changes in landslide activity near the epicentre of the 2008 Wenchuan earthquake by generating five landslide inventories for different years through stereoscopic digital visual image interpretation. From May 2008 to April 2015, 660 new landslides occurred outside the co-seismic landslide areas. In April 2015, the number of active landslides had gone down to 66, less than 1 % of the co-seismic landslides, but still much higher than the pre-earthquake levels. We expect that the landslide activity will continue to decay, but may be halted if extreme rainfall events occur.

ANALYSIS OF THE SEGMENTED FEATURES OF INDICATOR OF MINE PRESENCE

Abstract. The aim of this research is to investigate possibility for interactive semi-automatic interpretation of digital images in humanitarian demining for the purpose of detection and extraction of (strong) indicators of mine presence which can be seen on the images, according to the parameters of the general geometric shapes rather than radiometric characteristics. For that purpose, objects are created by segmentation. The segments represent the observed indicator and the objects that surround them (for analysis of the degree of discrimination of objects from the environment) in the best possible way. These indicators cover a certain characteristic surface. These areas are determined by segmenting the digital image. Sets of pixels that form such surface on images have specific geometric features. In this way, it is provided to analyze the features of the segments on the basis of the object, rather than the pixel level. Factor analysis of geometric parameters of this segments is performed in order to identify parameters that can be distinguished from the other parameters according to their geometric features. Factor analysis was carried out in two different ways, according to the characteristics of the general geometric shape and to the type of strong indicators of mine presence. The continuation of this research is the implementation of the automatic extraction of indicators of mine presence according results presented in this paper.