Observed Image Data Research Articles

Deep learning solutions for inverse problems in advanced biomedical image analysis on disease detection

Inverse problems in biomedical image analysis represent a significant frontier in disease detection, leveraging computational methodologies and mathematical modelling to unravel complex data embedded within medical images. These problems include deducing the unknown properties of biological structures or tissues from the observed imaging data, presenting a unique challenge in decoding intricate biological phenomena. Regarding disease detection, this technique has played a critical role in optimizing diagnostic efficiency by extracting meaningful insights from different imaging modalities like molecular imaging, MRI, and CT scans. Inverse problems contribute to uncovering subtle abnormalities by employing iterative optimization techniques and sophisticated algorithms, enabling precise and early disease detection. Deep learning (DL) solutions have emerged as robust mechanisms for addressing inverse problems in biomedical image analysis, especially in disease recognition. Inverse problems involve reconstructing unknown structures or parameters from observed data, and the DL model excels in learning complex representations and mappings. This study develops a DL Solution for Inverse Problems in the Advanced Biomedical Image Analysis on Disease Detection (DLSIP-ABIADD) technique. The DLSIP-ABIADD technique exploits the DL approach to solve inverse problems and detect the presence of diseases on biomedical images. To solve the inverse problem, the DLSIP-ABIADD technique uses a direct mapping approach. Bilateral filtering (BF) is used for image preprocessing. Besides, the MobileNetv2 model derives feature vectors from the input images. Moreover, the Henry gas solubility optimization (HGSO) method is applied for optimal hyperparameter selection of the MobileNetv2 model. Furthermore, a bidirectional long short-term memory (BiLSTM) model is deployed to identify diseases in medical images. Extensive simulations have been involved to illustrate the better performance of the DLSIP-ABIADD technique. The experimentation outcomes stated that the DLSIP-ABIADD technique performs better than other models.

The Utilization of LiCSBAS for Deformation Monitoring in Geresa Segment of Matano Fault, Central Sulawesi, Indonesia

Sulawesi is situated between the confluence of three plates, resulting in a very complex tectonic setting on the island. This has an impact on the occurrence of geological structures, including faults. One of them is the Matano Fault which consists of 6 segments (Kuleana Segment, Pewusai Segment, Matano Segment, Pamsoa Segment, Ballawai Segment, and Geresa Segment). The research area is located in Morowali Regency, covering the Geresa Segment. Morowali Regency recorded an earthquake with a magnitude of 5.7 in 2012. This indicates that deformation has occurred. Therefore, this study aims to identify the deformation velocity around the Geresa Segment area. The methods used are geological observation and satellite image data processing to obtain information on deformation rates. The geological field study includes aspects of geomorphology, geological structure, and the rock types distribution. Meanwhile, the processing of satellite image data in the form of InSAR is carried out through the LiCSBAS package tools that has been integrated with LiCSAR. The analysis results illustrate the difference in deformation velocity around the Geresa Segment area. The area which is composed of Tolaka formation and Ultramafic complex tends to uplift with a deformation rate of up to 17 mm/year. In addition, alluvium that covers the southeastarn part has a land subsidence of up to 7 mm/year.

The Use of EVI Algorithm in Predicting Rice Productivity, Harau District, Lima Puluh Kota Regency, West Sumatra

Using remote sensing data in predicting rice productivity can provide information quickly and easily for relatively large areas. Harau District is a rice center in Lima Puluh Kota Regency and is one of the many rice-producing areas in West Sumatra Province. This study aims to determine a mathematical model to estimate rice productivity in Harau District, Lima Puluh Kota Regency, using the EVI (Enhanced Vegetation Index) algorithm. This study used two methods, field observation and MODIS image data processing (MOD13A1 v006). The results of the analysis obtained a mathematical model to predict rice productivity in Harau District, namely y = 5393.3x4 - 8140.7x3 + 4518.9x2 - 1121.6x + 113.55 where y is the productivity value (tons/ha) and x as the EVI value. The percentage of errors in observation and estimation data was 15.284%, and the results of the observation and estimation data validation test (NSE) were 0.734, a good category.

로블록스를 활용한 디지털 미술치료 프로그램 개발과정에서 나타난 현상

The purpose of this study is to explore various elements and characteristics inherent in the phenomenon that occurred in the process of developing digital art therapy program through participatory observation. To this end, this researcher, an art therapist, participated in the development of art therapy program using Roblox for the purpose of preventing the burnout of new nurses. The research data were collected around interview data, minutes, researcher's observation diary, video data, and image data had derived from the development process for a total of four months from October 2021 to January 2022, and the facts were described and interpreted through transcription. The phenomenon of program development was analyzed in aspects of objective, content, subjective, methodological, political, and design. It is expected that this study will be used as premilary data for development of digital art therapy programs that require multidisciplinary collaboration and convergence.

SWM-DE: Statistical wavelet model for joint denoising and enhancement for multimodal medical images

Medical images are usually degraded by numerous noises during acquisition or transmission, which often causes low contrast leading to deterioration of image quality. As such, medical image denoising and enhancement has become a paramount routine task. To overcome this problem, we propose a cutting-edge joint statistical and morphological model for the denoising and enhancement operation. Firstly, we propose a statistical model in formulating the marginal distribution of the wavelet coefficients. This model is integrated into a Bayesian inference framework to develop a maximum a posterior (MAP) estimator of the noise-free coefficient. Based on the statistical model, we eliminate the need for noise level estimation, and allows the model to automatically adapts to the observed image data. Secondly, we propose an adjustable morphological reconstruction model to eliminate known and unknown noises associated with medical images, while preserving the image details. After these operations, the image is decomposed into several wavelet subbands to extract the illumination and detail components. The image is then reconstructed based on the inverse wavelet to generate the enhanced noise-free image.Experimental results show that the proposed framework obtained high EME values of 41.04, 48.81, 47.81, and 45.75 for OCTA, FFA, CT, and X-ray imaging modalities, and performs better than the state-of-the-art methods. The proposed algorithm can effectively and efficiently enhance medical images, which will assist the clinicians in disease diagnosis, monitoring, and treatment.

Improved method for spectral reflectance estimation and application to mobile phone cameras.

We propose an improved method for estimating surface-spectral reflectance from the image data acquired by an RGB digital camera. We suppose a multispectral image acquisition system in the visible range, where a camera captures multiple images for the scene of an object under multiple light sources. First, the observed image data are described using the camera spectral sensitivities, the surface-spectral reflectance, the illuminant spectral power distributions, an additive noise term, and a gain parameter. Then, the optimal reflectance estimate is determined to minimize the mean-square error between the estimate and the original surface-spectral reflectance. We attempt to further improve the estimation accuracy and develop a novel linear estimator in a more general form than the Wiener estimator. Furthermore, we calibrate the imaging system using a reference standard sample. Finally, experiments are performed to validate the proposed method for estimating the surface-spectral reflectance using different mobile phone cameras.

NDVI Threshold-Based Urban Green Space Mapping from Sentinel-2A at the Local Governmental Area (LGA) Level of Victoria, Australia

Obtaining accurate, precise and timely spatial information on the distribution and dynamics of urban green space is crucial in understanding livability of the cities and urban dwellers. Inspired from the importance of spatial information in planning urban lives, and availability of state-of-the-art remote sensing data and technologies in open access forms, in this work, we develop a simple three-level hierarchical mapping of urban green space with multiple usability to various stakeholders. We utilize the established Normalized Difference Vegetation Index (NDVI) threshold on Sentinel-2A Earth Observation image data to classify the urban vegetation of each Victorian Local Government Area (LGA). Firstly, we categorize each LGA region into two broad classes as vegetation and non-vegetation; secondly, we further categorize the vegetation regions of each LGA into two sub-classes as shrub (including grassland) and trees; thirdly, for both shrub and trees classes, we further classify them as stressed and healthy. We not only map the urban vegetation in hierarchy but also develop Urban Green Space Index (UGSI) and Per Capita Green Space (PCGS) for the Victorian Local Government Areas (LGAs) to provide insights on the association of demography with urban green infrastructure using urban spatial analytics. To show the efficacy of the applied method, we evaluate our results using a Google Earth Engine (GEE) platform across different NDVI threshold ranges. The evaluation result shows that our method produces excellent performance metrics such as mean precision, recall, f-score and accuracy. In addition to this, we also prepare a recent Sentinel-2A dataset and derived products of urban green space coverage of the Victorian LGAs that are useful for multiple stakeholders ranging from bushfire modellers to biodiversity conservationists in contributing to sustainable and resilient urban lives.

Tracking crop phenology in a highly dynamic landscape with knowledge-based Landsat–MODIS data fusion

Earth observation image data are regularly used to capture surface conditions over large areas, but there is a trade-off between high (or low) spatial and low (or high) temporal resolution. The Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) overcomes this trade-off by fusing high spatial and temporal resolution multisource image data. However, ESTARFM requires additional modifications in order to provide reliable estimates of surface conditions showing large spectral differences in highly dynamic and fragmented agricultural systems. We modified ESTARFM by taking a knowledge-based approach to track maize and rice phenology in a highly dynamic and fragmented agricultural landscape in Ethiopia in 2019. The two major improvements included: (i) Selection of Landsat-MODIS imageries based on crop sowing and harvesting information and (ii) generation and use of a land cover map to select similar pixels. We assessed model performance with the enhanced vegetation index (EVI) derived from independent Landsat image data and in-situ leaf area index (LAI) data. The improved ESTARFM workflow resulted in reliable Landsat-MODIS prediction (R2 = 0.67, RMSE = 0.07) compared to the standard ESTARFM workflow (R2 = 0.54 RMSE = 0.01) during the rapid growth stage. Our modifications outperformed the standard implementation of ESTARFM according to LAI magnitude (R2 = 0.73–0.84 versus R2 = 0.58–0.64) and phenological timing (RMSE = 8 days verses RMSE = 12 days). Our modified application of ESTARFM serves as a basis for monitoring crop growth and development in highly dynamic and fragmented agricultural systems.

BARcode DEmixing through Non-negative Spatial Regression (BarDensr).

Modern spatial transcriptomics methods can target thousands of different types of RNA transcripts in a single slice of tissue. Many biological applications demand a high spatial density of transcripts relative to the imaging resolution, leading to partial mixing of transcript rolonies in many voxels; unfortunately, current analysis methods do not perform robustly in this highly-mixed setting. Here we develop a new analysis approach, BARcode DEmixing through Non-negative Spatial Regression (BarDensr): we start with a generative model of the physical process that leads to the observed image data and then apply sparse convex optimization methods to estimate the underlying (demixed) rolony densities. We apply BarDensr to simulated and real data and find that it achieves state of the art signal recovery, particularly in densely-labeled regions or data with low spatial resolution. Finally, BarDensr is fast and parallelizable. We provide open-source code as well as an implementation for the 'NeuroCAAS' cloud platform.

Astro-TS3: Time-series Subimage Search Engine for archived astronomical data

Abstract Efficiently retrieving a subset of time series data in a specific sky region from large-scale astronomical observation data is important for some time-domain studies in astronomy, especially for research on long-term variable sources. Considering that the field of view of a telescope is generally larger than the effective sky region, the retrieval method at the full image level is relatively inefficient in terms of data transmission and storage. Therefore, this paper proposes a search engine dedicated to the efficient retrieval of time-series subimages in observed image data archives (Astro-TS3, Astronomical Time-Series Subimage Search Engine). An in situ and resilient indexing scheme is proposed, and it can build indexes for archived image data without any modification of the original files and support incremental updating with newly archived data. A subimage cutting tool is implemented to extract a part of the image according to the range specified by the user and generate a new Flexible Image Transport System (FITS) file for the extracted subimage. With Astro-TS3, users only need to submit requests including the ranges of the time and sky region to retrieve the required data and then download the time-series subimages after the preview. The evaluation of Astro-TS3 is performed using actual archived data from three Antarctic Survey Telescopes (AST3). The experimental data show that the time for the index establishment and incremental update increases approximately linearly relative to the number of data files. For retrieval, the response time of the data retrieval is approximately linearly related to the number and size of the subimages.

Fully synthetic neuroimaging data for replication and exploration

Scientific transparency, data exploration, and education are advanced through data sharing. However, risk for disclosure of personal information and institutional data sharing regulations can impede human subject/patient data sharing and thus limit open science initiatives. Sharing fully synthetic data is an alternative when it is not possible to share real or observed data. Here we describe a data sharing approach that borrows principles and methods from multiple imputation to replace observed values with synthetic values, thereby creating a fully synthetic neuroimaging dataset that accurately represents the covariance structure of the observed dataset. Predictor tables composed of demographic, site, behavioral and total intracranial volume (ICV) variables from 264 pediatric cases were used to create synthetic predictor tables, which were then used to synthesize gray matter images derived from T1-weighted data. The synthetic predictor tables demonstrated pooled variance and statistical estimates that closely approximated the observed data, as reflected in measures of efficiency and statistical bias. Similarly, the synthetic gray matter data accurately represented the variance and voxel-level associations with predictor variables (age, sex, verbal IQ, and ICV). The magnitude and spatial distribution of gray matter effects in the observed imaging data were replicated in the pooled results from the synthetic datasets. This approach for generating fully synthetic neuroimaging data has widespread potential for data sharing, including replication, new discovery, and education. Fully synthetic neuroimaging datasets can enable data-sharing because it accurately represents patterns of variance in the original data, while diminishing the risk of privacy disclosures that can accompany neuroimaging data sharing.

Hierarchical Bayesian myocardial perfusion quantification.

Myocardial blood flow can be quantified from dynamic contrast-enhanced magnetic resonance (MR) images through the fitting of tracer-kinetic models to the observed imaging data. The use of multi-compartment exchange models is desirable as they are physiologically motivated and resolve directly for both blood flow and microvascular function. However, the parameter estimates obtained with such models can be unreliable. This is due to the complexity of the models relative to the observed data which is limited by the low signal-to-noise ratio, the temporal resolution, the length of the acquisitions and other complex imaging artefacts. In this work, a Bayesian inference scheme is proposed which allows the reliable estimation of the parameters of the two-compartment exchange model from myocardial perfusion MR data. The Bayesian scheme allows the incorporation of prior knowledge on the physiological ranges of the model parameters and facilitates the use of the additional information that neighbouring voxels are likely to have similar kinetic parameter values. Hierarchical priors are used to avoid making a priori assumptions on the health of the patients. We provide both a theoretical introduction to Bayesian inference for tracer-kinetic modelling and specific implementation details for this application. This approach is validated in both in silico and in vivo settings. In silico, there was a significant reduction in mean-squared error with the ground-truth parameters using Bayesian inference as compared to using the standard non-linear least squares fitting. When applied to patient data the Bayesian inference scheme returns parameter values that are in-line with those previously reported in the literature, as well as giving parameter maps that match the independant clinical diagnosis of those patients.

Bayesian inference of grain growth prediction via multi-phase-field models

We propose a Bayesian inference methodology to evaluate unobservable parameters involved in multi-phase-field models to accurately predict the observed grain growth, such as in metals and alloys. This approach integrates models and a set of observational image data of grain structures. Because the image data set is not a time series, directly applying conventional inference techniques that require time series as the input data is difficult. The key idea in our methodology to overcome this difficulty is to construct a time series with an appropriate statistic that characterizes static image data of grain structures. Our methodology implements the empirical Bayes method. It can estimate not only a probability density function of the parameters but also an initial phase field, which is generally unobservable in real experiments. After validating the proposed method through numerical tests using synthetic data, we apply it to real experimental images of grain structures in a steel alloy. The proposed method properly estimates unobservable parameters along with their uncertainties and successfully selects the initial phase field that best explains the experimental data from among candidate initial phase fields.

Gauging Functional Brain Activity: From Distinguishability to Accessibility.

Standard neuroimaging techniques provide non-invasive access not only to human brain anatomy but also to its physiology. The activity recorded with these techniques is generally called functional imaging, but what is observed per se is an instance of dynamics, from which functional brain activity should be extracted. Distinguishing between bare dynamics and genuine function is a highly non-trivial task, but a crucially important one when comparing experimental observations and interpreting their significance. Here we illustrate how neuroimaging’s ability to extract genuine functional brain activity is bounded by functional representations’ structure. To do so, we first provide a simple definition of functional brain activity from a system-level brain imaging perspective. We then review how the properties of the space on which brain activity is represented induce relations on observed imaging data which allow determining the extent to which two observations are functionally distinguishable and quantifying how far apart they are. It is also proposed that genuine functional distances would require defining accessibility, i.e., how a given observed condition can be accessed from another given one, under the dynamics of some neurophysiological process. We show how these properties result from the structure defined on dynamical data and dynamics-to-function projections, and consider some implications that the way and extent to which these are defined have for the interpretation of experimental data from standard system-level brain recording techniques.

Dynamic Hyper-Graph Inference Framework for Computer-Assisted Diagnosis of Neurodegenerative Diseases.

Hyper-graph techniques have been widely investigated in computer vision and medical imaging applications, showing superior performance for modeling complex subject-wise relationships and sufficient flexibility to deal with missing data from multi-modal neuroimaging data. Existing hyper-graph methods, however, are inadequate for two reasons. First, representations are generated only from the observed imaging data, a process that is completely independent of the subsequent data label inference/ classification step. Thus, hyper-graph results constructed in this way may not be consistent with phenotype data such as clinical labels or scores. More critically, it might generate sub-optimal predictions in relation to clinical labels/scores. Second, current hyper-graph inference methods rely on two sequential steps: 1) building the hyper-graph for each individual modality and then predicted latent labels for new subjects upon each constructed hyper-graph and 2) a voting procedure to incorporate inference results across different hyper-graphs. This approach, however, is limited by failing to consider the complex and complementary relationships of multi-modal imaging data with respect to hyper-graph inference procedure. To address these two issues, we propose a novel dynamic hyper-graph inference method supported by a semi-supervised framework. Our method iteratively estimates and adjusts the hyper-graph structures from multi-modal imaging data until consistency between the learned hyper-graph and the observed clinical labels and scores is achieved. This hyper-graph inference framework also eases the integration process of classification (identifying individuals having neurodegenerative disease) and regression (predicting the clinical scores) within the same framework. The experimental results on identifying mild cognition impairment (MCI) subjects and the fine grained recognition of MCI progression stages show improved performance using our proposed hyper-graph inference method compared with conventional methods.

A web-based real-time and full-resolution data visualization for Himawari-8 satellite sensed images

It has been almost four decades since the first launch of geostationary meteorological satellite by Japan Meteorological Agency (JMA). The specifications of the geostationary meteorological satellites have shown tremendous progresses along with the generations, which are now entering their third generation. The third-generation geostationary meteorological satellites not only yield basic data for weather monitoring, but also globally observe the Earth’s environment. The development of multi-band imagers with improved spatial resolution onboard the third-generation geostationary meteorological satellites brings us meteorological data in larger size than those of the second-generation ones. Thus, new techniques for domestic and world-wide dissemination of the observational big data are needed. In this paper, we develop a web-based data visualization for Himawari-8 satellite sensed images in real time and with full resolution. This data visualization is supported by the ecosystems, which uses a tiled pyramid representation and parallel processing technique for terrain on an academic cloud system. We evaluate the performance of our techniques for domestic and international users on laboratory experiments. The results show that our data visualization is suitable for practical use on a temporal preview of observation image data for the domestic users.

Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning.

Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked autoencoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework, image registration experiments were conducted on 7.0-T brain MR images. In all experiments, the results showed that the new image registration framework consistently demonstrated more accurate registration results when compared to state of the art.

Moving Target Analysis in ISAR Image Sequences With a Multiframe Marked Point Process Model

In this paper, we propose a multiframe marked point process model of line segments and point groups for automatic target structure extraction and tracking in inverse synthetic aperture radar (ISAR) image sequences. To deal with scatterer scintillations and high speckle noise in the ISAR frames, we obtain the resulting target sequence by an iterative optimization process, which simultaneously considers the observed image data and various prior geometric interaction constraints between the target appearances in the consecutive frames. A detailed quantitative evaluation is performed on eight real ISAR image sequences of different carrier ships and airplane targets, using a test database containing 545 manually annotated frames.

Non-Rigid Object Detection with LocalInterleaved Sequential Alignment (LISA).

This paper shows that the successively evaluated features used in a sliding window detection process to decide about object presence/absence also contain knowledge about object deformation. We exploit these detection features to estimate the object deformation. Estimated deformation is then immediately applied to not yet evaluated features to align them with the observed image data. In our approach, the alignment estimators are jointly learned with the detector. The joint process allows for the learning of each detection stage from less deformed training samples than in the previous stage. For the alignment estimation we propose regressors that approximate non-linear regression functions and compute the alignment parameters extremely fast.

Motion Law of Graviton in Three-Dimensional Space and Its Influence on Star Distribution of Spiral Galaxy

According to some fundamental hypotheses compatible with microphysics theory in allusion to property of graviton shot by the spiral galaxy, we set up a motion trace equation of graviton in the three-dimensional space, concluding method for measuring the velocity of graviton. This theory reveals formation mechanism of symmetric rotating arm image of the spiral galaxy. The rotating arm image conforms to Archimedes spiral. Under the condition of using dynamic constraints, the image that astrolabe is in thin-disk structure is naturally obtained. And then, we also deduce the law that astrolabe thickness h is inversely proportional to the square root of the distance rxy between astrolabe and galaxy center, according with actual crossrange galaxy observation image excellently. This article, by utilizing several typical galaxy observation images with completely different appearances and shapes, verifies the motion trace law of graviton in three-dimensional space. And the theoretical simulation data meet observation image data very well. As per the discovery in the simulation of three-dimensional theoretical model, changes of parameters in theoretical model will exert a great influence on the shape of galaxy. This theory owns exclusively important meaning, not only to astrophysics, but also to reconstruction and development of relativity theory and the whole basic physics theory, as well as breaking through the current dilemma in studying graviton property.