Initial Imaging Data Research Articles

Sea ice melt pond bathymetry reconstructed from aerial photographs using photogrammetry: a new method applied to MOSAiC data

Abstract. Melt ponds are a core component of the summer sea ice system in the Arctic, increasing the uptake of solar energy and impacting the ice-associated ecosystem. They were thus one of the key topics during the 1-year drift campaign Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) in the Transpolar Drift 2019/2020. Pond depth is a dominating factor in describing the surface meltwater volume; it is necessary to estimate budgets and used in model parameterization to simulate pond coverage evolution. However, observational data on pond depth are spatially and temporally strongly limited to a few in situ measurements. Pond bathymetry, which is pond depth spatially fully resolved, remains unexplored. Here, we present a newly developed method to derive pond bathymetry from aerial images. We determine it from a photogrammetric multi-view reconstruction of the summer ice surface topography. Based on images recorded on dedicated grid flights and facilitated assumptions, we were able to obtain pond depth with a mean deviation of 3.5 cm compared to manual in situ observations. The method is independent of pond color and sky conditions, which is an advantage over recently developed radiometric airborne retrieval methods. It can furthermore be implemented in any typical photogrammetry workflow. We present the retrieval algorithm, including requirements for the data recording and survey planning, and a correction method for refraction at the air–pond interface. In addition, we show how the retrieved surface topography model synergizes with the initial image data to retrieve the water level of individual ponds from the visually determined pond margins. We use the method to give a profound overview of the pond coverage on the MOSAiC floe, on which we found unexpected steady pond coverage and volume. We were able to derive individual pond properties of more than 1600 ponds on the floe, including their size, bathymetry, volume, surface elevation above sea level, and temporal evolution. We present a scaling factor for single in situ depth measurements, discuss the representativeness of in situ pond measurements and the importance of such high-resolution data for new satellite retrievals, and show indications for non-rigid pond bottoms. The study points out the great potential to derive geometric properties of the summer sea ice surface emerging from the increasingly available visual image data recorded from uncrewed aerial vehicles (UAVs) or aircraft, allowing for an integrated understanding and improved formulation of the thermodynamic and hydrological pond system in models.

Identification of traditional settlement landscape areas in Hainan based on unmanned aerial remote sensing imagery

Abstract To enhance the accuracy of recognizing traditional settlement landscapes in Hainan, this study introduces a landscape recognition model predicated on a full convolutional neural network (FCN). The research delineates the selection of specific types and sensors for Unmanned Aerial Vehicles (UAVs), employing UAV remote sensing technology to capture image data of Hainan's traditional settlement landscapes. The collected initial image data, laden with interference information, underwent a preprocessing step. On this foundation, the landscape recognition model for Hainan's traditional settlements was developed utilizing the FCN. The empirical results derived from deploying the model reveal the presence of ancient buildings within a 400-meter radius of the principal river. Notably, these structures predominantly span a distance ranging from 100 to 350 meters. The spatial distribution pattern of these ancient edifices notably centers around the Zong ancestral hall. Furthermore, when compared to other benchmark models, the proposed FCN model exhibits superior performance in recognizing forest, grassland, and farmland within the Hainan settlement landscape, achieving average recognition rates of 88.66% and 84.91%, respectively. This investigation underscores the significant potential for applying UAV remote sensing technology in identifying traditional settlement landscapes. It provides pivotal technical support and a reference point for the survey of forest resources and ecological monitoring, thereby enhancing the applicability and dissemination of UAV technology in landscape recognition tasks.

3D Printing of Heart Model as Medical Education Tools

Abstract. 3D printing is a rapidly developing technology in the medical world that has been used for pre-operative planning, prosthetic manufacturing, and training for medical education. This 3D printing is needed for medical education to make it easier for students to study anatomical structures. The advantages of 3D printing provide more detail and tactile representation of anatomical aspects of organs to address the problems of online learning and cadaveric limitations. This research aimed to develop the manufacture of 3D printed models of the human heart organ to improve understanding in learning for medical students. Making a 3D printed model of a heart organ is divisible into six parts: the aorta, right ventricle, left atrium, left ventricle, right atrium, and pulmonary artery. The 3D printing model creation procedure consisted of several steps: image acquisition, image post-processing, and 3D printing. This research used Computed Tomography Scanning (CT-Scan) images of the normal heart in Digital Imaging in Medicine (DICOM) format from Saiful Anwar Hospital, Malang. The segmentation uses the grow from seed technique with 3D Slicer software and is saved in STL format. The accuracy of the 3D printing was carried out by measuring dimensions and volume. Measurements are required to ensure the accuracy of 3D printing so that the resulting organs match the initial image data and can be used as learning media in anatomical structures by medical students.

A hybrid method for cutting tool RUL prediction based on CNN and multistage Wiener process using small sample data

Based on the multistage and nonlinear characteristics of cutting tool wear, a hybrid method for cutting tool remaining useful life (RUL) prediction based on convolutional neural network (CNN) and multistage Wiener process using small sample data is proposed in this paper. First, wavelet transform is used to analyse the vibration data collected for cutting tools in the time–frequency domain to obtain an initial image data set. A CCVAE (conditional variational autoencoder with CNN) network is built to expand the initial data set and solve the problem of unbalanced data in different stages of cutting tool wear. The expanded data is used as the input of the CNN to monitor the tool wear state and amount of wear. Then, a nonlinear multistage Wiener process is established to describe the cutting tool wear degradation process and achieve accurate RUL prediction for the cutting tool. Specifically, a nonlinear Wiener process model corresponding to different degradation stages based on the change points between the cutting tool wear states output by the CNN is established. Maximum likelihood estimation and Bayesian methods are used to estimate and update the parameters, respectively, and the RUL value and corresponding probability density function (PDF) are obtained under different wear conditions. Finally, through experimental research and comparative analysis, it is found that the multistage nonlinear Wiener model accurately simulates cutting tool wear degradation, which verifies the feasibility and performance of the method proposed in this paper.

A Subregion-Based Survival Prediction Based on Multi-Parametric MRIs for Glioblastoma

<h3>Purpose/Objective(s)</h3> To predict the survival of GBM patients by using multi-parametric MR images, we developed a novel framework of subregion-based radiomic analysis. This study aims to search for the best combinations among tumor subregions and various MRI sequences for GBM survival prediction. <h3>Materials/Methods</h3> 119 GBM patients provided by BraTS 2020 were used in the study. 100 cases were used for training and evaluating under 5-fold cross-validation and the external 19 cases were used for independent testing. For each tumor subregion, we used open-source software to extract 680 subregional radiomic features from one MRI sequence for subsequent analysis. We proposed a novel algorithm to uncover an optimal MRIs combination from multiple MRI sequences. By a one-to-one matching relation between 4 sequences MRIs (T1/T1ce/T2/FLAIR) and 3 tumor subregions (gadolinium-enhancing tumor (ET), peritumoral edema (ED), and non-enhancing tumor core (NET)), we evaluated the 64 (4³) kinds of subregional radiomic features combinations (680 × 3) based on the evaluation of maximizing the performance of survival stage classification between long-stage (>365 days) and short-stage (<365 days) groups using the SVC (Support Vector Classification) classifier. The best combination with a quarter size compared to the initial imaging data (680 × 12) can be determined and achieved precisely prediction. <h3>Results</h3> As shown in table, the specific matching relation of NET-FLAIR, ED-T1ce, ET-T2 which used 3-sequence resulted in the highest AUCs of 0.998 and 0.983 on the training and independent test cohort respectively. The model generated from optimized 3 MR sequences of features was superior to those models using one single-sequence, 2-sequence, other combinations of 3-sequence and 4-sequence MRIs. <h3>Conclusion</h3> We demonstrated the efficacy of a novel subregion-based framework for survival prediction of GBM by using multi-parametric MRIs. The proposed approach effectively combined multiple MR images and achieved survival prediction precisely.

Convolutional Neural Networks-Based Framework for Early Identification of Dementia Using MRI of Brain Asymmetry.

Computer-aided diagnosis of health problems and pathological conditions has become a substantial part of medical, biomedical, and computer science research. This paper focuses on the diagnosis of early and progressive dementia, building on the potential of deep learning (DL) models. The proposed computational framework exploits a magnetic resonance imaging (MRI) brain asymmetry biomarker, which has been associated with early dementia, and employs DL architectures for MRI image classification. Identification of early dementia is accomplished by an eight-layered convolutional neural network (CNN) as well as transfer learning of pretrained CNNs from ImageNet. Different instantiations of the proposed CNN architecture are tested. These are equipped with Softmax, support vector machine (SVM), linear discriminant (LD), or [Formula: see text] -nearest neighbor (KNN) classification layers, assembled as a separate classification module, which are attached to the core CNN architecture. The initial imaging data were obtained from the MRI directory of the Alzheimer's disease neuroimaging initiative 3 (ADNI3) database. The independent testing dataset was created using image preprocessing and segmentation algorithms applied to unseen patients' imaging data. The proposed approach demonstrates a 90.12% accuracy in distinguishing patients who are cognitively normal subjects from those who have Alzheimer's disease (AD), and an 86.40% accuracy in detecting early mild cognitive impairment (EMCI).

Mechanical thrombectomy: Determining the proportion of eligible acute ischemic stroke patients in the cohort of single academic stroke center

Background and purposeMechanical thrombectomy (MT) is now well-established treatment method for selected patients with acute ischemic stroke (AIS) and efforts are being made to incorporate it into the systems of stroke care. Our objective is to assess the number of AIS individuals eligible for MT in the cohort of single academic stroke center. MethodsWe retrospectively reviewed initial non-invasive vascular imaging data of AIS patients presenting within 5h of symptom onset for the presence of large vessel occlusion (LVO) over 2-year period (2015–2016). Among subjects confirmed with LVO: time-to-presentation, premorbid functional and on-admission neurological state, site of occlusion and initial imaging data were further assessed. Two sets of criteria based on recent trials and recommendations were used to determine MT eligibility. The onset-to-evaluation time limit was set to 5h allowing ≤60min procedure initiation delay. Results895 patients with the final diagnosis of AIS were admitted to our stroke center as the initial treatment facility. 246 (27.5%) presented within 5h of symptom onset and had non-invasive imaging performed. Among those 102 (41.5%) had causative LVO. The number of ≤5h presenting patients eligible for MT was 51 (20.7%) when applying restrictive or 80 (32.5%) with more permissive criteria. ConclusionAmong AIS patients, in whom onset-to-arrival time allowed to initiate the endovascular procedure within 6h of symptom duration, 21% were eligible for MT treatment according to more and 33% to less restrictive criteria. It accounts for about 6% and 9% of all AIS cases, respectively.

MAPPING OF MANGROVE DISTRIBUTION AND SPATIAL ANALYSIS OF LAND SUITABILITY FOR POND AQUACULTURE IN COASTAL AREA OF GENDING SUB DISTRICT, PROBOLINGGO

The research on mapping of mangrove distribution and spatial analysis of land suitability for pond aquaculture in coastal area of Gending sub District, Probolinggo are to map the distribution of existing mangrove forests along the coastal area of Gending and to determine the land suitability for aquaculture activities. This research used Landsat satellite imagery as an initial imaging data of the study area. Band manypulation using visual band 3-2-1, 4-3-2 and infrared bands 4-5-3. The use of this band based on their sensitivity in capturing the value of a high reflectance of vegetation. Rupa Bumi Indonesia Map scale 1: 25,000 from Bakorsurtanal used as a correction of satellite imagery. Furthermore, the survey field mapping with a model of participatory mapping done to calibrate the field data and sampling of water quality in the study area. Based on the analysis of satellite image and survey found that mangroves are spread almost evenly along the coastal area of Gending with a high density (visually) and identified along the coordinates of 762720 mT, 9144134 mU and 795851 mT, 9144081 mU. While the aquaculture area identified spread along the coastal with the coordinates 751394 mT, 9138874 mU to 776632 mT, 9147895 mU. Through the spatial analysis of land suitability described that most of the pond in the western part of coastal area is very suitable for aquaculture activities. Keywords: Mapping, mangroves, land suitability

DOSIS RADIASI DARI TINDAKAN CT-SCAN KEPALA

The research on mapping of mangrove distribution and spatial analysis of land suitability for pond aquaculture in coastal area of Gending sub District, Probolinggo are to map the distribution of existing mangrove forests along the coastal area of Gending and to determine the land suitability for aquaculture activities. This research used Landsat satellite imagery as an initial imaging data of the study area. Band manypulation using visual band 3-2-1, 4-3-2 and infrared bands 4-5-3. The use of this band based on their sensitivity in capturing the value of a high reflectance of vegetation. Rupa Bumi Indonesia Map scale 1: 25,000 from Bakorsurtanal used as a correction of satellite imagery. Furthermore, the survey field mapping with a model of participatory mapping done to calibrate the field data and sampling of water quality in the study area. Based on the analysis of satellite image and survey found that mangroves are spread almost evenly along the coastal area of Gending with a high density (visually) and identified along the coordinates of 762720 mT, 9144134 mU and 795851 mT, 9144081 mU. While the aquaculture area identified spread along the coastal with the coordinates 751394 mT, 9138874 mU to 776632 mT, 9147895 mU. Through the spatial analysis of land suitability described that most of the pond in the western part of coastal area is very suitable for aquaculture activities. Keywords: Mapping, mangroves, land suitability

Computationally efficient noise reduction filter for enhancement of ultrasound images

In ultrasound imaging, acquired images are corrupted by slowly varying multiplicative non-uniformity. When the image is corrected for non-uniformity alone, noise in the dark regions of the original image becomes multiplicatively enhanced, thereby providing an unnatural look to the image. A pre-filtering technique is used to reduce noise in ultrasound pixel images by shrinking initial image data and processing the shrunken image with known segmentation-based filtering techniques that identify and differentially process structures within the image. The segmentation is based on both gradient threshold and the distance from the near field of the ultrasound image. This modification selectively suppresses near-field artifacts. After processing, the shrunken image is enlarged to the dimensions of the initial data and then blended with the initial image to form the final image. During blending, a small predetermined fraction of intensity-dependent, uniform random noise is added to the non-structure region pixels whose intensities are above a pre-specified intensity threshold, to mitigate ultrasound speckles while leaving non-echogenic regions undisturbed.

Shape Recognition Algorithm Using Simulated Retinal Function for Grafting Robot System

Development of intelligent systems for bioproduction including a grafting system requires high level machine vision techniques. The required task is to extract useful information through the machine vision. It is of interest that introduction of more human like image processing including sofware and hardware aspects into the machine vision system is considered. In this study, a solid state receptor arranged over a polar coordinate system which serves as an artificial retina was fabricated. An algorithm which converts initial image data into a specific pattern containing useful features using 2 dimensional FFT and polar coordinate transformation was developed.

Constrained disparity and motion estimators for 3DTV image sequence coding

This paper presents two-dimensional motion estimation methods which take advantage of the intrinsic redundancies inside 3DTV stereoscopic image sequences. Most of the previous studies extract, either disparity vector fields if they are involved in stereovision, or apparent motion vector fields to be applied to motion compensation coding schemes. For 3DTV image sequence analysis and transmission, we can jointly estimate these two feature fields. Locally, initial image data are grouped within two views (the left and right ones) at two successive time samples and spatio-temporal coherence has to be used to enhance motion vector field estimation. Three different levels of ‘coherence’ have been experimented subject to the crucial assumption that an initial calibration of the stereoscopic sensors provides us with geometric change of coordinates for two matched features.