Evaluating the utility of multispectral information in delineating the areal extent of precipitation

Data from geosynchronous Earth-orbiting (GEO) satellites equipped with visible (VIS) and infrared (IR) scanners are commonly used in rain retrieval algorithms. These algorithms benefit from the high spatial and temporal resolution of GEO observations, either in stand-alone mode or in combination with higher-quality but less frequent microwave observations from low Earth-orbiting (LEO) satellites. In this paper, a neural network-based framework is presented to evaluate the utility of multispectral information in improving rain/no-rain (R/NR) detection. The algorithm uses the powerful classification features of the self-organizing feature map (SOFM), along with probability matching techniques to map single- or multispectral input space into R/NR maps. The framework was tested and validated using the 31 possible combinations of the five Geostationary Operational Environmental Satellite 12 (GOES-12) channels. An algorithm training and validation study was conducted over the conterminous United States during June-August 2006. The results indicate that during daytime, the visible channel (0.65 μm) can yield significant improvements in R/NR detection capabilities, especially when combined with any of the other four GOES-12 channels. Similarly, for nighttime detection the combination of two IR channels - particularly channels 3 (6.5 μm) and 4 (10.7 μm)-resulted in significant performance gain over any single IR channel. In both cases, however, using more than two channels resulted only in marginal improvements over two-channel combinations. Detailed examination of event-based images indicate that the proposed algorithm is capable of extracting information useful to screen no-rain pixels associated with cold, thin clouds and identifying rain areas under warm but rainy clouds. Both cases have been problematic areas for IR-only algorithms. © 2009 American Meteorological Society

Analysis and evaluation of fragment size distributions in rock blasting at the Erdenet Mine

Master's Project (M.S.) University of Alaska Fairbanks, 2015Rock blasting is one of the most important operations in mining. It significantly affects the subsequent comminution processes and, therefore, is critical to successful mining productions. In this study, for the evaluation of the blasting performance at the Erdenet Mine, we analyzed rock fragment size distributions with the digital image processing method. The uniformities of rock fragments and the mean fragment sizes were determined and applied in the Kuz-Ram model. Statistical prediction models were also developed based on the field measured parameters. The results were compared with the Kuz-Ram model predictions and the digital image processing measurements. A total of twenty-eight images from eleven blasting patterns were processed, and rock size distributions were determined by Split-Desktop program in this study. Based on the rock mass and explosive properties and the blasting parameters, the rock fragment size distributions were also determined with the Kuz-Ram model and compared with the measurements by digital image processing. Furthermore, in order to improve the prediction of rock fragment size distributions at the mine, regression analyses were conducted and statistical models w ere developed for the estimation of the uniformity and characteristic size. The results indicated that there were discrepancies between the digital image measurements and those estimated by the Kuz-Ram model. The uniformity indices of image processing measurements varied from 0.76 to 1.90, while those estimate by the Kuz-Ram model were from 1.07 to 1.13. The mean fragment size of the Kuz-Ram model prediction was 97.59% greater than the mean fragment size of the image processing. The multivariate nonlinear regression analyses conducted in this study indicated that rock uniaxial compressive strength and elastic modulus, explosive energy input in the blasting, bench height to burden ratio and blast area per hole were significant predictor variables in determining the fragment characteristic size and the uniformity index. The regression models developed based on the above predictor variables showed much closer agreement with the measurements

Daytime precipitation estimation using bispectral cloud classification system

Two previously developed Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) algorithms that incorporate cloud classification system (PERSIANN-CCS) and multispectral analysis (PERSIANN-MSA) are integrated and employed to analyze the role of cloud albedo from Geostationary Operational Environmental Satellite-12 (GOES-12) visible (0.65 μm) channel in supplementing infrared (10.7 mm) data. The integrated technique derives finescale (0.04° × 0.04° latitudelongitude every 30 min) rain rate for each grid box through four major steps: 1) segmenting clouds into a number of cloud patches using infrared or albedo images; 2) classification of cloud patches into a number of cloud types using radiative, geometrical, and textural features for each individual cloud patch; 3) classification of each cloud type into a number of subclasses and assigning rain rates to each subclass using a multidimensional histogram matching method; and 4) associating satellite gridbox information to the appropriate corresponding cloud type and subclass to estimate rain rate in grid scale. The technique was applied over a study region that includes the U.S. landmass east of 115°W. One reference infrared-only and three different bis-pectral (visible and infrared) rain estimation scenarios were compared to investigate the technique's ability to address two major drawbacks of infrared-only methods: 1) underestimating warm rainfall and 2) the inability to screen out no-rain thin cirrus clouds. Radar estimates were used to evaluate the scenarios at a range of temporal (3 and 6 hourly) and spatial (0.04°, 0.08°, 0.12°, and 0.24° latitude-longitude) scales. Overall, the results using daytime data during June-August 2006 indicate that significant gain over infrared-only technique is obtained once albedo is used for cloud segmentation followed by bispectral cloud classification and rainfall estimation. At 3-h, 0.04° resolution, the observed improvement using bispectral information was about 66% for equitable threat score and 26% for the correlation coefficient. At coarser 0.24° resolution, the gains were 34% and 32% for the two performance measures, respectively. © 2010 American Meteorological Society

PERSIANN-MSA: A precipitation estimation method from satellite-based multispectral analysis

Visible and infrared data obtained from instruments onboard geostationary satellites have been extensively used for monitoring clouds and their evolution. The Advanced Baseline Imager (ABI) that will be launched onboard the Geostationary Operational Environmental Satellite-R (GOES-R) series in the near future will offer a larger range of spectral bands; hence, it will provide observations of cloud and rain systems at even finer spatial, temporal, and spectral resolutions than are possible with the current GOES. In this paper, a new method called Precipitation Estimation from Remotely Sensed information using Artificial Neural Networks-Multispectral Analysis (PERSIANN-MSA) is proposed to evaluate the effect of using multispectral imagery on precipitation estimation. The proposed approach uses a self-organizing feature map (SOFM) to classify multidimensional input information, extracted from each grid box and corresponding textural features of multispectral bands. In addition, principal component analysis (PCA) is used to reduce the dimensionality to a few independent input features while preserving most of the variations of all input information. The above method is applied to estimate rainfall using multiple channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite. In comparison to the use of a single thermal infrared channel, the analysis shows that using multispectral data has the potential to improve rain detection and estimation skills with an average of more than 50% gain in equitable threat score for rain/no-rain detection, and more than 20% gain in correlation coefficient associated with rain-rate estimation. © 2009 American Meteorological Society

Tumour auto-contouring on 2d cine MRI for locally advanced lung cancer: A comparative study

BACKGROUND AND PURPOSE: Radiotherapy guidance based on magnetic resonance imaging (MRI) is currently becoming a clinical reality. Fast 2d cine MRI sequences are expected to increase the precision of radiation delivery by facilitating tumour delineation during treatment. This study compares four auto-contouring algorithms for the task of delineating the primary tumour in six locally advanced (LA) lung cancer patients. MATERIAL AND METHODS: Twenty-two cine MRI sequences were acquired using either a balanced steady-state free precession or a spoiled gradient echo imaging technique. Contours derived by the auto-contouring algorithms were compared against manual reference contours. A selection of eight image data sets was also used to assess the inter-observer delineation uncertainty. RESULTS: Algorithmically derived contours agreed well with the manual reference contours (median Dice similarity index: ⩾0.91). Multi-template matching and deformable image registration performed significantly better than feature-driven registration and the pulse-coupled neural network (PCNN). Neither MRI sequence nor image orientation was a conclusive predictor for algorithmic performance. Motion significantly degraded the performance of the PCNN. The inter-observer variability was of the same order of magnitude as the algorithmic performance. CONCLUSION: Auto-contouring of tumours on cine MRI is feasible in LA lung cancer patients. Despite large variations in implementation complexity, the different algorithms all have relatively similar performance

Development and assessment of estimate methods for internal dosimetry using PET/CT

The aim of this thesis was to assess and develop internal dose calculations methods in diagnostic and therapeutic nuclear medicine procedures to patients undergone PET/CT explorations. Towards this objective, the accuracy and precision of different classical methods commonly used to estimate internal dosimetry were investigated. Biodistribution studies were used in order to compare these methods. The main study aspects included region-of-interest (ROI) delineation methods, reconstruction algorithms, scatter correction and radiopharmaceutical's biokinetic. Optimization of internal dosimetry in this thesis was completed with the development of a Monte Carlo (MC) technique for estimating the patient-specific PET/CT dosimetry. The development of a mathematical model using MC techniques allowed us to have a gold standard to which compare classical techniques and study the aspects discussed previously. It was observed that effective dose (ED) estimations were sensitive to whichever delineation ROI method was applied. Furthermore, it was perceived that the biokinetics of the radioligand also influences in the ED estimation. On the other hand, similar quantitative accuracy was found regarding image reconstruction (FBP and OSEM) and scatter correction methods studied (FSC and SSC). Analysis of the impact of inter- and intra-operator variability in dose estimations revealed higher reproducibility in 3D methods in comparison with 2D planar method. The last one, showed the highest interoperator variability, which implies an overestimation of the ED. In this dissertation, specific routines were developed to be applied with the MC code PENELOPE/penEasy to perform individualized internal dosimetry estimations. Voxel-level absorbed dose maps which include self- and cross-irradiation doses were generated from the morphological and functional patient images. Further parameters such as cumulative organ dose, maximum and minimum voxel organ values, volume of the organ and dose-volume histograms of interest were reported. The model implemented was applied to a theoretical study using simulated PET images of a voxelized Zubal phantom. The results were benchmarked with the ones obtained using the OLINDA/EXM software. The comparison was in good agreement for those organs were both phantoms considered (Zubal and the reference one in OLINDA/EXM) were close. Undoubtedly, the implementation of a patient-specific internal dosimetry method not only leads to an improvement in diagnostic examinations where the risk could be quantified, but also NM therapy could become more effective in terms that patients receiving an optimal care.L'objectiu d'aquesta tesi va ser avaluar i desenvolupar mètodes de càlcul de dosis interna en procediments de diagnòstic i terapèutics de medicina nuclear per a pacients sotmesos a exploracions PET / TC. Amb aquest objectiu, es va investigar l'exactitud i la precisió dels diferents mètodes clàssics utilitzats habitualment per estimar la dosimetria interna. Es van utilitzar estudis de biodistribució per comparar aquests mètodes. Els principals aspectes d'estudi incloïen mètodes de delimitació de la regió d'interès (ROI), algoritmes de reconstrucció, correcció de dispersió i biocinètiques de radiofàrmacs. L'optimització de la dosimetria interna en aquesta tesi es va completar amb el desenvolupament d'una tècnica de Monte Carlo (MC) per a estimar la dosimetria PET / TC específica del pacient. El desenvolupament d'un model matemàtic amb tècniques de MC ens va permetre tenir una referència amb la que comparar les tècniques clàssiques i estudiar els aspectes descrits anteriorment. Es va observar que les estimacions de la dosi efectiva (DE) eren sensibles a qualsevol mètode de delimitació de la ROI aplicada. A més a més, es va percebre que la biocinètica del radiolligand també influeix en l'estimació de la DE. D'altra banda, es va trobar una exactitud quantitativament similar pel que fa a la reconstrucció d'imatges (FBP i OSEM) i els mètodes de correcció de dispersió estudiats (FSC i SSC). L'anàlisi de l'impacte de la variabilitat entre operadors i intra-operadors en les estimacions de dosis va mostrar una major reproductibilitat en els mètodes 3D en comparació amb el mètode planar 2D. Aquest últim, va mostrar la màxima variabilitat entre operadors, la qual cosa implica una sobreestimació de la DE. En aquesta tesi, es van desenvolupar rutines específiques per aplicar-les amb el codi MC PENELOPE / penEasy per a realitzar estimacions de dosimetria interna individualitzades. Es van generar mapes de dosis absorbida a nivell de voxel que incloïen dosis d? autoirradiació i irradiació creuada a partir de les imatges morfològiques i funcionals del pacient. Es van reportar altres paràmetres d?interès com la dosi d'òrgan acumulada, els valors màxims i mínims de l'òrgan i del vòxel, el volum de l'òrgan i els histogrames de dosi-volum. El model implementat es va aplicar a un estudi teòric mitjançant imatges simulades de PET d'un maniquí de Zubal voxelitzat. Els resultats es van comparar amb els obtinguts mitjançant el programa OLINDA / EXM. Es va observar un bon acord per a aquells òrgans semblants entre el maniquí de Zubal i el maniquí de referència del software OLINDA/EXM. Sens dubte, la implementació d'un mètode de dosimetria interna específic per al pacient no només condueix a una millora en les exploracions de diagnòstic on es pot quantificar el risc d?irradiació, sinó que la teràpia amb medicina nuclear podria ser més eficaç en termes que els pacients rebin un tractament òptim.Postprint (published version

The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1 The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Haraemail , S. Green and T. McCarthy DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019 PDF Abstract Article PDF References Recommendations Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales

Implementation and Training of Convolutional Neural Networks for the Segmentation of Brain Structures

Precise delivery of radiotherapy depends on accurate segmentation of the anatomical structures surrounding the cancer tissue. With increasing knowledge of radio-sensitivity of critical brain structures, more detailed contouring of a range of structures is required. Manual segmentation is time-consuming, and research into methods for auto segmentation has advanced in the past decade. This thesis presents a general-purpose convolutional neural network with the U-net architecture for auto-segmenting the brain, brainstem, Papez Circuit, and right hippocampus. Several different models were trained using T1 MRI, T2 MRI, and CT images to compare the performance of models trained with the different modalities. Low-level preprocessing was done to the images before training, and the Dice score measured model performance. The best performing model for segmentation of the full brain resulted in a Dice score of 0.98, whereas the segmentation of the brainstem achieved a Dice score of 0.73. Furthermore, segmentation of the complex structure Papez Circuit attained Dice score of 0.52, and segmentation of the hippocampus resulted in a Dice score of 0.49. The selected model performed well in segmentation of the full brain and decent for the brainstem compared to similar studies. In contrast, the segmentation results for the hippocampus were slightly lower than previously reported results. No comparison was found for the segmentation results of the Papez Circuit. More preprocessing and patient data is necessary to provide accurate segmentation of the smaller structures. The dataset presented a few problems, and it was discovered that a similar acquisition method for image sequences gives better results. The network architecture provides a solid framework for segmentation.Masteroppgave i medisinsk teknologiMTEK39