Weather forecaster working at radar scope at U.S. Weather Service

Inscriptions: Verso: [stamped] Photograph by Freda Leinwand. [463 West Street, Studio 229G, New York, NY 10014].

Using Image Processing Methods to Improve the Detection of Buried Explosive Threats in GPR Data

Current state of the art techniques for landmine detection in ground penetrating radar (GPR) utilize statistical methods to identify characteristics of a landmine response. This research makes use of 2-D slices of data in which subsurface landmine responses have hyperbolic shapes. Various methods from the field of visual image processing are adapted to the 2-D GPR data, producing superior landmine detection results. This research goes on to develop a physics-based GPR augmentation method motivated by current advances in visual object detection. This GPR specific augmentation is used to mitigate issues caused by insufficient training sets. This work shows that augmentation improves detection performance under training conditions that are normally very difficult. Finally, this work introduces the use of convolutional neural networks as a method to learn feature extraction parameters. These learned convolutional features outperform hand-designed features in GPR detection tasks. This work presents a number of methods, both borrowed from and motivated by the substantial work in visual image processing. The methods developed and presented in this work show an improvement in overall detection performance and introduce a method to improve the robustness of statistical classification.

Retrieval of grassland biophysical parameters using multitemporal optical and radar satellite data

The amount and quality of available biomass is a key factor for the sustainable livestock industry and agricultural management related decision making. Globally 31.5% of land cover is grassland while 80% of Ireland’s agricultural land is grassland. In Ireland, grasslands are intensively managed and provide the cheapest feed source for animals. This dissertation presents a detailed state of the art review of satellite remote sensing of grasslands, and the potential application of optical (Moderate–resolution Imaging Spectroradiometer (MODIS)) and radar (TerraSAR-X) time series imagery to estimate the grassland biomass at two study sites (Moorepark and Grange) in the Republic of Ireland using both statistical and state of the art machine learning algorithms. High quality weather data available from the on-site weather station was also used to calculate the Growing Degree Days (GDD) for Grange to determine the impact of ancillary data on biomass estimation. In situ and satellite data covering 12 years for the Moorepark and 6 years for the Grange study sites were used to predict grassland biomass using multiple linear regression, Neuro Fuzzy Inference Systems (ANFIS) models. The results demonstrate that a dense (8-day composite) MODIS image time series, along with high quality in situ data, can be used to retrieve grassland biomass with high performance (R2 = 0:86; p < 0:05, RMSE = 11.07 for Moorepark). The model for Grange was modified to evaluate the synergistic use of vegetation indices derived from remote sensing time series and accumulated GDD information. As GDD is strongly linked to the plant development, or phonological stage, an improvement in biomass estimation would be expected. It was observed that using the ANFIS model the biomass estimation accuracy increased from R2 = 0:76 (p < 0:05) to R2 = 0:81 (p < 0:05) and the root mean square error was reduced by 2.72%. The work on the application of optical remote sensing was further developed using a TerraSAR-X Staring Spotlight mode time series over the Moorepark study site to explore the extent to which very high resolution Synthetic Aperture Radar (SAR) data of interferometrically coherent paddocks can be exploited to retrieve grassland biophysical parameters. After filtering out the non-coherent plots it is demonstrated that interferometric coherence can be used to retrieve grassland biophysical parameters (i. e., height, biomass), and that it is possible to detect changes due to the grass growth, and grazing and mowing events, when the temporal baseline is short (11 days). However, it not possible to automatically uniquely identify the cause of these changes based only on the SAR backscatter and coherence, due to the ambiguity caused by tall grass laid down due to the wind. Overall, the work presented in this dissertation has demonstrated the potential of dense remote sensing and weather data time series to predict grassland biomass using machine-learning algorithms, where high quality ground data were used for training. At present a major limitation for national scale biomass retrieval is the lack of spatial and temporal ground samples, which can be partially resolved by minor modifications in the existing PastureBaseIreland database by adding the location and extent ofeach grassland paddock in the database. As far as remote sensing data requirements are concerned, MODIS is useful for large scale evaluation but due to its coarse resolution it is not possible to detect the variations within the fields and between the fields at the farm scale. However, this issue will be resolved in terms of spatial resolution by the Sentinel-2 mission, and when both satellites (Sentinel-2A and Sentinel-2B) are operational the revisit time will reduce to 5 days, which together with Landsat-8, should enable sufficient cloud-free data for operational biomass estimation at a national scale. The Synthetic Aperture Radar Interferometry (InSAR) approach is feasible if there are enough coherent interferometric pairs available, however this is difficult to achieve due to the temporal decorrelation of the signal. For repeat-pass InSAR over a vegetated area even an 11 days temporal baseline is too large. In order to achieve better coherence a very high resolution is required at the cost of spatial coverage, which limits its scope for use in an operational context at a national scale. Future InSAR missions with pair acquisition in Tandem mode will minimize the temporal decorrelation over vegetation areas for more focused studies. The proposed approach complements the current paradigm of Big Data in Earth Observation, and illustrates the feasibility of integrating data from multiple sources. In future, this framework can be used to build an operational decision support system for retrieval of grassland biophysical parameters based on data from long term planned optical missions (e. g., Landsat, Sentinel) that will ensure the continuity of data acquisition. Similarly, Spanish X-band PAZ and TerraSAR-X2 missions will ensure the continuity of TerraSAR-X and COSMO-SkyMed.

Applications of Microwave Antenna Array for Wireless Power Transmission and Radar Imaging in Complex Environment

Thesis (Ph.D.)--University of Washington, 2016-08

Apports de la texture multibande dans la classification orientée-objets d'images multisources (optique et radar).

Résumé : La texture dispose d’un bon potentiel discriminant qui complète celui des paramètres radiométriques dans le processus de classification d’image. L’indice Compact Texture Unit (CTU) multibande, récemment mis au point par Safia et He (2014), permet d’extraire la texture sur plusieurs bandes à la fois, donc de tirer parti d’un surcroît d’informations ignorées jusqu’ici dans les analyses texturales traditionnelles : l’interdépendance entre les bandes. Toutefois, ce nouvel outil n’a pas encore été testé sur des images multisources, usage qui peut se révéler d’un grand intérêt quand on considère par exemple toute la richesse texturale que le radar peut apporter en supplément à l’optique, par combinaison de données. Cette étude permet donc de compléter la validation initiée par Safia (2014) en appliquant le CTU sur un couple d’images optique-radar. L’analyse texturale de ce jeu de données a permis de générer une image en « texture couleur ». Ces bandes texturales créées sont à nouveau combinées avec les bandes initiales de l’optique, avant d’être intégrées dans un processus de classification de l’occupation du sol sous eCognition. Le même procédé de classification (mais sans CTU) est appliqué respectivement sur : la donnée Optique, puis le Radar, et enfin la combinaison Optique-Radar. Par ailleurs le CTU généré sur l’Optique uniquement (monosource) est comparé à celui dérivant du couple Optique-Radar (multisources). L’analyse du pouvoir séparateur de ces différentes bandes à partir d’histogrammes, ainsi que l’outil matrice de confusion, permet de confronter la performance de ces différents cas de figure et paramètres utilisés. Ces éléments de comparaison présentent le CTU, et notamment le CTU multisources, comme le critère le plus discriminant ; sa présence rajoute de la variabilité dans l’image permettant ainsi une segmentation plus nette, une classification à la fois plus détaillée et plus performante. En effet, la précision passe de 0.5 avec l’image Optique à 0.74 pour l’image CTU, alors que la confusion diminue en passant de 0.30 (dans l’Optique) à 0.02 (dans le CTU).

Image Processing Algorithms applied to Radio SLAM using MIMO Radars

Radio Simultaneous Location and Mapping (SLAM) consists of the simultaneous tracking of the target and estimation of the surrounding environment, to build a map and estimate the target movements within it. It is an increasingly exploited technique for automotive applications, in order to improve the localization of obstacles and the target relative movement with respect to them, for emergency situations, for example when it is necessary to explore (with a drone or a robot) environments with a limited visibility, or for personal radar applications, thanks to its versatility and cheapness. Until today, these systems were based on light detection and ranging (lidar) or visual cameras, high-accuracy and expensive approaches that are limited to specific environments and weather conditions. Instead, in case of smoke, fog or simply darkness, radar-based systems can operate exactly in the same way. In this thesis activity, the Fourier-Mellin algorithm is analyzed and implemented, to verify the applicability to Radio SLAM, in which the radar frames can be treated as images and the radar motion between consecutive frames can be covered with registration. Furthermore, a simplified version of that algorithm is proposed, in order to solve the problems of the Fourier-Mellin algorithm when working with real radar images and improve the performance. The INRAS RBK2, a MIMO 2x16 mmWave radar, is used for experimental acquisitions, consisting of multiple tests performed in Lab-E of the Cesena Campus, University of Bologna. The different performances of Fourier-Mellin and its simplified version are compared also with the MatchScan algorithm, a classic algorithm for SLAM systems.

Tradução, adaptação cultural e validação do questionário Body image After Breast Cancer para a língua portuguesa do Brasil

Universidade Estadual de Campinas . Faculdade de Educação Física

Tradução, adaptação transcultural e validação do "Body Image Avoidance Questionnaire (BIAQ)" e do "Body Checking Questionnaire (BCQ)" para lingua portuguesa no Brasil

Universidade Estadual de Campinas . Faculdade de Educação Física

Juvenile idiopathic arthritis with involvement of the temporomandibular joint: the role of image examinations

PURPOSE: Juvenile idiopathic arthritis (JIA) has unknown etiology, and the involvement of the temporomandibular joint (TMJ) is rare in the early phase of the disease. The present article describes the use of computed tomography (CT) and magnetic resonance (MRI) images for the diagnosis of affected TMJ in JIA. CASE DESCRIPTION: A 12-year-old, female, Caucasian patient, with systemic rheumathoid arthritis and involvement of multiple joints was referred to the Imaging Center for TMJ assessment. The patient reported TMJ pain and limited opening of the mouth. The helical CT examination of the TMJ region showed asymmetric mandibular condyles, erosion of the right condyle and osteophyte-like formation. The MRI examination showed erosion of the right mandibular condyle, osteophytes, displacement without reduction and disruption of the articular disc. CONCLUSION: The disorders of the TMJ as a consequence of JIA must be carefully assessed by modern imaging methods such as CT and MRI. CT is very useful for the evaluation of discrete bone changes, which are not identified by conventional radiographs in the early phase of JIA. MRI allows the evaluation of soft tissues, the identification of acute articular inflammation and the differentiation between pannus and synovial hypertrophy.

Classificação de sistemas meteorológicos e comparação da precipitação estimada pelo radar e medida pela rede telemétrica na bacia hidrográfica do alto Tietê

Foram analisadas características da precipitação estimada a partir de 145.194 campos de refletividade, de um total de 827 dias entre 1998 e 2003, obtidos do Radar Meteorológico de São Paulo (RSP). Os eventos foram classificados de acordo com intensidades de precipitação; em Convectivos (EC) e Estratiformes (EE). Quanto à morfologia, cinco tipos de sistemas foram identificados; Convecção Isolada (CI), Brisa Marítima (BM), Linhas de Instabilidade (LI), Bandas Dispersas (BD) e Frentes Frias (FF). Eventos convectivos dominam na primavera e verão e estratiformes no outono e inverno. A CI e a BM tiveram maiores picos de atuação entre outubro e março enquanto as FF de abril a setembro. BD atuam durante todo o ano e as LI só não foram observadas nos meses de junho e julho. Uma comparação pontual entre a precipitação medida pela telemetria e estimada com o radar foi realizada e, mostrou haver, na maioria dos casos, um viés positivo do RSP, para acumulações de 10, 30 e 60 minutos. Com o objetivo de integrar as estimativas de precipitação do radar com as medidas da rede telemétrica, por meio de uma análise objetiva estatística, foram obtidas dos campos de precipitação do radar as estruturas das correlações espaciais em função da distância para acumulações de chuva de 15, 30, 60 e 120 minutos para os cinco tipos de sistemas precipitantes que foram caracterizados. As curvas das correlações espaciais médias de todos os eventos de precipitação de cada sistema foram ajustadas por funções polinomiais de sexta ordem. Os resultados indicam diferenças significativas na estrutura espacial das correlações entre os sistemas precipitantes.

Evaluation of follicular lymphoid depletion in the Bursa of Fabricius: an alternative methodology using digital image analysis and artificial neural networks

Fifty Bursa of Fabricius (BF) were examined by conventional optical microscopy and digital images were acquired and processed using Matlab® 6.5 software. The Artificial Neuronal Network (ANN) was generated using Neuroshell® Classifier software and the optical and digital data were compared. The ANN was able to make a comparable classification of digital and optical scores. The use of ANN was able to classify correctly the majority of the follicles, reaching sensibility and specificity of 89% and 96%, respectively. When the follicles were scored and grouped in a binary fashion the sensibility increased to 90% and obtained the maximum value for the specificity of 92%. These results demonstrate that the use of digital image analysis and ANN is a useful tool for the pathological classification of the BF lymphoid depletion. In addition it provides objective results that allow measuring the dimension of the error in the diagnosis and classification therefore making comparison between databases feasible.

Decision theory applied to image quality control in radiology

Background: The present work aims at the application of the decision theory to radiological image quality control ( QC) in diagnostic routine. The main problem addressed in the framework of decision theory is to accept or reject a film lot of a radiology service. The probability of each decision of a determined set of variables was obtained from the selected films. Methods: Based on a radiology service routine a decision probability function was determined for each considered group of combination characteristics. These characteristics were related to the film quality control. These parameters were also framed in a set of 8 possibilities, resulting in 256 possible decision rules. In order to determine a general utility application function to access the decision risk, we have used a simple unique parameter called r. The payoffs chosen were: diagnostic's result (correct/incorrect), cost (high/low), and patient satisfaction (yes/no) resulting in eight possible combinations. Results: Depending on the value of r, more or less risk will occur related to the decision-making. The utility function was evaluated in order to determine the probability of a decision. The decision was made with patients or administrators' opinions from a radiology service center. Conclusion: The model is a formal quantitative approach to make a decision related to the medical imaging quality, providing an instrument to discriminate what is really necessary to accept or reject a film or a film lot. The method presented herein can help to access the risk level of an incorrect radiological diagnosis decision.

Chrysotile effects on human lung cell carcinoma in culture: 3-D reconstruction and DNA quantification by image analysis

Background: Chrysotile is considered less harmful to human health than other types of asbestos fibers. Its clearance from the lung is faster and, in comparison to amphibole forms of asbestos, chrysotile asbestos fail to accumulate in the lung tissue due to a mechanism involving fibers fragmentation in short pieces. Short exposure to chrysotile has not been associated with any histopathological alteration of lung tissue. Methods: The present work focuses on the association of small chrysotile fibers with interphasic and mitotic human lung cancer cells in culture, using for analyses confocal laser scanning microscopy and 3D reconstructions. The main goal was to perform the analysis of abnormalities in mitosis of fibers-containing cells as well as to quantify nuclear DNA content of treated cells during their recovery in fiber-free culture medium. Results: HK2 cells treated with chrysotile for 48 h and recovered in additional periods of 24, 48 and 72 h in normal medium showed increased frequency of multinucleated and apoptotic cells. DNA ploidy of the cells submitted to the same chrysotile treatment schedules showed enhanced aneuploidy values. The results were consistent with the high frequency of multipolar spindles observed and with the presence of fibers in the intercellular bridge during cytokinesis. Conclusion: The present data show that 48 h chrysotile exposure can cause centrosome amplification, apoptosis and aneuploid cell formation even when long periods of recovery were provided. Internalized fibers seem to interact with the chromatin during mitosis, and they could also interfere in cytokinesis, leading to cytokinesis failure which forms aneuploid or multinucleated cells with centrosome amplification.

Comparison of multispectral wide-field optical imaging modalities to maximize image contrast for objective discrimination of oral neoplasia

Multispectral widefield optical imaging has the potential to improve early detection of oral cancer. The appropriate selection of illumination and collection conditions is required to maximize diagnostic ability. The goals of this study were to (i) evaluate image contrast between oral cancer/precancer and non-neoplastic mucosa for a variety of imaging modalities and illumination/collection conditions, and (ii) use classification algorithms to evaluate and compare the diagnostic utility of these modalities to discriminate cancers and precancers from normal tissue. Narrowband reflectance, autofluorescence, and polarized reflectance images were obtained from 61 patients and 11 normal volunteers. Image contrast was compared to identify modalities and conditions yielding greatest contrast. Image features were extracted and used to train and evaluate classification algorithms to discriminate tissue as non-neoplastic, dysplastic, or cancer; results were compared to histologic diagnosis. Autofluorescence imaging at 405-nm excitation provided the greatest image contrast, and the ratio of red-to-green fluorescence intensity computed from these images provided the best classification of dysplasia/cancer versus non-neoplastic tissue. A sensitivity of 100% and a specificity of 85% were achieved in the validation set. Multispectral widefield images can accurately distinguish neoplastic and non-neoplastic tissue; however, the ability to separate precancerous lesions from cancers with this technique was limited. (C) 2010 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3516593]

Improvements on ICA mixture models for image pre-processing and segmentation

Today several different unsupervised classification algorithms are commonly used to cluster similar patterns in a data set based only on its statistical properties. Specially in image data applications, self-organizing methods for unsupervised classification have been successfully applied for clustering pixels or group of pixels in order to perform segmentation tasks. The first important contribution of this paper refers to the development of a self-organizing method for data classification, named Enhanced Independent Component Analysis Mixture Model (EICAMM), which was built by proposing some modifications in the Independent Component Analysis Mixture Model (ICAMM). Such improvements were proposed by considering some of the model limitations as well as by analyzing how it should be improved in order to become more efficient. Moreover, a pre-processing methodology was also proposed, which is based on combining the Sparse Code Shrinkage (SCS) for image denoising and the Sobel edge detector. In the experiments of this work, the EICAMM and other self-organizing models were applied for segmenting images in their original and pre-processed versions. A comparative analysis showed satisfactory and competitive image segmentation results obtained by the proposals presented herein. (C) 2008 Published by Elsevier B.V.