Satellite-based precipitation estimation using watershed segmentation and growing hierarchical self-organizing map

This paper outlines the development of a multi-satellite precipitation estimation methodology that draws on techniques from machine learning and morphology to produce high-resolution, short-duration rainfall estimates in an automated fashion. First, cloud systems are identified from geostationary infrared imagery using morphology based watershed segmentation algorithm. Second, a novel pattern recognition technique, growing hierarchical self-organizing map (GHSOM), is used to classify clouds into a number of clusters with hierarchical architecture. Finally, each cloud cluster is associated with co-registered passive microwave rainfall observations through a cumulative histogram matching approach. The network was initially trained using remotely sensed geostationary infrared satellite imagery and hourly ground-radar data in lieu of a dense constellation of polar-orbiting spacecraft such as the proposed global precipitation measurement (GPM) mission. Ground-radar and gauge rainfall measurements were used to evaluate this technique for both warm (June 2004) and cold seasons (December 2004-February 2005) at various temporal (daily and monthly) and spatial (0.04 and 0.25) scales. Significant improvements of estimation accuracy are found classifying the clouds into hierarchical sub-layers rather than a single layer. Furthermore, 2-year (2003-2004) satellite rainfall estimates generated by the current algorithm were compared with gauge-corrected Stage IV radar rainfall at various time scales over continental United States. This study demonstrates the usefulness of the watershed segmentation and the GHSOM in satellite-based rainfall estimations

Automatic Dti-based Parcellation Of The Corpus Callosum Through The Watershed Transform

Introduction: Parcellation of the corpus callosum (CC) in the midsagittal cross-section of the brain is of utmost importance for the study of diffusion properties within this structure. The complexity of this operation comes from the absence of macroscopic anatomical landmarks to help in dividing the CC into different callosal areas. In this paper we propose a completely automatic method for CC parcellation using diffusion tensor imaging (DTI). Methods: A dataset of 15 diffusion MRI volumes from normal subjects was used. For each subject, the midsagital slice was automatically detected based on the Fractional Anisotropy (FA) map. Then, segmentation of the CC in the midsgital slice was performed using the hierarchical watershed transform over a weighted FA-map. Finally, parcellation of the CC was obtained through the application of the watershed transform from chosen markers. Results: Parcellation results obtained were consistent for fourteen of the fifteen subjects tested. Results were similar to the ones obtained from tractography-based methods. Tractography confirmed that the cortical regions associated with each obtained CC region were consistent with the literature. Conclusions: A completely automatic DTI-based parcellation method for the CC was designed and presented. It is not based on tractography, which makes it fast and computationally inexpensive. While most of the existing methods for parcellation of the CC determine an average behavior for the subjects based on population studies, the proposed method reflects the diffusion properties specific for each subject. Parcellation boundaries are found based on the diffusion properties within each individual CC, which makes it more reliable and less affected by differences in size and shape among subjects.302132143Aboitiz, F., Scheibel, A.B., Fisher, R.S., Zaidel, E., Fiber composition of the human corpus callosum (1992) Brain Research, 598 (1-2), pp. 143-153. , http://dx.doi.org/10.1016/0006-8993(92)90178-CBasser, P.J., Pierpaoli, C., Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI (1996) Journal of Magnetic Resonance, Series B, 111 (3), pp. 209-219. , http://dx.doi.org/10.1006/jmrb.1996.0086Basser, P.J., Mattiello, J., LeBihan, D., MR diffusion tensor spectroscopy and imaging (1994) Biophysical Journal, 66 (1), pp. 259-267. , http://dx.doi.org/10.1016/S0006-3495(94)80775-1Beucher, S., Lantuéjoul, C., (1979) Use of watersheds in contour detection, , In: International Workshop on Image Processing: Proceedings of the International Workshop on Image 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Automatic DTI-based parcellation of the corpus callosum through the watershed transform

Parcellation of the corpus callosum (CC) in the midsagittal cross-section of the brain is of utmost importance for the study of diffusion properties within this structure. The complexity of this operation comes from the absence of macroscopic anatomical landmarks to help in dividing the CC into different callosal areas. In this paper we propose a completely automatic method for CC parcellation using diffusion tensor imaging (DTI). A dataset of 15 diffusion MRI volumes from normal subjects was used. For each subject, the midsagital slice was automatically detected based on the Fractional Anisotropy (FA) map. Then, segmentation of the CC in the midsgital slice was performed using the hierarchical watershed transform over a weighted FA-map. Finally, parcellation of the CC was obtained through the application of the watershed transform from chosen markers. Parcellation results obtained were consistent for fourteen of the fifteen subjects tested. Results were similar to the ones obtained from tractography-based methods. Tractography confirmed that the cortical regions associated with each obtained CC region were consistent with the literature. A completely automatic DTI-based parcellation method for the CC was designed and presented. It is not based on tractography, which makes it fast and computationally inexpensive. While most of the existing methods for parcellation of the CC determine an average behavior for the subjects based on population studies, the proposed method reflects the diffusion properties specific for each subject. Parcellation boundaries are found based on the diffusion properties within each individual CC, which makes it more reliable and less affected by differences in size and shape among subjects302132143CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPnão temnão temnão te

Elements of the metacommunity structure : comparison across multiple metacommunities

Les « Éléments de la Structure des Metacommunautés » (EMS) est un outil analytique puissant pour l'évaluation des patrons de distributions d'espèces dans l'espace géographique ou environnementale; par contre, cette technique est encore sous-utilisée parmi les études écologiques. L'objectif de cette étude est d'évaluer les mécanismes structurants les patrons de distributions d'espèces de poissons de lacs boréaux à des multiples échelles en appliquant la technique EMS sur la Ontario Fish Distribution Database, une base de données contenant des informations sur la présence-absence des espèces de poissons de plus de 9000 lacs de l'Ontario ainsi que leurs positions géographiques. Pour chaque lac, l'information sur les variables environnementales on été obtenue grâce au Lake lnventory Database (LINY) et des indices spatiaux, comme la connectivité entre les lacs et leur distance aux refuges postglaciaires, ont été calculés à partir d'informations géographiques. Puis, la relation phylogénétique des espèces et leurs niches B on été estimés pour comprendre le rôle des espèces dans l'assemblage des communautés et formation des metacommunautés. Dans le premier chapitre, la technique EMS a indiqué que nestedness et Clementsian gradients sont les patrons de distributions les plus courants parmi les bassins versants. La pluparts des patrons nestedness se situent dans des bassins de faible énergie contenant des grands lacs et localisés dans de hautes latitudes tandis que les patrons Clementsian gradients sont rencontrés dans des conditions opposés. À l'échelle des bassins, les variables environnementales expliquent en moyenne 9.1% de la variation dans la distribution des espèces pour les deux type de patrons contre moins de 3.5% pour les variables spatiales. À l'échelle provinciale, la variation dans la distribution des espèces est expliquée principalement par les variables environnementales structurées spatialement (29,26%) suivit des variables environnementales indépendantes de l'espace (10.80%). Des tests statistiques suggèrent que le taux de changement dans la composition des communautés, la caractéristique qui mieux distingue les deux patrons, augmente du nord vers le sud, influencé principalement par la latitude et les variables associées (e.g., température). Dans le second chapitre, les résultats indiquent que, à l'échelle du bassin versant, la sous-dispersion phylogénétique prédomine tandis que la sur-dispersion phylogénétique est plus observée à l'échelle locale. La structure phylogénétique et de niche des communautés sont principalement influencés par la taille des lacs, les variables liées à l'énergie (e.g., température, degré-jour de croissance) et la latitude. Dans les régions du Nord, il y a des taux élevés de chevauchement des niches et de plus grande distance phylogénétique entre les espèces qui cohabitent alors que dans les bassins versants du Sud on rencontre le patron inverse. \ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : EMS, analyse de correspondance, Clementsian gradients, distribution d'espèces, nestedness, species turnover, structure phylogénétique, niche, gradient environnementa

Web-based Platform For Collaborative Medical Imaging Research

Medical imaging research depends basically on the availability of large image collections, image processing and analysis algorithms, hardware and a multidisciplinary research team. It has to be reproducible, free of errors, fast, accessible through a large variety of devices spread around research centers and conducted simultaneously by a multidisciplinary team. Therefore, we propose a collaborative research environment, named Adessowiki, where tools and datasets are integrated and readily available in the Internet through a web browser. Moreover, processing history and all intermediate results are stored and displayed in automatic generated web pages for each object in the research project or clinical study. It requires no installation or configuration from the client side and offers centralized tools and specialized hardware resources, since processing takes place in the cloud.941

General Catalog 2007-2009

Contains course descriptions, University college calendar, and college administrationhttps://digitalcommons.usu.edu/universitycatalogs/1127/thumbnail.jp

General Catalog 2009-2010

Contains course descriptions, University college calendar, and college administrationhttps://digitalcommons.usu.edu/universitycatalogs/1128/thumbnail.jp

3-D image segmentation and rendering

Finding methods for detecting objects in computer tomography images has been an active area of research in the medical and industrial imaging communities. While the raw image can be readily displayed as 2-D slices, 3-D analysis and visualization require explicitly defined object boundaries when creating 3-D models. A basic task in 3-D image processing is the segmentation of an image that classifies voxels/pixels into objects or groups. It is very computation intensive for processing because of the huge volume of data. The objective of this research is to find an efficient way to identify, isolate and enumerate 3-D objects in a given data set consisting of tomographic cross-sections of a device under test. In this research, an approach to 3-D image segmentation and rendering of CT data has been developed. Objects are first segmented from the background and then segmented between each other before 3-D rendering. During the first step of segmentation, current techniques of thresholding and image morphology provide a fast way to accomplish the work. During the second step of segmentation, a new method based on the watershed transform has been developed to deal with objects with deep connections. The new method takes advantage of the similarity between consecutive cross section images. The projections of the objects in the first image are taken as catchment basins for the second image. Only the different pixels in the second image are processed during segmentation. This not only saves time to find catchment basins, but also splits objects with deep connections that cannot be simply implemented by the watershed transform. A unique label has been issued to each object after segmentation. Objects can be distinguished well from each 2-D slice by their labels. This is a good preparation for 3-D rendering and quantitative analysis of each object. In this thesis, a novel 3-D rendering has been developed by surface rendering approach. A new and easier rendering model has been invented under the assumptions that light comes from the same side as the viewer, both of which are situated at infinity. It works fast because only surface pixels are being processed and interior pixels are left unprocessed. The surface intensity of the objects is attenuated by coefficients according to their distance from the viewer. The objects finally are shown from top and side views. Volume rendering was accomplished by sample images as well. In this research, the new method works several times faster than previous methods. After successful segmentation and rendering, the volume of each object can be easily calculated and the objects are recognizable in 3-D visualization. Keywords: 3-D Image Segmentation, 3-D Image Rendering, Watershed Transform, Surface Rendering, Thresholding, Morphological Transform