A goal-driven unsupervised image segmentation method combining graph-based processing and Markov random fields

Image segmentation is the process of partitioning a digital image into a set of homogeneous regions (according to some homogeneity criterion) to facilitate a subsequent higher-level analysis. In this context, the present paper proposes an unsupervised and graph-based method of image segmentation, which is driven by an application goal, namely, the generation of image segments associated with a user-defined and application-specific goal. A graph, together with a random grid of source elements, is defined on top of the input image. From each source satisfying a goal-driven predicate, called seed, a propagation algorithm assigns a cost to each pixel on the basis of similarity and topological connectivity, measuring the degree of association with the reference seed. Then, the set of most significant regions is automatically extracted and used to estimate a statistical model for each region. Finally, the segmentation problem is expressed in a Bayesian framework in terms of probabilistic Markov random field (MRF) graphical modeling. An ad hoc energy function is defined based on parametric models, a seed-specific spatial feature, a background-specific potential, and local-contextual information. This energy function is minimized through graph cuts and, more specifically, the alpha-beta swap algorithm, yielding the final goal-driven segmentation based on the maximum a posteriori (MAP) decision rule. The proposed method does not require deep a priori knowledge (e.g., labelled datasets), as it only requires the choice of a goal-driven predicate and a suited parametric model for the data. In the experimental validation with both magnetic resonance (MR) and synthetic aperture radar (SAR) images, the method demonstrates robustness, versatility, and applicability to different domains, thus allowing for further analyses guided by the generated product

Sustainable cooperative distance learning system for education in developing countries

The use of Information and Communication Technologies (ICTs) in a cooperative way can give a good improvement in the educational field; in developing countries, especially in Sub-Saharan area. Sustainable technologies and collaborative LMS are investigated in a case study as possible ways to improve, towards distance learning systems, education and towards it, social empowerment and development. A web-based system, customized for an academic year proposal in medicine and surgery field, helps the development of cooperation among universities and research empowerment and exchange, useful for both entities and their stakeholders. Different uses and approaches to the technological solution allow different participants to model and interact with the project despite difficulties and environmental gabs that could occur in some territories of the Sub-Saharan countries

Archaeology from mid and late Holocene in southern Patagonia: human peopling and climate fluctuations

Este trabajo busca generar un aporte al conocimiento de la dinámica poblacional de cazadores, en Patagonia meridional, durante el Holoceno medio y tardío en el marco de condiciones climáticas altamente fluctuantes. Para este fin se analizaron distintas líneas de evidencias arqueológicas provenientes de diferentes ambientes del centro-oeste de la provincia de Santa Cruz, Argentina. Se identificó una interesante variabilidad del registro arqueológico, en estrecha relación con las modificaciones ambientales registradas en el Holoceno.This article seeks to generate a contribution to the knowledge of the peopling dynamics of hunters in southern Patagonia during the middle and late Holocene within fluctuating climatic conditions. In order to do this, different archaeological evidences were analyzed from diverse environments in Santa Cruz province (Argentina). An interesting variability in the archaeological record was identified, in close relation with the environmental modifications registered in the Holocene.Fil: Cassiodoro, Gisela Eva. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: Rindel, Diego Damián. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina. Universidad de Buenos Aires; ArgentinaFil: Goñi, Rafael Agustín. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Sociales; Argentina. Universidad de Buenos Aires. Facultad de Filosofía y Letras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; ArgentinaFil: Re, Anahi. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: Tessone, Augusto. Universidad de Buenos Aires; Argentina. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Garcia Guraieb, Solana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; ArgentinaFil: Belardi, Juan Bautista. Universidad Nacional de la Patagonia Austral. Unidad Académica Río Gallegos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Espinosa, Silvana Laura. Universidad Nacional de la Patagonia Austral. Unidad Académica Río Gallegos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Nuevo Delaunay, Amalia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Filosofía y Letras; ArgentinaFil: Dellepiane, Juan Matias. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Sociales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Flores Coni, Josefina. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Universidad de Buenos Aires; ArgentinaFil: Guichon, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; ArgentinaFil: Martinez, Carla. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina. Universidad de Buenos Aires. Facultad de Filosofía y Letras; ArgentinaFil: Pasqualini, Sebastian Diego. Universidad de Buenos Aires. Facultad de Filosofía y Letras; Argentina. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Asociación entre Artritis Reumatoidea y otras enfermedades autoinmunes

Objetivos: determinar la frecuencia de enfermedades autoinmunes (EAI) en pacientes con Artritis Reumatoidea (AR) y comparar la frecuencia de EAI entre pacientes con AR y sin AR ni otra EAI reumatológica. Material y Métodos: estudio multicéntrico, observacional, analítico, retrospectivo. Se incluyeron pacientes consecutivos con AR (ACR/EULAR 2010) y como grupo control pacientes con diagnóstico inicial de Osteoartritis primaria (OA).

COSMO SKYMED IN SUPPORT OF FLOOD MONITORING

In this paper, the exploitation of Earth Observation (EO) data in the operational chains for flood monitoring and post-event damage assessment is addressed, focusing to the specific task of flood mapping. The very-high-resolution (VHR) multitemporal observation capability offered by the current sensors, and in particular by Cosmo-SkyMed Synthetic Aperture Radar (SAR) constellation, is exploited. Two image processing algorithms based on data fusion and segmentation, merged with a preliminary non-linear filtering, are presented for flood maps generation. Both approaches help to locate flooded areas from a pair of SAR images acquired before and after the event. The work is framed in the context of a project funded by the Italian Space Agency. Experiments are performed on Cosmo-SkyMed images acquired in Stripmap configuration, related to different datasets acquired all over the world

Multi-temporal MRI carpal bone volumes analysis by principal axes registration

abstract In this paper, a principal axes registration technique is presented, with the relevant application to segmented volumes. The purpose of the proposed registration is to compare multi-temporal volumes of carpal bones from Magnetic Resonance Imaging (MRI) acquisitions. Starting from the study of the second-order moment matrix, the eigenvectors are calculated to allow the rotation of volumes with respect to reference axes. Then the volumes are spatially translated to become perfectly overlapped. A quantitative evaluation of the results obtained is carried out by computing classical indices from the confusion matrix, which depict similarity measures between the volumes of the same organ as extracted from MRI acquisitions executed at different moments. Within the medical field, the way a registration can be used to compare multi-temporal images is of great interest, since it provides the physician with a tool which allows a visual monitoring of a disease evolution. The segmentation method used herein is based on the graph theory and is a robust, unsupervised and parameters independent method. Patients affected by rheumatic diseases have been considered. \ua9 (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

Adaptive SAR Image Processing Techniques to Support Flood Monitoring from Earth Observation Data

This chapter addresses the exploitation of Earth Observation (EO) data in the operational chains for flood monitoring and post-event damage assessment, focusing specifically on the task of post-flood mapping. In this context, this work provides a general review of our research into image processing techniques with an emphasis on adaptive methods applied to synthetic aperture radar (SAR) images. These procedures involve no restrictions on SAR acquisition parameters (frequency band, polarization, spatial resolution, and observation angle). Depending on the data availability, different maps can be produced. When multi-temporal images are available, two different products can be generated: fast-ready flood maps; and detailed flood maps;. The former is a color composite image that enhances the visualization of changes that have occurred after an event. The latter is a more detailed map obtained after a segmentation process. In contrast, when only an image acquired on a single date is available, a water body map can be generated. All these maps are intended as support for institutional interventions. Since only methods of segmentation and numerical data fusion are applied, such results are not final classification products. They are symbolic and not semantic maps, generated using fast and simple procedures that can be used as input for a classification purpose or employed by the user in other application tasks. The experiments described here were performed on real SAR images related to different datasets. The images were acquired from COSMO-SkyMed (CSK) and RADARSAT satellites

Quality Assessment of Despeckled SAR Images

\u2014In this paper, a novel method for the quality assessment of despeckled SAR images is proposed. This method is based on the observation that the perceived quality of despeckled SAR images is not always appropriately described by classical statistical and deterministic parameters that are proposed in the literature. Various evaluations are performed here. A preliminary visual qualitative evaluation is taken as a reference for the subsequent quantitative assessment. Then, a revised statistical analysis that can solve some of the drawbacks of previous methods is proposed; however, the statistical approach still has certain drawbacks. To address this problem, a new frequency analysis approach is first proposed, together with a definition of the appropriate indexes. In this way, it is possible to select the best filter in terms of noise reduction, edge and texture preservation, while limiting the effect of introduced distortions. While statistical analysis is widely used in the literature, frequency analysis has never been presented for this aim, especially for non-linear filters. We prove that frequency analysis can robustly identify the best filter, taking perceptual considerations into account, even when statistical analysis fails. Despeckling methods based on anisotropic diffusion algorithms are used for a comparison, but the proposed analysis can be applied to any filtering method. Experiments are presented with SAR images from the Italian Cosmo/Skymed constellation. Both Stripmap and Spotlight acquisitions have been evaluated, and to prove the validity of the proposed method with respect to different spatial resolutions and different classes of interest, various classes are considered