International audienceSegmenting the heart in medical images is a challenging and important task for many applications. In particular, segmenting the heart in CT images is very useful for cardiology and oncological applications such as radiotherapy. Although the majority of methods in the literature are designed for ventricle segmentation, there is a real interest in segmenting the heart as a whole in this modality. In this paper, we address this problem and propose an automatic and robust method, based on anatomical knowledge about the heart, in particular its position with respect to the lungs. This knowledge is represented in a fuzzy formalism and it is used both to define a region of interest and to drive the evolution of a deformable model in order to segment the heart inside this region. The proposed method has been applied on non-contrast CT images and the obtained results have been compared to manual segmentations of the heart, showing the good accuracy and high robustness of our approach

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The analysis of spatial relations among objects in an image is a important vision problem that involves both shape analysis and structural pattern recognition. In this paper, we propose a new approach to characterize the spatial relation along, an important feature of spatial configuration in space that has been overlooked in the literature up to now. We propose a mathematical definition of the degree to which an object A is along an object B, based on the region between A and B and a degree of elongatedness of this region. In order to better fit the perceptual meaning of the relation, distance information is included as well. Experimental results obtained using synthetic shapes and brain structures in medical imaging corroborate the proposed model and the derived measures, thus showing their adequation with the common sense.C. M. Takemura is grateful to CAPES (BEX 3402/04-5). R. Cesar Jr. is grateful to FAPESP (99/12765-2), to CAPES and to CNPq (300722/98-2 and 474596/2004-4).

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The SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm and the radiation use efficiency (RUE) model were coupled to test large-scale environmental indicators in the Brazilian biomes. The MODIS MOD13Q1 reflectance product and weather data were used along the year 2016. The analyzed biomes were Amazon (AM), Caatinga (CT), Cerrado (CE), Pantanal (PT), Atlantic Forest (AF), and Pampa (PP). Significant differences on precipitation (P), actual evapotranspiration (ET), and biomass production (BIO) yielded differences on water balance (WB = P - ET) and water productivity (WP = ET/BIO). The highest WB and WP along the year were for the wettest AM, AF, and PP biomes, when compared with the driest CT. Precipitation (P) distribution along the year affected the magnitude of the evaporative fraction (ETf), i.e, the ratio of ET to reference evapotranspiration (ET0), however there was a gap between ETf and WB, what can be related to the time needed for recovering the good conditions of soil moisture levels after rainfalls. For some biomes, BIO was related to the levels of absorbed photosynthetically active radiation (PARabs), which depends on the leaf area and soil cover (AM, AF, and PP), while for others BIO followed the soil moisture levels, represented by ETf (CT, CE, and PT). The large-scale modelling presented suitability for monitoring environmental parameters at a 250-m spatial and 16-day spatial and temporal resolutions, with great potential to subsidize public policies regarding the management and conservation of the natural resources, with possibility for replication of the methods in other countries.

The SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm and the radiation use efficiency (RUE) model were coupled to test large-scale remote sensing environmental indicators in the Brazilian biomes. The MODIS MOD13Q1 reflectance product and gridded weather data were used for the year 2016. The analyzed biomes were Amazon, Caatinga, Cerrado, Pantanal, Atlantic Forest, and Pampa. Significant differences on precipitation (P), actual evapotranspiration (ET), and biomass production (BIO) yielded differences on water balance (WB = P - ET) and water productivity (WP = ET/BIO). The highest WB and WP differences along the year were for the wettest Amazon, Atlantic Forest, and Pampa biomes, when compared with the driest Caatinga biome. Rainfall distribution along the year affected the magnitude of the evaporative fraction (ETf), i.e, the ratio of ET to reference evapotranspiration (ET0), however there was a gap between ETf and WB, what can be related to the time needed for recovering the good soil moisture conditions after the rainy seasons. For some biomes, BIO was more related to the levels of absorbed photosynthetically active radiation (Amazon, Atlantic Forest, and Pampa), while for others BIO followed more the soil moisture levels, represented by ETf (Caatinga, Cerrado, and Pantanal). The large-scale modelling presented suitability for monitoring environmental indicators, opening the room to detect anomalies for specific periods along the year by using historical images and weather data, with great potential to subsidize public policies regarding the management and conservation of the natural resources and possibility for replication of the methods in other countries.