The rupture mechanism of intracranial aneurysms is still not fully understood. Although the size of the aneurysm is the shape index most commonly used to predict rupture, some controversy still
exists about its adequateness as an aneurysm rupture predictor. In this work, an automatic method to geometrically characterize the shape of cerebral saccular aneurysms using 3D moment invariants is proposed. Geometric moments are efficiently computed via application of the Divergence Theorem over the aneurysm surface using a non-structured mesh. 3D models of the aneurysm and its connected parent vessels have been reconstructed from segmentations of both 3DRA and CTA images. Two alternative approaches have been used for segmentation, the first one based on isosurface deformable models, and the second one based on the level set method. Several experiments were also conducted to both assess the influence of pre-processing steps in the stability of the aneurysm shape descriptors, and to know the robustness of the proposed method. Moment invariants have proved to be a robust technique while providing a reliable way to discriminate between ruptured and unruptured aneurysms (Sensitivity=0.83, Specificity=0.74) on a data set containing 55 aneurysms. Further investigation over larger databases is necessary to establish their adequateness as reliable predictors of rupture risk.
The study of eye movements provides useful insight into the cognitive processes underlying visual search tasks. The analysis of the dynamics of eye movements has often been approached from a purely spatial perspective. In many cases, however, it may not be possible to define meaningful or consistent dynamics without considering the features underlying the scan paths. In this paper, the definition of the feature space has been attempted through the concept of visual similarity and non-linear low dimensional embedding, which defines a mapping from the image space into a low dimensional feature manifold that preserves the intrinsic similarity of image patterns. This has enabled the definition of perceptually meaningful features without the use of domain specific knowledge. Based on this, this paper introduces a new concept called Feature Space Transient Fixation Moments (TFM). The approach presented tackles the problem of feature space representation of visual search through the use of TFM. We demonstrate the practical values of this concept for characterizing the dynamics of eye movements in goal directed visual search tasks. We also illustrate how this model can be used to elucidate the fundamental steps involved in skilled search tasks through the evolution of transient fixation moments.
Estimation of Sea Surface Temperature (SST) from split- window algorithms for NOAA-AVHRR data can be determined with rms values of 0.7 K on a global basis. However, this figure is not compatible with the stringent accuracy of 0.3 K required by climate studies. Among the different sources of errors, the presence of tropospheric aerosols in the satellite field of view prevents the retrieval of accurate satellite SSTs. Still, the effect of aerosols on temperature measurements derived from remote sensing techniques has been traditionally overlooked. Very few studies have addressed the problem of giving split-window algorithms which incorporate aerosol correction, although retrieving algorithms of the aerosol loading from the images do exist. The aim of this study is the evaluation of the effect of the aerosols on the SST MODTRAN code. Such code was used to compute the upwelling radiances and, subsequently k, the brightness temperatures under cloud-free conditions. The filter response functions for the NOAA14 instrument are used to produce theoretical brightness temperatures for the zenith angles: 0 degrees, 30 degrees and 55 degrees. The results show that for most of all the atmospheres that we have considered, deviations as far as 0.8 K are reached compared with the case in which the aerosols are not considered. It is important to point up that deviations higher than 0.4K are able to mask the improvement introduced by a diminution of the Noise Equivalent Temperature in the new sensors as a consequence of error propagation.
Multi-angle algorithms for estimating sea and land surface temperature with ATSR data require a precise knowledge of the angular variation of surface emissivity in the thermal infrared. Nowadays, very few measurements do exist of this variation. In this work an experimental investigation of the angular variation of the infrared emissivity in the thermal infrared (8 - 14 micrometer) band of some representative samples has been made at angles of 0 degrees - 65 degrees (at 5 degree increments) to the surface normal. The results show a decrease of the emissivity with increasing viewing angles, being water the substance with highest angular dependence (about 7% from 0 degree to 65 degree views). Clay, sand, slimy and gravel show variations about 1 - 3% in the same range of views while an homogeneous grass cover does not show angular dependence. Finally, we include an evaluation of the impact that these data can produce in the algorithms for determining land and sea surface temperature from double angle views.
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