Gender Differences in White Matter Microstructure

Sexual dimorphism in human brain structure is well recognised, but little is known about gender differences in white matter microstructure. We used diffusion tensor imaging to explore differences in fractional anisotropy (FA), an index of microstructural integrity.A whole brain analysis of 135 matched subjects (90 men and 45 women) using a 1.5 T scanner. A region of interest (ROI) analysis was used to confirm those results where proximity to CSF raised the possibility of partial-volume artefact.Men had higher fractional anisotropy (FA) in cerebellar white matter and in the left superior longitudinal fasciculus; women had higher FA in the corpus callosum, confirmed by ROI.The size of the differences was substantial--of the same order as that attributed to some pathology--suggesting gender may be a potentially significant confound in unbalanced clinical studies. There are several previous reports of difference in the corpus callosum, though they disagree on the direction of difference; our findings in the cerebellum and the superior longitudinal fasciculus have not previously been noted. The higher FA in women may reflect greater efficiency of a smaller corpus callosum. The relatively increased superior longitudinal fasciculus and cerebellar FA in men may reflect their increased language lateralisation and enhanced motor development, respectively

Un algorithme de compression efficace de LUTs 3D couleur basé sur un schéma de diffusion anisotrope multi-échelle

International audience3D CLUTs (Color Look Up Tables) are popular digital models used in image and video processing for color grading, simulation of analog films, and more generally for the application of various colorimetric transformations. The large size of these models leads to data storage issues when trying to distribute them on a large scale. In this paper, a highly effective lossy compression technique for 3D CLUTs is proposed. It is based on a multi-scale anisotropic diffusion reconstruction scheme. Our method exhibits an average compression rate of more than 99%, while ensuring visually indistinguishable differences with the application of the original CLUTs .Les CLUTs 3D (Color Look Up Tables) sont des modèles numériques très utilisés en traitement d'images et de vidéos, pour l’étalonnage couleur, la simulation de films argentiques, et plus généralement pour l'application de transformées colorimétriques quelconques. La dimension élevée de ces modèles pose des problèmes de stockage, lorsque l'on cherche à les distribuer à grande échelle. Nous proposons ici une technique de compression (avec perte) très efficace de CLUTs 3D, fondée sur un schéma de reconstruction par diffusion anisotrope multi-échelle. Notre méthode affiche un taux de compression moyen supérieur à 99%, pour une dégradation résultante visuellement indiscernabl

Classification of Hyperspectral Images as Tensors Using Nonnegative CP Decomposition

International audienceA Hyperspectral Image (HSI) is an image that is acquired by means of spatial and spectral acquisitions, over an almost continuous spectrum. Pixelwise classification is an important application in HSI due to the natural spectral diversity that the latter brings. There are many works where spatial information (e.g., contextual relations in a spatial neighborhood) is exploited performing a so-called spectral-spatial classification. In this paper, the problem of spectral-spatial classification is addressed in a different manner. First a transformation based on morphological operators is used with an example on additive morphological decomposition (AMD), resulting in a 4-way block of data. The resulting model is identified using tensor decomposition. We take advantage of the compact form of the tensor decomposition to represent the data in order to finally perform a pixelwise classification. Experimental results show that the proposed method provides better performance in comparison to other state-of-the-art methods

Optimized Projection Matrix for Compressive Sensing

Compressive sensing (CS) is mainly concerned with low-coherence pairs, since the number of samples needed to recover the signal is proportional to the mutual coherence between projection matrix and sparsifying matrix. Until now, papers on CS always assume the projection matrix to be a random matrix. In this paper, aiming at minimizing the mutual coherence, a method is proposed to optimize the projection matrix. This method is based on equiangular tight frame (ETF) design because an ETF has minimum coherence. It is impossible to solve the problem exactly because of the complexity. Therefore, an alternating minimization type method is used to find a feasible solution. The optimally designed projection matrix can further reduce the necessary number of samples for recovery or improve the recovery accuracy. The proposed method demonstrates better performance than conventional optimization methods, which brings benefits to both basis pursuit and orthogonal matching pursuit