Abstract: A prediction, diagnosis, or suggesting of a diagnosis of a psychiatric disorder is made based in embodiments of the disclosed technology by determining a shape of one or both pupils in an image of a patient. Such a diagnosis can be made in real-time by diagnosing the patient, or by viewing a digitized version of the patient's face as part of post-processing. By finding a pupil which is ovoid or irregular shape (non-circular) and/or comparing the shape to that of shapes of people with known psychiatric disorders, a diagnosis can be suggested.

Abstract: The present disclosure relates to an image processing method including the following steps: receiving a deformation or displacement image; defining a meshing of the deformation image; assigning a respective pair of unknown variables to each node of the meshing; detecting one (or more) node(s) located at the boundary between two different materials of the body studied; enriching the node(s) detected; calculating a basic matrix for each basic cell of the meshing; assembling the basic matrixes in order to obtain an overall structural matrix; calculating a resilience image of the body based on the overall structural matrix.

Abstract: A prediction, diagnosis, or suggesting of a diagnosis of a psychiatric disorder is made based in embodiments of the disclosed technology by determining a shape of one or both pupils in an image of a patient. Such a diagnosis can be made in real-time by diagnosing the patient, or by viewing a digitized version of the patient's face as part of post-processing. By finding a pupil which is ovoid or irregular shape (non-circular) and/or comparing the shape to that of shapes of people with known psychiatric disorders, a diagnosis can be suggested.

Abstract: An image processing method enables an elasticity image of a body including a cavity to be produced on the basis of the material(s) forming the body, wherein the method includes the steps of: receiving a deformation image illustrating a field of movement of the points of the body on the basis of a pressure difference in the body, estimating a shape function of the body from the deformation image, calculating an elasticity image of the body on the basis of the shape function, of the pressure difference and of the deformation image.

Abstract: The present disclosure relates to an image processing method including the following steps: receiving a deformation or displacement image; defining a meshing of the deformation image; assigning a respective pair of unknown variables to each node of the meshing; detecting one (or more) node(s) located at the boundary between two different materials of the body studied; enriching the node(s) detected; calculating a basic matrix for each basic cell of the meshing; assembling the basic matrixes in order to obtain an overall structural matrix; calculating a resilience image of the body based on the overall structural matrix.

Abstract: An image processing method enables an elasticity image of a body including a cavity to be produced on the basis of the material(s) forming the body, wherein the method includes the steps of: receiving a deformation image illustrating a field of movement of the points of the body on the basis of a pressure difference in the body, estimating a shape function of the body from the deformation image, calculating an elasticity image of the body on the basis of the shape function, of the pressure difference and of the deformation image.

Abstract: The invention relates to an image processing method for estimating a risk of atheroma plaque breakage, that includes the step of receiving (40) a so-called elastogram image representing the inner deformation resulting from the compression of an analyzed blood vessel tissue based on blood pressure, the step of pre-segmenting (50) the elastogram in order to obtain a pre-segmented image including a plurality of areas, and the step of calculating an elasticity image (60) representing the elasticity of at least one region of the elastogram, the region corresponding to an area selected among the plurality of areas of the pre-segmented image, and the elasticity image enabling the user to estimate the risk of atheroma plaque breakage, wherein said method is characterized in that: the pre-segmentation is carried out while considering the tissue as being a non-compressible elastic solid in order to determine the Young modulus gradient as equal to the sum of the Lagrange multiplier gradient and of a space depending vect

Abstract: The invention relates to image processing method for estimating a risk of atheroma plaque breakage, that includes the step of receiving (40) a so-called elastogram image representing the inner deformation resulting from the compression of an analysed blood vessel tissue based on blood pressure, the step of pre-segmenting (50) the elastogram in order to obtain a pre-segmented image including a plurality of areas, and the step of calculating an elasticity image (60) representing the elasticity of at least one region of the elastogram, the region corresponding to an area selected among the plurality of areas of the pre-segmented image, and the elasticity image enabling the user to estimate the risk of atheroma plaque breakage, wherein said method is characterised in that: the pre-segmentation is carried out while considering the tissue as being a non-compressible elastic solid in order to determine the Young modulus gradient as equal to the sum of the Lagrange multiplier gradient and of a space depending vector,

Abstract: The method for vascular elastography comprises: i) obtaining a sequence of radio-frequency (RF) images including pre-tissue-motion and post-tissue-motion images in digital form of a vessel delimited by a vascular wall; the pre-tissue-motion and post-tissue-motion images being representative of first and second time-delayed configuration, of the whole vessel; ii) partitioning both the pre-tissue-motion and post-tissue-motion images within the vascular wall into corresponding data windows; approximating a trajectory between the pre- and post-tissue-motion for corresponding data windows; and using the trajectory for each data window to compute the full strain tensor in each data window, which allow determining the Von Mises coefficient. The method can be adapted for non-invasive vascular elastography (NIVE), for non-invasive vascular micro-elastography (MicroNlVE) on small vessels, and for endovascular elastography (EVE).

Abstract: The method for vascular elastography comprises: i) obtaining a sequence of radio-frequency (RF) images including pre-tissue-motion and post-tissue-motion images in digital form of a vessel delimited by a vascular wall; the pre-tissue-motion and post-tissue-motion images being representative of first and second time-delayed configuration, of the whole vessel; ii) partitioning both the pre-tissue-motion and post-tissue-motion images within the vascular wall into corresponding data windows; approximating a trajectory between the pre- and post-tissue-motion for corresponding data windows; and using the trajectory for each data window to compute the full strain tensor in each data window, which allow determining the Von Mises coefficient. The method can be adapted for non-invasive vascular elastography (NIVE), for non-invasive vascular micro-elastography (MicroNIVE) on small vessels, and for endovascular elastography (EVE).