A non-invasive imaging system, including: a non-invasive imaging scanner; a signal processing unit in communication with the imaging scanner to receive an imaging signal from a subject under observation; and a data storage unit in communication with the signal processing unit, wherein the data storage unit stores template data corresponding to a tissue region of the subject, and wherein the signal processing unit is adapted to generate a surface map to encode a property of a subvolume of the tissue region using the template data.

A non-invasive imaging system, including: a non-invasive imaging scanner; a signal processing unit in communication with the imaging scanner to receive an imaging signal from a subject under observation; and a data storage unit in communication with the signal processing unit, wherein the data storage unit stores template data corresponding to a tissue region of the subject, and wherein the signal processing unit is adapted to generate a surface map to encode a property of a subvolume of the tissue region using the template data.

Assessment of pulmonary function in coronavirus patients includes use of a computer aided diagnostic system to assess pulmonary function and risk of mortality in patents with coronavirus disease 2019. The CAD system processes thoracic X-ray data from a patient, extracts imaging markers, and grades disease severity based at least in part on the extracted imaging markers, thereby distinguishing between higher risk and lower risk patients.

A computer implemented method, device and computer program device are provided that obtain a panoramic video for a scene, with a coordinate system. The method, device and computer program product identify a point of interest (POI) from the scene within the panoramic video, and track a position of the POI within the panoramic video. The method, device and computer program product record POI position data in connection with changes in the position of the POI during the panoramic video. The method, device and computer program product support play back of the panoramic video and adjustment of the field of view based on the POI position data.

A method and apparatus for detecting vascular landmarks in a 3D image volume, such as a CT volume, is disclosed. One or more guide slices are detected in a 3D image volume. A set of landmark candidates for multiple target vascular landmarks are then detected based on the guide slices. A node potential value for each landmark candidate is generated based on an error value determined using spatial histogram-based error regression, and edge potential values for pairs of landmark candidates are generated based on a bifurcation analysis of the image volume using vessel tracing. The optimal landmark candidate for each target landmark is then determined using a Markov random field model based on the node potential values and the edge potential values.

This characterizing method comprises: estimating (28) the scalar coefficients .sub.m of an even function () defined in [0; 2] that minimizes the following criterion C:

[00001]$C=\underset{j=1}{\overset{n_i}{.Math.}}.Math.{\left({}_j-*\left({}_j\right)\right)}^2$

where: .sub.j are terms estimated from an acquired digital image, () is a -periodic function defined in the interval [0; 2], () is the function defined by the following relationship:

[00002]$\left(\right)={}_{+}.Math.{.Math.\hat{v}\left(.Math..Math.\right).Math.}^2.Math.{}^{-2.Math.H-1}.Math.d.Math..Math.$ where: {circumflex over (V)} is the discrete Fourier transform of a convolution kernel v, H is an estimated Hurst exponent of the acquired image,
f) then, calculating (30), depending on the estimate of the scalar coefficients .sub.m, an anisotropy index that characterizes the anisotropy of the image, this index varying monotonically as a function of the statistical dispersion of the values of the function () for varying between 0 and .

This characterizing method comprises: estimating (28) the scalar coefficients .sub.m of an even function () defined in [0; 2] that minimizes the following criterion C:

[00001]$C=\underset{j=1}{\overset{n_i}{.Math.}}.Math.{\left({}_j-*\left({}_j\right)\right)}^2$

where: .sub.j are terms estimated from an acquired digital image, () is a -periodic function defined in the interval [0; 2], () is the function defined by the following relationship:

[00002]$\left(\right)={}_{+}.Math.{.Math.\hat{v}\left(.Math..Math.\right).Math.}^2.Math.{}^{-2.Math.H-1}.Math.d.Math..Math.$ where: {circumflex over (V)} is the discrete Fourier transform of a convolution kernel v, H is an estimated Hurst exponent of the acquired image,
f) then, calculating (30), depending on the estimate of the scalar coefficients .sub.m, an anisotropy index that characterizes the anisotropy of the image, this index varying monotonically as a function of the statistical dispersion of the values of the function () for varying between 0 and .