Median Technologies

Institut Fuer Rundfunktechnik Gmbh

Joanneum Research Forschungsgesellschaft Mbh Institute of Information Systems

Portugal Telecom Inovacao, Sa

Institut Fur Rundfunktechnik Gmbh

Benoit Huet

Pierre Baudot

Jean-Christophe Brisset

Gerhard Stoll

Bernard Merialdo

Elton Rexhepaj

Jörg Deigmöller

Vladimir Groza

Ezequiel Geremia

Elias Munoz

Benjamin Renoust

Sébastien Poullot

Corinne Ramos

Nozha Boujemaa

Remi Trichet

Roger Salgado De Alem

Fausto Jose Oliveira De Carvalho

Jose Bernardo Dos Santos Cardoso

Andreas Kriechbaum

Helmut Neuschmied

Joerg Deigmoeller

Fausto José Oliveira De Carvalho

José Bernardo Dos Santos Cardoso

A method for performing classification of the severity of at least one liver disease from non-invasive radiographic images is disclosed. The method includes: providing radiographic images of slices of the abdomen of a patient; pre-processing the radiographic images by: segmenting liver and spleen, thus achieving a spleen binary mask and a liver binary mask per slice, and normalizing the images with each other, thus achieving normalized radiographic images per slice; for each slice, from the liver binary mask and the normalized radiographic images, extracting a liver parameter; from at least one spleen binary mask, extracting a spleen parameter; and classifying, in function of both parameters and by help of a trained Machine Learning model, the severity of liver disease between one among a group of liver disease at early stage and a group of liver disease at advanced stage.

Automated pattern recognition and scoring method of fibrosis from non invasive radiology imaging

An apparatus and method for training and using a computing operation for digital image processing are provided. The apparatus and method may be used for 3-dimensional medical images. An exemplary method for digital image processing comprises: receiving an image displaying at least one detectable structure, determining the detectable structure; segmenting the image to obtain a segmentation mask that is associated with a geometric shape and comprises at least one quantifiable visual feature; generating a mesh based on the quantifiable visual feature; computing at least on quantifiable visual parameter based on the mesh; extracting quantifiable visual data from the image based on the quantifiable visual parameter; training the computing operation with the quantifiable visual data. The method for digital image processing further comprises: receiving another image; segmenting, generating a mesh, computing quantifiable visual parameters, and extracting quantifiable visual data; and classifying the extracted quantifiable visual data with the trained computing operation.

Apparatuses and methods for training and using computational operations for digital image processing

To improve a cropping system by obtaining coverage of a wide range of contents for smaller sized displays of handheld devices, a method starts from a metadata aggregation and corresponding video, e.g. in post-production, program exchange and archiving, wherein (a) video is passed to a video analysis to deliver video, e.g. by use of motion detection, morphology filters, edge detection, etc., (b) separated video and metadata are combined to extract important features in a context wherein important information from the metadata is categorized and used to initialize a dynamically fitted chain of feature extraction steps adapted to the delivered video content, (c) extracted important features are combined to define regions of interest (ROI) which are searched in consecutive video frames by object tracking, the object tracking identifies a new position and deformation of each initialized ROI in consecutive video frames and returns this information to the feature extraction thereby obtaining a permanent communication between the feature extraction and the object tracking, (d) one or plural ROIs are extracted and input video frame by video frame into a cropping step (e); based on weighting information a well composed image part is cropped by classifying the supplied ROIs by importance, and (f) the cropped image area(s) are scaled to the desired small screen size.

Method of adapting video images to small screen sizes

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Roquefort les Pins, France

Benoit Huet