Agfa Healthcare Nv

Agfa-gevaert

Agfa Healthcare

Agfa Gevaert Nv

Agfa Nv

Gert Behiels

Piet Dewaele

Pieter Paul Vuylsteke

Tom Bertens

Rudi Deklerck

Edgard Nyssen

Samar Mohamed

Marek Suliga

A method of aligning at least two breast images includes aligning a relevant image part in each of the images, the relevant image parts being obtained on the basis of the result of a shape analysis procedure performed on the breast images.

Image alignment of breast images

A method for defining a region of interest in a radiation image of a breast based on the result of shape analysis applied to a skin line estimate determined in the radiation image.

Method for defining a region of interest in a radiation image of a breast

Method of generating radiation images of an elongate body by applying different computer-implemented stitching methods to the same set of partial radiation images. A warning is generated in case of substantial deviation between the applied stitching methods.

Method to detect and indicate inaccuracies in long length imaging

In a method to enhance the contrast of spatially-localized phenomena in an image such as microcalcifications in a mammogram, a multi-scale decomposition is applied to a digital signal representation of the image thereby generating a number of detail images at different scales, the detail signals pertaining to the spatially-localized phenomena having a common polarity (either negative or positive) being modified at at least one scale.

Method and system for enhancing contrast of spatially-localized phenomena in mammography image

Method of generating a radiation image of an elongate body by taking plural partial X-ray images on a digital radiography detector using a multiple shot exposure technique. Partial image dimensions are determined so that the partial image representing that part of the elongate body that is most susceptible of movement during the multiple shot exposure is recorded covering an as large as possible area of the detector.

Method of generating a radiation image of an elongate body

A basic component of Computer-Aided Detection systems for digital mammography comprises generating candidate mass locations suitable for further analysis. A component is described that relies on filtering either the background image or the complementary foreground mammographic detail by a purely signal processing method on the one hand or a processing method based on a physical model on the other hand. The different steps of the signal processing approach consist of band-pass filtering the image by one or more band pass filters, multidimensional clustering, iso-contouring of the distance to centroid of the one or more filtered values, and finally candidate generation and segmentation by contour processing. The physics-based approach also filters the image to retrieve a fat-corrected image to model the background of the breast, and the resulting image is subjected to a blob detection filter to model the intensity bumps on the foreground component of the breast that are associated with mass candidates.

Method for mass candidate detection and segmentation in digital mammograms

A Markov Random Field (MRF)-based technique is described for performing clustering of images characterized by poor or limited data. The proposed method is a statistical classification model that labels the image pixels based on the description of their statistical and contextual information. Apart from evaluating the pixel statistics that originate from the definition of the K-means clustering scheme, the model expands the analysis by the description of the spatial dependence between pixels and their labels (context), hence leading to the reduction of the inhomogeneity of the segmentation output with respect to the result of pure K-means clustering.

Method for segmenting digital medical image

A short-time Goertzel transform is applied to the image signal to transform it into a representation of the spatial frequency component corresponding to the periodic pattern. This representation is processed. Next the inverse of the transformation is computed and the result of the inverse transformation is removed from the image signal.

Method of suppressing a periodical pattern in an image

A method of determining disturbing signal frequencies in a signal representation of radiation image originating from the presence of an anti-scatter grid during image acquisition, the signal representation being generated by an image read out and digitizing process, whereby the digitizing of an image of a simulated anti-scatter grid is simulated and the signal resulting from this simulation is evaluated to determine the disturbing frequencies.

Method of identifying disturbing frequencies originating from the presence of an anti-scatter grid during acquisition of a radiation image

An artifact profile signal is generated representing an estimation of the contribution of artifacts to a reference signal G comprising these artifacts, in regions defined around the position of artifact generating element(s). Signal portions in these regions are extracted from the image signal. The artifact profile signal is deformed so as to generate a new artifact profile signal which is used for correction of the image signal that has been subjected to the above extraction.

Method of correcting artifacts in an image signal

Method of detecting an image of a grid in a digital image by extracting a number of features from the digital image and classifying extracted features in a classification algorithm, preferably implemented with support vector machines, which is trained with the same training features extracted from a number of reference images.

Grid detection method

Several methods for retrospective correction of intensity inhomogeneities in digital diagnostic radiation images are presented. The methods are based on the correction of a digital image representation by means of a bias field. The bias field is deduced from the digital image representation of the diagnostic radiation image.

Retrospective correction of inhomogeneities in radiographs

Several methods for retrospective correction of intensity inhomogeneites in digital diagnostic radiation images are presented. The methods are based on the correction of a digital image representation by means of a bias field. The bias field is deduced from the digital image representation of the diagnostic radiation image.

A suppression signal representing the periodic variation of a digital signal is separated into separation signals containing values of equal phase. The separations signals are subjected to high frequency attenuating filtering before being recomposed into a corrected suppression signal which is used for correcting the digital signal.

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Edegem, Belgium

Gert Behiels