Agfa Healthcare Nv

Agfa Nv

Agfa Healthcare

Tom Bertens

Pieter Paul Vuylsteke

Gert Behiels

Piet Vuylsteke

A motion correction method includes two steps. The first step includes a global motion correction using the bilinear warping technique and a rough delineation of the lung fields. One of the native images (low energy image, high energy image) is deformed to match the other image. In a second step, local motion corrections are applied to the globally motion corrected image by computing a proximity value in small overlapping tiles. Only tiles with a sufficient high proximity value are taken into account. The maximum shift applied in this second step is limited to a few pixels to avoid strong deformations of the native images.

Motion correction method in dual energy radiography

A method for processing a frame in a sequence of successively acquired frames in dynamic digital radiography includes the multi-scale representation of frames being subjected to temporal filtering by adding at least one correction image to a corresponding detail image(s) in the multi-scale representations of a frame of interest, the correction image being computed by combining clipped difference images obtained as the difference between the multi-scale representation of the frame of interest and the multi-scale representations of a selection of other frames in said sequence.

Method for noise reduction in an image sequence

A multi-scale detail representation of an image is computed as a weighted sum of translation difference images. A denoising operator is applied to the translation difference images so that translation differences are modified as a function of an estimated local signal-to-noise ratio and at least one denoised center difference image at a specific scale is computed by combining denoised translation difference images at scale s or a finer scale. A denoised image is computed by applying a reconstruction algorithm to the denoised center difference images.

Image de-noising method

A method to extract irradiation field areas in an X-ray image represented by a digital signal representation comprising the steps of segmenting the image in multiple regions of pixels which have similar local image characteristics, fitting line segments to the boundaries of these regions whereby said line segments correspond with candidate irradiation field boundaries and constitute a segmentation map, classifying regions in said segmentation map into at least two classes, one class being irradiation field and the other class being collimated region on the basis of at least one of local, regional and global image characteristics.

Irradiation field recognition

A method of generating a bone and a soft tissue image of an object includes a dual energy radiography technique wherein weight parameters for calculating these images as a weighted sum of a low energy image Pand a high energy image Pare deduced from a ratio image Log(P)/Log(P).

Calibration free dual energy radiography method

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

A method of generating a multiscale contrast enhanced image is described wherein the shape of edge transitions is preserved. Detail images are subjected to a conversion, the conversion function of at least one scale being adjusted for each detail pixel value according to the ratio between the combination of the enhanced center differences and the combination of the unenhanced center differences. Several adaptive enhancement measures are described.

Method of enhancing the contrast of an image

At least one of two enhancement methods for changing contrast and/or density are applied to an image. According to a first enhancement method the window width/level settings of the image are modified and according to a second enhancement method density and contrast of the image are modified by modifying a multi-scale representation of the image whereby modification is derived from at least two gradient functions determined at different scales, a gradient function at a specific scale specifying the dependency of contrast amplification at said scale as a function of density. The amount of modification obtained by applying either of said first and second enhancement methods is determined by the amount of movement of at least one indicium.

Method and system for changing image density and contrast

At least one approximation image is created of the image at one or multiple scales. Translation difference images are created by pixel-wise subtracting the values of an approximation image at scale s and the values of a translated version of the approximation image. A non-linear modification is applied to the values of the translation difference image (s) and at least one enhanced center difference image at a specific scale is computed by combining the modified translation difference images at that scale or a smaller scale with weights W. An enhanced image is computed by applying a reconstruction algorithm to the enhanced center difference images. The non-linear modification of the values of the translation difference images is steered by the values of an orientation map which comprises for each pixel a local direction of interest. In addition or alternatively, at least one enhanced center difference image is computed by anisotropic weighing of the enhanced translation differences with weights steered by the orientation map.

Method of generating a multiscale contrast enhanced image

A region of interest in the image is determined and for each pixel in each location (x,y) in that region of interest a value h(x,y) is computed representing the amount of dense tissue below said pixel as wherein p(x,y) represents the pixel value of a pixel at location (x,y) in said digital mammographic image, pis a reference fat pixel value, &#x3bc;,&#x3bc;are the linear attenuation coefficients of fat tissue and dense tissue and E is the mono-energetic energy value of the x-ray source at image recording, whereby E and pare derived from said digital mammographic image representation.

Method for deriving amount of dense tissue from mammographic image

A digital image signal is decomposed into a multi-scale representation comprising detail images and approximation images. Translation difference images are computed by subtracting an approximation image at scale s and a translated version of that approximation image. The values of the translation difference images are non-linearly modified. An amplification image is computed at least one scale as the ratio of a first image being computed by combining the modified translation difference images at the same or smaller scale and a second image created by combining unenhanced translation difference images at the same or smaller scale. Next, an enhanced multi-scale detail representation is computed by modifying at least one scale the detail image according to the amplification image at that scale. An enhanced image representation is computed by applying a reconstruction algorithm to the enhanced multi-scale detail representation. The non-linear modification of the values of the translation difference image is steered by the values of an orientation map which comprises for each pixel on a specific scale a local direction of interest.

At least one approximation image is created of the image at one or multiple scales. Translation difference images are created by pixel-wise subtracting the values of an approximation image at scale s and the values of a translated version of the approximation image. A non-linear modification is applied to the values of the translation difference image (s) and at least one enhanced center difference image at a specific scale is computed by combining the modified translation difference images at that scale or a smaller scale with weights w. An enhanced image is computed by applying a reconstruction algorithm to the enhanced center difference images. The non-linear modification of the values of the translation difference images is steered by (a) characteristic (s) computed out of the approximation image (s) at least one scale.

A method of generating a multiscale contrast enhanced image is described wherein the shape of edge transitions is preserved. Detail images are subjected to a conversion, the conversion function of at least one scale being adjusted for each detail pixel value according to the ratio between the combination of the enhanced center differences and the combination of the unenhanced center differences.

Method of generating multiscale contrast enhanced image

A method of generating a multiscale contrast enhanced image preserving the shape of the edge transitions is described. Enhanced detail pixel values are computed by combining enhanced center difference images at least one scale.

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Tom Bertens