Multiple processing of radiographic images based on a pyramidal image decomposition

Claim 2

A method according to claim 1 wherein the multiresolution representation is a pyramidal representation wherein the number of pixels in each detail image decreases at each coarser resolution level.

Claim 3

A method according to claim 1 wherein said multiresolution representation is obtained as a sequence of detail images at multiple resolution levels and a residual image at a resolution level lower than the minimum of said multiple resolution levels.

Claim 4

A method according to claim 3 wherein the detail image at the finest resolution level is obtained as the pixel wise difference between the original image and an image obtained by low pass filtering the original image, and wherein the successive coarser resolution level detail images are obtained by taking the pixelwise difference between two low pass filtered versions of the original image, the second having a smaller bandwidth than the former.

Claim 5

A method according to claim 3 - wherein the detail images at successive coarser resolution levels are obtained as a result of K iterations of the following steps: a) computing an approximation image at a next coarser level by applying a low pass filter to the approximation image corresponding to the current iteration, ans subsampling the result in proportion to the reduction in spatial frequency bandwidth, using the original image as input to said low pass filter in the course of the first iteration; b) computing a detail image as the pixelwise difference between the approximation image corresponding to the current iteration and the approximation image at a next coarser resolution level computed according to the method sub 4a;both images being brought into register by proper interpolation of the latter image; - and wherein the residual image is equal to the approximation image produced by the last iteration, - and wherein said processed image is computed by iterating K times the following procedure starting from the coarsest detail image and the residual image: computing the approximation image at the current resolution level by pixelwise adding the modified detail image at the same resolution level to the approximation image at the coarser resolution level corresponding to the previous iteration, both images being brought into register by proper interpolation of the latter image, using the residual image instead of the coarser approximation image in the course of the first iteration.

Claim 6

A method according to claim 5 wherein said subsampling factor is 2, and said low-pass filter has an impulse response which approximates a two-dimensional gaussian distribution.

Claim 7

A method according to claim 1 wherein said multiresolution representation comprises detail transform images at multiple resolution levels and a residual coefficient, each detail image comprising a set of transform coefficients expressing the relative contribution to the original image of the corresponding basis function out of a set of predetermined basis functions, each of said functions representing local detail at a specific resolution level and being non-periodic, and having zero mean value, and wherein said transform is characterised by the existence of an inverse transform which returns the original image or a close approximation thereof when being applied to said transform coefficients and said residual coefficient.

Claim 8

A method according to claim 7 wherein said basis functions are orthogonal.

Claim 9

A method according to claim 7 wherein said basis functions are discrete wavelets.

Claim 10

A method according to claim 1 wherein said radiographic image is stored in a photostimulable phosphor screen and wherein said digital image representation is obtained by scanning said screen with stimulating irradiation, detecting light emitted upon stimulation and converting said detected light into a digital representation.

Claim 11

A method according to claim 1 wherein said function depends on the value of a corresponding pixel in said original image.

Claim 12

A method according to claim 1 wherein said function has a slope that gradually decreases with increasing argument values with the exception of a region of lowest absolute argument values where the slope is constant or increasing.

Claim 13

A method according to claim 1 wherein said function is not identically defined at each resolution level in said decomposition, such that modifications applied to finer resolution levels ampplify image detail more than modifications applied to intermediate resolution levels.

Claim 14

A method according to claim 1 wherein said function is not identically defined at each resolution level in said decomposition, such that modifications applied to coarser resolution levels amplify image detail less than modifications applied to intermediate resolution levels.

Claim 15

A method according to claim 1 wherein said values are modified as a function of the estimated amount of image content and in accordance with an estimated noise level.

Claim 16

A method according to claim 15 wherein said noise level is determined as the estimated noise level in each detail image.

Claim 17

A method according to claim 15 wherein a set of noise suppression functions are computed , each being associated with one of said detail images, said functions being monotonically non-decreasing in one variable and parametrically depending on said noise variance in a non-increasing monotonic way and said suppression functions being positive and asymptotically reaching a maximum value equal to one and wherein each detail image is attenuated by multiplying it with an associated noise suppression function evaluated at an abscissa equal to said local detail image variance.