Method of registering images, algorithm for carrying out the method of registering images, a program for registering images using the said algorithm and a method of treating biomedical images to reduce imaging artefacts caused by object movement

A method according to one or more of the preceding claims in which the set of characteristic parameters is subdivided in a certain number of subsets of characteristic parameters and for each subset a secondary characteristic parameter is determined as a function of the characteristic parameter of the said subset;

• a threshold being defined for each of the secondary characteristic parameter and for each secondary characteristic parameter the quality of validly trackable and non validly trackable landmark being determined by a function of the said secondary characteristic parameter and the corresponding threshold.

A method according to one or more of the preceding claims 7 to 10, characterised in that a global evaluation parameter is defined as a function of the secondary characteristic parameters and the quality of validly trackable and non validly trackable landmark is determined by a function of the said global evaluation parameter and a global threshold consisting in a function of the thresholds for the secondary characteristic parameters.

A method according to one or more of the preceding claims characterised in that the functions for determining the global evaluation parameter and/or the secondary characteristic parameters and/or the global thresholds consist in linear combinations of the modulus of one or more characteristic parameters, summation of the square values or non linear functions by which the characteristic parameters or the secondary parameters or the thresholds are processed.

A method according to one or more of the preceding claims, characterised in that the pixels or voxels of an image are processed previously or in parallel by means of the so called Lucas & Kanade automatic feature tracking method or algorithm.

A method according to one or more of the preceding claims characterised in that the characteristic parameters consist in the intensity values of the pixels or voxels of the selected windows and in the intensity values of the target pixel or voxel.

A method according to one or more of the preceding claims, characterised in that the characteristic parameters consist alternatively or in addition in the singular values of the numerical matrix comprising the image data of the pixels or voxels of the selected window.

A method according to one or more of the preceding claims, characterised in that the characteristic parameters consist, alternatively or in addition, in the eigenvalues of the gradient matrix of the said numerical matrix representing the pixels or voxels of the said window.

A method according to one or more of the preceding claims, characterised in that the characteristic parameters consist, alternatively or in addition, in the eigenvalues of the Hessian matrix of the said numerical matrix representing the pixels or voxels of the said window.

A method according to one or more of the preceding claims, characterised in that the characteristic parameters consist, alternatively or in addition, in one or more or a combination of the coefficients of the wavelet transform of the said numerical matrix representing the pixels or voxels of the said window obtained by processing of the said matrix with one or more different wavelet basis functions.

A method according to one or more of the preceding claims, characterised in that the characteristic parameters consist, alternatively or in addition, in the so called co-occurrence transform of the matrix.

A method according to one or more of the preceding claims, characterised in that the characteristic parameters consist, alternatively or in addition, in one or more of the coefficients of the autocorrelation transform of the said numerical matrix representing the pixels or voxels of the said window.

A method according to one or more of the preceding claims, characterised in that the characteristic parameters consist in a a combination of eigenvalues or singular values of the matrix of the numerical values representing the pixels or voxels of the windows and/or of the eigenvalues of the gradient matrix or of the Hessian matrix of the said numerical matrix representing the pixels or voxels of the said window and/or of one or more of the coefficients of the wavelet transform and/or one or more of the coefficients of the autocorrelation transform and/or of the singular values of the co occurrence matrix of said numerical matrix representing the pixels or voxels of the said window.

A method according to one or more of the preceding claims, characterised in that

• at leas two or more images of the same object are acquired or are provided which at least two or more images are acquired at different time instants;
• for each voxel being individuated as coinciding with a validly trackable landmark in a first image volume I the said feature is tracked relatively to its position in the voxel array of a further image J of the same object taken at a second later time, the said tracking being carried out by means of a so called pyramidal implementation of the Lucas and Kanade feature tracker.