Abstract: A computer-implemented method for determining a vascular function of a perfusion imaging sequence, includes the steps of: (i) receiving a perfusion imaging sequence including a voxel time series for a plurality of voxels; (ii) applying a trained classifier on the perfusion imaging sequence for receiving voxel-wise weights; (iii) receiving voxel-wise weights from the classifier; and (iv) determining the vascular function as the weighted sum of the voxel time series; wherein the classifier is trained by optimizing over the similarity between a predicted vascular function and a ground truth vascular function using a set of examples.

Abstract: A method for analyzing an image to assess a degree of asymmetry in an object having a presumed mirror symmetry includes: retrieving an image of the object; obtaining a mirrored image by flipping along an axis that has an a-priori unknown spatial relation to the presumed plane of symmetry; obtaining a mapping between the retrieved image and the mirrored image; determining a measure of asymmetry in the object by considering element pairs of a first element of the retrieved image and a second element of the mirrored image according to the mapping. Obtaining the mapping comprises performing a rigid registration followed by a non-rigid registration of the retrieved image to the mirrored image. The measure of asymmetry is determined by calculating the Jacobian of the non-rigid deformation in each element of the image. The invention also pertains to a computer program product and an image processing system.

Abstract: A method of analyzing image data comprises: obtaining a first image of a first part of an object; obtaining a second image of a second part of the object having overlap with the first part; obtaining a mapping between the first and second images; segmenting the second image to obtain a segmentation; detecting outliers in the first image by identifying extreme intensity values of elements within one or more classes of elements on the basis of the segmentation; replacing elements of the second image that correspond to at least some outliers of the first image, with replacement values, to obtain a corrected second image; and updating the segmentation by performing the segmenting on the corrected second image. The detecting outliers, the replacing, and the updating are performed iteratively until a predetermined convergence criterion is met, which represents a point where there is no significant change in the tissue and lesion segmentations.