An initial image data set is obtained, and the initial image data set is filtered to generate a filtered data set. The filtering comprises computing, for each voxel of the initial image data set, a value of intensity for a standardized spherical volume of interest centred on that voxel. A user selection of a region of interest in the initial image data set is registered, and from the filtered image data set a value of intensity for the selected region of interest is computed. This filtered data forms part of a workflow process for analyzing an image as follows: A PET image volume is loaded within a user's workflow application, specifically a PERCIST Evaluation workflow, an organ such as the liver is identified auto matically(e.g. by atlas-based registration) and ROI is positioned on it. The mean SUL(Lean body mass-corrected Standard Uptake value(SUV)) is then computed for this ROI and a minimal metabolically measureable tumor activity(MMMTA) is computed based on this SUL value. The filtered data as defined above is then used with the original data to develop a tree structure showing the relationship between voxel position and local maxima for all voxels greater than the MMMTA. A clinician can then select an area of interest e.g. lesion in the image and the SUL peak for the volume of interest including the lesion can be computed using the filtered data.
Methods of analyzing a selected region of interest in medical image data