Patent Document ID: 8872822
Application ID: 12682791
Patent Status: 1

Claim One:
1. A method of visualizing temporal data, the method comprising, with one or more processors: receiving a set of temporal image data including a plurality of T-dimensional vectors, one for each voxel of an image describing evolution of the voxel over time, where T is a number of time instants an imaging technique has generated image data for the voxel; determining a number of distinct regions in the temporal image data based on temporal behavior of the temporal image data by use of a clustering algorithm and a model-selection algorithm, wherein the number of distinct regions in the temporal image data is determined by: first running the clustering algorithm for a range of input class values thereby obtaining a range of class assignments which group together voxels having similar temporal evolution of the respective T-dimensional vector; for each run of the clustering algorithm, performing a statistical analysis stage based on an analysis of an intensity distribution associated with each class assignment in the range of the class assignments to detect the number of distinct regions resulting in a threshold value calculated as a function of a maximum range of intensity over time of each class in the range of class assignments, wherein the maximum range is calculated for each class i in the range of class assignments according to the steps of: for each voxel belonging to class i, identifying a maximum intensity value observed at any time instant and identifying a minimum intensity value observed at any time instant; computing an intensity distribution of class i in the form of an intensity range by subtracting the maximum intensity value observed at each voxel from the minimum intensity value observed at each voxel; and from the intensity distribution of class i, identifying a maximum range for class i; and labelling each class as a background class or a non-background class in accordance with whether or not an intensity range of the class is below or above the threshold value, each labelled class being identified as a region, the classes assigned to the background class being counted as only one distinct region, and the actual number of distinct regions from each run of the clustering algorithm being combined to provide the number of distinct regions in the temporal data; identifying and assigning a color scheme to each distinct region; and controlling a display device to visualize each distinct region in accordance with the assigned color scheme.