Author :
Patel, Daniel ; Haidacher, Martin ; Balabanian, Jean-Paul ; Gröller, Eduard M.
Author_Institution :
Christian Michelsen Res., Bergen
Abstract :
We define a transfer function based on the first and second statistical moments. We consider the evolution of the mean and variance with respect to a growing neighborhood around a voxel. This evolution defines a curve in 3D for which we identify important trends and project it back to 2D. The resulting 2D projection can be brushed for easy and robust classification of materials and material borders. The transfer function is applied to both CT and MR data.
Keywords :
biomedical MRI; computerised tomography; curve fitting; image classification; medical image processing; statistical analysis; transfer functions; 2D projection; CT data; MR data; moment curves; robust classification; statistical moments; transfer function; Computed tomography; Computer graphics; Histograms; Image processing; Image segmentation; Informatics; Material properties; Noise robustness; Transfer functions; Volume measurement; I.4.10 [Image Processing]: Image Representation—Statistical; I.4.10 [Image Processing]: Image Representation—Volumetric; I.4.6 [Image Processing]: Segmentation—Pixel classification; I.4.7 [Image Processing]: Feature Measurement—Feature representation;
Conference_Titel :
Visualization Symposium, 2009. PacificVis '09. IEEE Pacific
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-4404-5
DOI :
10.1109/PACIFICVIS.2009.4906857
