Abstract
A segmentation algorithm that assists material analysis in multi-energy computed tomography is presented. Segmentation is typically used in conjunction with quantitative material analysis algorithms (known as material decomposition) to increase the total number of materials which can be discriminated and quantified. The algorithm illustrated here identifies voxels (in the image domain) with one of three material classes: air, soft tissues and dense tissue or contrast pharmaceuticals. Two soft tissue materials are chosen (the most and the least attenuating soft tissues) to define the boundaries between the different material classes. The intensity (calculated from the multi-energy representation using the Euclidean norm) of each voxel is compared to the two boundary materials to determine which material class it belongs to. Unlike other intensity based segmentation methods this algorithm checks, using multi-variate confidence intervals (ellipsoids), whether each voxel is statistically distinguishable from the two boundary materials. If the voxels are not distinguishable then they are defaulted to the soft tissue class. An advantage of this segmentation method is that noise which passes through to the binary representations of each material class typically resemble salt and pepper noise, which is easily removed with a median filter. Simulations demonstrate that the algorithm can correctly allocate a variety of medically relevant soft tissue and non soft tissue materials to their correct material classes and that segmentation using multi-energy information can handle noisier data than when using single-energy information. The proposed algorithm is also successfully applied to a multi-energy CT scan taken using the MARS (Medipix All Resolution System) scanner.