ELECTRONIC STOOL SUBTRACTION USING QUADRATIC REGRESSION AND INTELLIGENT MORPHOLOGY

摘要

An improved method for processing image voxel data representative of 3-d?mensional images of a colon to remove the effects of tagged stool. The method uses parabolic curve intensity-gradient models at a transition between two material types as a function of the fraction of the two materials for each of a plurality of two-material type classes, including a gas-tissue transition model, a gas-stool transition model and a stool-tissue transition model. The voxels are classified into one of a plurality of substance classes including tagged stool, gas, tissue and unknown classes. The unknown class voxels are processed to classify the unknown class voxels into one of the two-material type classes. The two-material type class voxels are processed to determine the fractions of materials in each voxel. The intensity of the two-material type class voxels is then adjusted as a function of the fraction of the materials in the voxels.