A comparison of material classification techniques for ultrasound inverse imaging

The conjugate gradient method with edge preserving regularization (CGEP) is applied to the ultrasound inverse scattering problem for the early detection of breast tumors. To accelerate image reconstruction, several different pattern classification schemes are introduced into the CGEP algorithm. These classification techniques are compared for a full-sized, two-dimensional breast model. One of these techniques uses two parameters, the sound speed and attenuation, simultaneously to perform classification based on a Bayesian classifier and is called bivariate material classification (BMC). The other two techniques, presented in earlier work, are univariate material classification (UMC) and neural network (NN) classification. BMC is an extension of UMC, the latter using attenuation alone to perform classification, and NN classification uses a neural network. Both noiseless and noisy cases are considered. For the noiseless case, numerical simulations show that the CGEP-BMC method requires 40% fewer iterations than the CGEP method, and the CGEP-NN method requires 55% fewer. The CGEP-BMC and CGEP-NN methods yield more accurate reconstructions than the CGEP method. A quantitative comparison of the CGEP-BMC, CGEP-NN, and GN-UMC methods shows that the CGEP-BMC and CGEP-NN methods are more robust to noise than the GN-UMC method, while all three are similar in computational complexity.