  The aim of this study is to use a Multilayer Perceptron (MLP) Artificial
Neural Network (ANN) for phaseless imaging the human heel (modeled as a bilayer
dielectric media: bone and surrounding tissue) and the calcaneus cross-section
size and location using a two dimensional (2D) microwave tomographic array.
Computer simulations were performed over 2D dielectric maps inspired by
Computed Tomography (CT) images of human heels for training and testing the
MLP. A morphometric analysis was performed to account for the scatterer shape
influence on the results. A robustness analysis was also conducted in order to
study the MLP performance in noisy conditions. The standard deviations of the
relative percentage errors on estimating the dielectric properties of the
calcaneus bone were relatively high. Regarding the calcaneus surrounding
tissue, the dielectric parameters estimations are better, with relative
percentage error standard deviations up to $\approx$ 15 %. The location and
size of the calcaneus are always properly estimated with absolute error
standard deviations up to $\approx $ 3 mm.
