A Robust Footprint Detection Using Color Images and Neural Networks

The automatic detection of different foot’s diseases requires the analysis of a footprint, obtained from a digital image of the sole. This paper shows that optical monochromatic images are not suitable for footprint segmentation purposes, while color images provide enough information for carrying out an efficient segmentation. It is shown that a multiplayer perceptron trained with bayesian regularization backpropagation allows to adequately classify the pixels on the color image of the footprint and in this way, to segment the footprint without fingers. The footprint is improved by using a classical smoothing filter, and segmented by performing erosion and dilation operations. This result is very important for the development of a low cost system designed to diagnose pathologies related to the footprint form.

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