Level-set segmentation of footprint images aimed at insole design

Abstract

Chronic foot pain is a disease that progresses with age and has a high prevalence. Therapeutic procedures include the utilization of orthoses or insoles that are placed inside the footwear. Design of personalized insoles is a process that includes several stages. An important stage is the acquisition and analysis of footprint images. Their segmentation enables quantification of the footprint shape by estimating several indices that allow classification and diagnosis of foot morphology abnormalities. A segmentation method for footprint images using Level-Set algorithms is reported. Two area based Level-Set segmentation algorithms were applied. The first is the Chan-Vese algorithm using a global minimizer. The second is the Lankton algorithm that implements the Chan-Vese energy function using a localized minimizer and the Sparse Field Method for reducing the computational cost. Algorithms tested are accurate for segmenting the footprint images, providing an average Dice coefficient higher than 0.93. The Lankton algorithm is robust with respect to spatial variation in intensities within the footprint shape. It is also fast as the average time for segmenting one image is only 6.4 seconds