Nonlinear spatial warping for between-subjects pedobarographic image registration

Foot size and shape vary between individuals and the foot adopts arbitrary stance phase postures, so traditional pedobarographic analyses regionalize foot pressure images to afford homologous data comparison. An alternative approach that does not require explicit anatomical labelling and that is used widely in other functional imaging domains is to register images such that homologous structures optimally overlap and then to compare images directly at the pixel level. Image registration represents the preprocessing cornerstone of such pixel-level techniques, so its performance warrants independent attention. The purpose of this study was to evaluate the performance of four between-subjects warping registration algorithms including: Principal Axes (PA), four-parameter Optimal Scaling (OS4), eight-parameter Optimal Projective (OP8), and locally affine Nonlinear (NL). Fifteen subjects performed 10 trials of self-paced walking, and their peak pressure images were registered within-subjects using an optimal rigid body transformation. The resulting mean images were then registered between-subjects using all four methods in all 210 (15 × 14) subject combinations. All registration methods improved alignment, and each method performed qualitatively well for certain image pairs. However, only the NL consistently performed satisfactorily because of disproportionate anatomical variation in toe lengths and rearfoot/forefoot width, for example. Using three independent image (dis)similarity metrics, MANOVA confirmed that the NL method yielded superior registration performance (p < 0.001). These data demonstrate that nonlinear spatial warping is necessary for robust between-subject pedobarographic image registration and, by extension, robust homologous data comparison at the pixel level.