Affine image registration guided by particle filter

Image registration is a central task to different applications, such as medical image analysis, biomedical systems, stereo computer vision and optical flow estimation. There are many methods described in the literature for resolving this task, but they are mainly based on the minimisation of some cost function. These methods, depending on the complexity of the function to optimise, use different strategies for localising a minimum which explain the alignment between images or volumes, such as linearising the cost function or using multiscale spaces. In this work, a particle filter method, also known as sequential Monte Carlo strategy, is proposed to settle these difficulties by estimating the probability distribution function (PDF) of the parameters of affine transformations. Using the reconstructed PDF, it is possible to obtain an accurate estimation of the transformation parameters in order to register unimodal and multimodal data. The proposed method proved to be robust to noise, partial data and initialising parameters. A set of evaluation experiments also showed that the method is easy to implement, and competitive to estimate affine parameters in two-dimensional (2D) and 3D.