Non-parametric coupled shape priors for segmentation of deformable objects in time-series images using particle filters

Official URL: http://risc01.sabanciuniv.edu/record=b1817033 (Table of Contents)

Segmentation is usually the first step in image processing and directly impacts the success or failure of the image analysis algorithm. It turns into a very challenging problem when the observed image suffers from insufficiencies, such as high level of noise, clutter, data loss and, occlusion. The effect of prior knowledge has been widely studied in the curve evolution-based models and it has been proved that utilization of some kind of prior information obtained by exploiting the known features of the object to be segmented, can aid the result of the segmentation process. In this thesis, shape, which is a favorable attribute of the object to be segmented, is used to form the prior information. The proposed method has been developed based on a sampling approach using sequential Monte Carlo (Particle Filters) and In order to enrich the segmentation model, a new term is introduced which we refer to as coupled shape priors. By involving a curve evolution step into the sampling process, the coupled shape prior term, takes part in the proposed energy functional defined for the curve evolution step and incorporates the temporal shape dependencies. The proposed method has been evaluated on three different datasets, a deforming synthetic dataset, hand gesture dataset and 2-photon microscopy images of dendritic spines and, according to both visual and quantitative results, it has been demonstrated that it has a successful performance in segmentation of deforming objects whose shapes come from multi-modal shape densities. Also it has been shown that the proposed method is able to handle low quality images, highly noisy images, images with data loss, and occluded images.