Active contours using density estimation with applications to magnetic resonance image segmentation and target tracking

Active contour models, more commonly known as snakes, have received considerable attention for more than a decade since their introduction by Kass et al. Snakes are energy minimizing contours. The energy of the snake depends upon its shape and location within the image. These models segment and/or track target areas in the images, that have certain characteristics. Active contour Models have been used in a wide range of applications. In vision-guided robotics, snakes have been used for object tracking, object grasping and object disambiguation. They have also been used for tumor segmentation in medical imaging applications. In Human-Computer Interaction (HCI), active contours have been used for non-intrusive eye tracking.; Snakes attracted much of this attention because of several characteristics. They can segment objects with a reasonable computational cost, compared to other techniques. They also give a piecewise linear description of the contour of the object with no additional processing. On the other hand, classical snakes suffered from two major problems. First, active contours tended to fail in images with weak gradient fields. Also, classical snakes were limited to segmenting simple colored objects. Several formulations have been proposed trying to solve these problems. Most of the proposed formulations try to do this by adding more energy terms to the snake in order to control its evolution. Even though these approaches enhance the segmentation accuracy, they do so by increasing the computational complexity of the snake.; In this dissertation, a new active contour formulation is presented. The new formulation alleviates the need for strong gradient field, while providing the low computational cost. The proposed formulation is based on estimating the probability density functions (PDF) of the target and the background. The PDFs are estimated using either Expectation Maximization (EM) or kernel estimators and then Bayesian decision theory is employed to drive the snake. Experimental results show that the proposed approach can be effectively used for both target tracking and target segmentation.; A fuzzy-sets approach to active contours is also presented, along with experimental results, to show how to integrate other classification mechanisms into active contours to form a framework for object segmentation and tracking. Also, as a by-product of this research, and to bridge the gap between parametric and nonparametric density estimation, a Stochastic Learning Automata-based (SLA) approach for a nonparametric Expectation Maximization is presented.