ABSTRACT
This chapter focuses on deformable-model based segmentation, due to the many advances it has made in the field of medical imaging analysis, the ease of integrating its concepts in any framework, and the high efficiency it provides in convergence to solutions. It provides an overview of the advances in image segmentation using deformable models, which have been extensively used in literature for image segmentation. The chapter presents a variety of energy functions and numerical schemes to solve their associated partial differential equations. It reviews the parametric deformable models in literature and the geometric deformable models that were designed to overcome the limitations of the classical parametric representation. The chapter explores basics of the level sets method and some of its important concepts and numerical schemes for solving the level sets equation. It discusses some of the popular guiding forces in literature, such as the Chan-Vese model that is widely applied in image segmentation techniques, along with some improvements of it.
