  Segmentation is the process of partitioning a digital image into multiple
segments (sets of pixels). Such common segmentation tasks including segmenting
written text or segmenting tumors from healthy brain tissue in an MRI image,
etc. Chan-Vese model for active contours is a powerful and flexible method
which is able to segment many types of images, including some that would be
quite difficult to segment in means of "classical" segmentation - i.e., using
thresholding or gradient based methods. This model is based on the Mumford-Shah
functional for segmentation, and is used widely in the medical imaging field,
especially for the segmentation of the brain, heart and trachea. The model is
based on an energy minimization problem, which can be reformulated in the level
set formulation, leading to an easier way to solve the problem. In this
project, the model will be presented (there is an extension to color
(vector-valued) images, but it will not be considered here), and Matlab code
that implements it will be introduced.
