Morphons: segmentation using elastic canvas and paint on priors

This paper presents a new robust approach for segmentation. The segmentation is attained by morphing of an TV-dimensional model, the Morphon, onto the TV-dimensional data. The approach is general and can, in fact, be said to encompass much of the deformable model ideas that have evolved over the years. However, in contrast to commonly used models, a distinguishing feature of the Morphon approach is that it allows an intuitive interface for specifying prior information, hence the expression paint on priors. In this way it is simple to design Morphons for specific situations. The priors determine the behavior of the Morphon and can be seen as local data interpreters and response generators. There are three different kinds of priors: material parameter fields (elasticity, viscosity, anisotropy etc.), context fields (brightness, hue, scale, phase, anisotropy, certainly etc.) and global programs (filter banks, estimation procedures, adaptive mechanisms etc.). The morphing is performed using a dense displacement field. Both the material parameter and context fields are addressed via the present displacement field. An example of the performance of is given using 2D ultrasound images of a heart where the purpose is to segment the heart wall.