Cue Integration and Front Evolution in Image Segmentation

Overview & Synopsis

Automatic detection and selection of regions of interest inside an image is a key step in image understanding. Many studies have been dedicated to this issue during the past decades. Efficient and robust algorithms have been developed for many applications. However, most of them make use of heuristics inherent to a particular class of images. The limiting factor to obtain a general algorithm is the large variety of cues available to characterize a region of interest. Examples include gray-level, color, texture and shape. In this thesis, we propose a general formulation able to deal with each one of these characteristics. Image intensity, color, texture, motion and prior shape knowledge are considered. For this purpose, a probabilistic inference is obtained from a Bayesian formulation of the segmentation problem. Then, reformulated as an energy minimization problem, the most probable image partition is obtained using front evolution techniques. Level-set functions are introduced to represent the evolving fronts while region statistics are optimized in parallel. This framework can naturally handle scalar and vector-valued smooth images but more complex cues are also integrated. Texture and motion features, as well as prior shape knowledge are successively introduced. Complex medical images are considered in the last part of the thesis, with a focus on diffusion magnetic resonance images and their associated 3D probability density fields.