Topologically Adaptable GVF Model And Its Application In Medical Image Segmentation

The increasingly important role of medical imaging in the diagnosis and treatment of disease has opened an array of challenging problems centered on the computation of accurate geometric models of anatomic structures from medical images. A promising approach to tackle such problems is the use of deformable models. These powerful models have proven to be effective in segmenting, visualizing, matching and tracking anatomic structures by exploiting constraints derived from the image data together with a priori knowledge about the location, size and shape of these structures. Furthermore, deformable models support highly intuitive interaction mechanisms that allow medical scientists and practitioners to bring their expertise to bear on the image interpretation task.This thesis proposes, develops and applies a new class of discrete deformable models for the segmentation and analysis of medical images. This new deformable models is embedded and defined in terms of an Affine Cell Decomposition (ACD) framework. It can deal with objects with complex geometries and topologies. Multiple instances of the models can be dynamically created or destroyed and can seamlessly split or merge to adapt to object topology.Topologically adaptable GVF model combined with a novel and original reparameterization algorithm. Original models maintain the traditional parametric physics-based formulation of deformable models, allowing them to incorporate a priori anatomic knowledge in the form of energy and force-based constraints and ? provide intuitive interactive capabilities. Novel algorithm significantly extends the abilities of traditional deformable models, enabling topological flexibility among other features. So topologically adaptable GVF model can be used to extract, reconstruct and analyze even the most complex biological structures from medical images.We propose topologically adaptable GVF model as a solution to many of limitations of original models. Experimentations have proved the feasibility.