An integrated approach to boundary finding in medical images

A key issue in biomedical image analysis is to accurately segment and quantify structures. Gradient based boundary finding and region based segmentation, the two conventional methods of image segmentation often suffer from a variety of limitations. Here the authors propose a method which endeavors to integrate the two approaches in an effort to form a unified approach that is robust to noise and poor initialization. The authors´ approach uses Green´s theorem to derive the boundary of a homogeneous region-classified area in the image and integrates this with a grey-level-gradient-based boundary finder. This combines the perceptual notions of edge/shape information with gray level homogeneity. A number of experiments were performed both on synthetic and real medical images of the brain and heart to evaluate the new approach and it is shown that the integrated method typically performs better than conventional gradient based boundary finding