Integrating watersheds and critical point analysis for object detection in discrete 2D images.

This paper presents an improved method for the detection of "significant" low-level objects in medical images. The method overcomes topological problems where multiple redundant saddle points are detected in digital images. Information derived from watershed regions is used to select and refine saddle points in the discrete domain and to construct the watersheds and watercourses (ridges and valleys). We also demonstrate an improved method of pruning the tessellation by which to define low level objects in zero order images. The algorithm was applied on a set of medical images with promising results. Evaluation was based on theoretical analysis and human observer experiments.