Segmentation of medical images by feature tracing in a self-dual morphological scale-space
Volker H. Metzler, Christian J. Thies, Thomas Lehmann · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
The multiscale approach derives a segmentation from the evolution of appropriate signal-descriptive features in scale-space. Features that are stable for a wide range of scales are assumed to belong to visually sensible regions. To compensate the well-known drawbacks of linear scale- spaces, the shape-preserving properties of morphological scale-space filtering are utilized. The limiting duality of morphological filters is overcome by a selfdual morphological approach considering both light and dark structures in either the opening or the closing branch of the scale-space. Reconstructive opening/closing-filters enable the scale=analysis of 2D signals, since they are causal with respect to regional maxima/minima. This allows to identify important regions in scale=space via their extrema. Each extremum is assigned a region by a gradient watershed of the corresponding scale. Due to morphological filtering, the scale behavior of the regions is representable by a tree structure describing the spatial inter- and intra-scale relations among regions. The significance of a watershed region is automatically derived from its scale behavior by considering various attributes describing scale-dependent, morphological, and statistical properties of the region. The most significant regions from the segmentation of the image. The algorithm was verified for various medical image domains, such as cytological micrographs, bone x-rays, and cranial NMR slices.