Segmentation of elongated structures in medical images

Abstract

The research described in this thesis concerns the automatic detection, recognition and segmentation of elongated structures in medical images. For this purpose techniques have been developed to detect subdimensional pointsets (e.g. ridges, edges) in images of arbitrary dimension. These pointsets are grouped into primitives, such as line elements and surface patches. The primitives form the basis for recognition and segmentation task, which is accomplished with classifiers from statistical pattern recognition. Two applications are given: segmentation of the vasculature in color images of the human retina and detection, labeling and segmentation of ribs in CT-scans (computed tomography) of the thorax.