Structure and location preserving topological representation with applications on CT segmentation.

Abstract

Contour tree represents the nesting relations of the individual components in the image; however, it neglects the geometric structure of the terrain. In this paper, we propose a new topological representation that provides the nesting and spatial relations of regions for CT image interpretation. The tree is constructed based on the signed distance transformation of binary CT image, and combines intensity based contour tree and a new gradient based topology tree. We also investigate the application of the topological representation as a constraint in target object segmentation. Ten non-small cell lung tumor CT studies were used for segmentation accuracy evaluation. The image representation results showed that the proposed tree structure retained the nesting and spatial relations of the tissues or objects in the CT image. The segmentation results demonstrated its usability in the separation of adjacent objects with similar intensity distributions.