Skeletonization and segmentation algorithms for object representation and analysis

Abstract

Skeletonization and segmentation are two important techniques for object representation and analysis. Skeletonization algorithm extracts the “centre-lines” of an object and uses them to efficiently represent the object. It has many applications in various areas, such as computer-aided design, computer-aided engineering, and virtual reality. Segmentation algorithm locates the target object or Region Of Interest (ROI) from images. It has been widely applied to medical image analysis and many other areas. This thesis presents two studies in skeletonization and two studies in segmentation that advanced the state-of-the-art research. The first skeletonization study suggests an improvement of an existing algorithm for connectivity preservation, which is one of the fundamental requirements for skeletonization algorithms. The second skeletonization study proposes a method to generate curve skeletons with unit-width, which is required by many applications. The first segmentation study presents a new approach named Flexible Vector Flow (FVF) to address a few problems of other active contour models such as insufficient capture range and poor convergence for concavities. This approach was applied to brain tumor segmentation in two dimensional (2D) space. The second segmentation study extends the 2D FVF algorithm to three-dimension (3D) and utilizes it to automatically segment brain tumors in 3D.