Segmentation of 3D Tubular Tree Structures in Medical Images ...

14 Chapter 1. Introduction
part:
Methods: In the methods part, we are dealing with the development and evaluation of
suitable methods for the individual building blocks of the proposed general approach [9]
(Section 1.2) that forms the basis of our work. Depending on the applications, the requirements
for the these building blocks may vary and therefore different methods for
each building block are presented. The methods are compared to each other and properties
of the different methods are analyzed.
In Chapter 2 we first review different methods for the detection/extraction of tubular
structures from volumetric medical images known from the literature. We highlight a
core problem of conventional tube detection approaches that is related with the utilized
Gaussian scale space – diffusion of nearly image structures on larger scales – which may lead
to false responses. To overcome this problem, we introduce a novel approach for detection
of tubular objects [6] (Section 2.3) to address this issue by replacing the conventional
multi-scale gradient vector computation by the Gradient Vector Flow (GVF) [168]. As
shown in our experiments, this approach proofs to be beneficial in case of surface patches
or other nearby image structures. Additionally, the method has the ability to cope with
varying background conditions, an issue that can not be addressed using conventional
Gaussian scale space based methods. This makes the presented GVF-based approach also
applicable in areas where the utilization of tube detection filters has not been considered
so far. Further, we present an efficient height ridge traversal for extraction of the tubular
structures from the tube detection filter responses (Section 2.4).
In Chapter 3 we introduce two novel methods for grouping and linking identified tubular
structures into complete tree structures. The first method [9] (Section 3.2) is based
on structural properties of the identified tubular structures and enables a separation of
interwoven tubular tree structures and handling of various kinds of disturbances – objectives
that are typically not addressed in this level. The second method [5] (Section 3.3)
utilizes properties of the GVF and enables an extraction of centered linkage paths in areas
where the shape of the structures deviate strongly from a typical tubular shape such as
stenosis, aneurysms, or branching areas. This allows extraction of high quality curve skeletons
directly from the the gray scale image with an accuracy comparable to sophisticated
skeletonization methods, what supersedes the need to deal with a segmentation problem
and a skeletonization problem.
In Chapter 4 we present two novel methods for the segmentation of tubular objects
associated with known tubular structures. Both aim at preserving the topology of the