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

Chapter 3
Grouping and Linkage into
Connected Networks
Contents
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.2 Structure Based Approach . . . . . . . . . . . . . . . . . . . . . . 49
3.3 Image Based Approach . . . . . . . . . . . . . . . . . . . . . . . . 51
3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.5 Dicussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . 63
3.1 Introduction
In the last chapter we presented different methods for the identification and extraction
of tubular objects from medical images. With these methods major parts of tubular tree
structures can be already described. However, the methods are not capable of coping
with branching areas or other areas where the appearance of the vessels/airways strongly
deviates from the typical tube shape. This may be because of stenosis, aneurysms, or
tumors where the shape of the vessels/airways is perturbed, or in case of imaging artifacts
or adjacent structures within the same gray value range. Due to these cases the
tubular structures extracted with methods as the ones presented in the last chapter are
unconnected. A second issue not addressed with TDFs is that they identify all tubular
structures in a given volume of interest and they do not distinguish between multiple
potentially interwoven tubular tree structures. Consequently, such structures need to be
separated in an additional processing step.
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