THESIS
2017
xii, 58 pages : illustrations ; 30 cm
Abstract
Curvilinear structure analysis is the foundation of a wide range of applications, for instance image
enhancement, centerline detection, vascular segmentation and surface reconstruction. Specifically
in medical imaging, the appearance, morphology and topology of curvilinear objects are important
indicators of many vascular disease and systemic disorders. As such, the analysis of curvilinear
structure is valuable for pathology quantification, disease diagnosis and surgery planning.
In this thesis, we first review the existing techniques for curvilinear structure analysis. The descriptor
- conventional Optimally Oriented Flux (OOF) is then introduced and enhanced for three
dimensional curvilinear structure analysis, namely inter-scale OOF analysis. OOF attempts to find
an optim...[
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Curvilinear structure analysis is the foundation of a wide range of applications, for instance image
enhancement, centerline detection, vascular segmentation and surface reconstruction. Specifically
in medical imaging, the appearance, morphology and topology of curvilinear objects are important
indicators of many vascular disease and systemic disorders. As such, the analysis of curvilinear
structure is valuable for pathology quantification, disease diagnosis and surgery planning.
In this thesis, we first review the existing techniques for curvilinear structure analysis. The descriptor
- conventional Optimally Oriented Flux (OOF) is then introduced and enhanced for three
dimensional curvilinear structure analysis, namely inter-scale OOF analysis. OOF attempts to find
an optimal axis along which the image gradients are projected prior to quantifying the amount of
gradient flows. To solve the overshooting problem in conventional OOF, in the proposed method,
responses obtained in different radii are competed to eliminate the disturbances introduced from
the nearby non-curvilinear structures. The performance of conventional OOF also suffers from
the circular cross-sectional assumption. Therefore, we propose to acquire the residual responses
separately along the object normal and tangent spaces in order to benefit the analysis of eccentric
structures. Further, local orientation coherence is enforced to generated a more consistent OOF vector field. With the inter-scale OOF analysis, three important characteristics of curvilinear structure
can be estimated simultaneously: curvilinearity, dimension and orientation. We experimentally
demonstrate that the new descriptor delivers more accurate and stable detection responses under the
interference of adjacent high-contrast structures and exhibits extraordinary noise robustness.
Vascular segmentation is one important application of curvilinear structure analysis. In order to
evaluate the capability of inter-scale OOF and to reversely facilitate the curvilinear structure analysis
in the understanding of vasculatures, we then propose to feed it into two existing general segmentation
frameworks, which are Random Walks and Continuous Max-Flow, for three dimensional
vascular segmentation. The proposed methods have been evaluated on public available databases
and comparable segmenting results have been achieved.
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