Segmentation of vascular structures based on adaptive tracking and new seed detection
適應的 追跡과 始作点 檢出에 基盤한 管形體의 領域化에 대한 硏究

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Shim, Hakjoon
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segmentationvascular structurevessel trackingcerebral arteriesCTAinter-partition trackingparticle filtershape analysiscoronary arteriesMDCT영역화관형 구조체혈관 추적뇌동맥입자필터형상분석
In this dissertation, methods to construct vascular structures in
computed tomography angiography (CTA) are proposed. The
construction is useful in numerous applications, including
vascular surgery, interventional radiology (IR), and medical

The construction commences with tracking of the central axis of a
vessel segment. In this dissertation two adaptive tracking methods
are proposed to extract a cerebral arterial segment in CTA.
Segmentation of cerebral arteries in CTA is very challenging
mainly due to bone contact and vein contamination. Both the two
proposed tracking methods iteratively accumulate elliptical
cross-sections from the given initial seed.

The first method is very intuitive. The boundary of each
cross-section is modelled as an ellipse with border points
detected by gradient thresholds. The thresholds are established
adaptively to bone contact and vein contamination. This
adaptiveness improves robustness of the tracking compared to a
conventional method.

The other tracking method is augmented by rigorous mathematical
framework of a particle filter (PF). Its measurement model is
defined as the border points on the plane normal to the axis as
the observation. The border points are detected using the radial
component of the gradient and the adaptive thresholds in the same
way as the intuitive tracking. The outliers in the measurement
vector are discarded in the computation of the distance between
each particle and the actual observation. The robustness of the PF
tracking method is confirmed by intensive experiments on both
clinical and synthesized vessel data.

Since vascular structures are composed a number of vessel
segments, tracking of a vessel segment need to be supplemented by
any methods to extract many vessel segments. Specifically to
cerebral arteries in CTA, the head CT volume is partitioned into
two sub-volumes according to the amount of contact between bone
and arteries. In the lower sub-volume, as the arteries are often
adjacent to bone or veins, they are extracted by the
above-mentioned tracking methods. In the upper sub-volume where
bone and vessels are not adjacent to each other, vessels can be
segmented well by intensity thresholds and connected component
analysis (CCA). Separate application of different algorithms to
lower and upper sub-volumes leads to discontinuity in the
partitioning slice and inter-partition tracking algorithm is also
proposed to resolve this discontinuity problem.

The other method to construct vascular structure is based on
detection of new seeds and can be generalized to other vascular
structures, i.e. pulmonary vasculature, cardio-vasculature or
hepatic vasculature. New seed is detected by searching for tubular
object near the vessel segment already extracted. Objects are
classified into tubular objects or non-tubular objects according
to the eigenvalues of the covariance matrix.

Construction results of the two methods are compared to
Registration-Subtraction results of pre-contrast and post-contrast
scanned volumes and are evaluated to show comparable performance
by a clinical expert.

Finally, as an application of the proposed methods, coronary
arteries are extracted, which is not much different from
extraction of cerebral arteries. It can be estimated as easier,
since the main two problems of bone contact and vein contamination
need not be considered. However, limited contrast resolution may
cause leakage of the tracking. As multi-detector row CT (MDCT)
technology develops, non-invasive image of coronary arteries
becomes realizable and their extraction becomes more important.
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Electrical and Computer Engineering (전기·정보공학부)Theses and dissertations (학위논문_전기·정보공학부)
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