Identification and Registration of Vascular Networks via Geometrical Graph-based Models.
Almasi, Sepideh.
2015
-
Abstract: In this
dissertation, we investigate the identification and application of geometrical
graph-based models (GGMs) of tubular structures with a focus on the vascular networks.
Our initial contribution is in the development of a method that directly extracts
microvasculature from highly artifacted raw 3-D fluorescence microscopy images. This
method comprises two novel initialization ... read moreand constrained recovery and enhancement
stages. The approach is fully automated using features derived from bi-scale statistical
measures and produces results robust to non-uniform illumination, low SNR, and local
structural variations. We next introduce a GGM-based method that identifies a piece-wise
linear skeletal approximation of a microvascular network that merely requires a rough
segmentation of the structures. The nodes of the graph represent the critical points
(CPs), defined as locations of large structural deformation and detected with template
and convex hull filterings that are independent of any a priori geometric and
probabilistic information such as direction, degree, or intensity distribution. The
anatomical connectivity of the CPs is derived by solving a binary integer program whose
utility function reflects both intensity profile and structural information of the
vasculature along the edges. In a "divide and conquer" manner, we have designed a graph
interpolation technique that extends applicability of the GGM identification method to
larger data sizes. Finally, the GGMs are employed to non-rigidly register cranial artery
networks which is formulated as a homologous landmarks guided point correspondence
problem. We have developed a novel collection of features, which we call a "signature,"
that captures geometrical attributes of nodes (location of junctions) and edges (length
and curvature of vessels) in a topologically encoded form. Using this signature, we
formulate registration as a Linear Assignment Problem (LAP) rather than the more
commonly employed (and NP-hard) quadratic assignment problem. Using signatures allows us
to relax the combinatorial problem to a convex form that results in a profound
computational complexity reduction. By solving the LAP via a graduated assignment
technique, nodes are first matched, and then the edge correspondences are determined
using a heuristic approach. The performance of this method is tested using clinical
angiography images and synthetic data sets. Quantitative results suggest that this
method is highly reliable under the influence of different perturbing factors that turns
it into a potential technique for inter-subject and multi-modal
registration.
Thesis (Ph.D.)--Tufts University, 2015.
Submitted to the Dept. of Electrical Engineering.
Advisor: Eric Miller.
Committee: Xiaoyin Xu, Brian Tracey, and Misha Kilmer.
Keyword: Electrical engineering.read less - ID:
- 7d2795587
- Component ID:
- tufts:21347
- To Cite:
- TARC Citation Guide EndNote
