Various types of vascular diseases such as carotid stenosis, aneurysms, Arterio-venous Malformations (AVM) and Subarachnoid Hemorrhage (SAH) caused by the rupture of an aneurysm are some of the common causes of stroke. The diagnosis and management of such vascular conditions presents a challenge. In this dissertation we present a vascular analysis framework for Computer Aided Diagnosis (CAD) of the neuro-vasculature. We develop methods for 3D vascular decomposition, vascular skeleton extraction and identification of vascular structures such as aneurysms.
Owing to the complex and highly tortuous nature of the vasculature, analysis is often only attempted on a subset of the vessel network. In our framework we first, compute the decomposition of the vascular tree into meaningful sub-components. A novel spectral segmentation approach is presented that focuses on the eigenfunctions of the Laplace-Beltrami operator (LBO), FEM discretization. In this approach, we attain a set of vessel segmentations induced by the nodal sets of the LBO. This discretization produces a family of real valued functions, which provide interesting insights in the structure and morphology of the vasculature. Next, a novel Weighted Approximate Convex Decomposition (WACD) strategy is proposed to understand the nature of complex vessel structures. We start by addressing this problem of vascular decomposition as a cluster optimization problem and introduce a methodology for compact geometric decomposition. These decomposed vessel structures are then grouped into anatomically relevant sections using a novel vessel skeleton extraction methodology that utilizes a Laplace based operator. Vascular analysis is performed by obtaining a surface mapping between decomposed vessel sections. A non-rigid correspondence between vessel surfaces are achieved using Thin Plate Splines (TPS), and changes between corresponding surface morphologies are detected using Gaussian curvature maps and mean curvature maps. Finally, characteristic vascular structures such as vessel bifurcations and aneurysms are identified using a Support Vector Machine (SVM) on the most relevant eigenvalues, obtained through feature selection.
The proposed CAD framework was validated using pre-segmented sections of vasculature archived for 98 aneurysms in 112 patients. We first test our methodologies for vascular segmentation and next for detection. Our vascular segmentation approaches produced promising results, 81% of the vessel sections correctly segmented. For vascular classification, Recursive Feature Elimination (RFE) was performed to find the most compact and informative set of features. We showed that the selected sub-set of eigenvalues produces minimum error and improved classifier precision. This analysis framework was also tested on longitudinal cases of patients having internal cerebral aneurysms. Volumetric and surface area comparisons were made by establishing a correspondence between segmented vascular sections. Our results suggest that the CAD framework was able to decompose, classify and detect changes in aneurysm volumes and surface areas close to that segmented by an expert.
|Advisor:||Scott, Peter D.|
|Commitee:||Govindaraju, Venu, Kesavadas, Thenkurussi|
|School:||State University of New York at Buffalo|
|Department:||Computer Science and Engineering|
|School Location:||United States -- New York|
|Source:||DAI-B 75/06(E), Dissertation Abstracts International|
|Subjects:||Medical imaging, Artificial intelligence, Computer science|
|Keywords:||Aneurysm, Computer aided diagnosis, Reconstruction, Skeleton extraction, Spectral, Vascular segmentation|
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