Gregory J. Garvin

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This study investigates novel object-interaction priors for graph cut image segmentation with application to intervertebral disc delineation in magnetic resonance (MR) lumbar spine images. The algorithm optimizes an original cost function which constrains the solution with learned prior knowledge about the geometric interactions between different objects in(More)
We state vertebral body (VB) segmentation in MRI as a distribution-matching problem, and propose a convex-relaxation solution which is amenable to parallel computations. The proposed algorithm does not require a complex learning from a large manually-built training set, as is the case of the existing methods. From a very simple user input, which amounts to(More)
This study proposes an unsupervised intervertebral disc segmentation system based on middle sagittal spine MR scans. The proposed system employs the novel anisotropic oriented flux detection scheme which helps distinguish the discs from the neighboring structures with similar intensity, recognize ambiguous disc boundaries, and handle the shape and intensity(More)
As a nondestructive method of historical and anthropologic inquiry, imaging has played an important role in mummy studies over the past several decades. Recent technologic advances have made multidetector computed tomography (CT) an especially useful means for deepening the present understanding of ancient cultures by examining preserved human remains. In(More)
This study investigates a new parameterization of deformation fields for image registration. Instead of standard displacements, this parameterization describes a deformation field with its transformation Jacobian and curl of end velocity field. It has two important features which make it appealing to image registration: 1) it relaxes the need of an explicit(More)
This study investigates a novel CT/MR spine image fusion algorithm based on graph cuts. This algorithm allows physicians to visually assess corresponding soft tissue and bony detail on a single image eliminating mental alignment and correlation needed when both CT and MR images are required for diagnosis. We state the problem as a discrete multilabel(More)
Centerline extraction and segmentation of the spinal cord--an intensity varying and elliptical curvilinear structure under strong neighboring disturbance are extremely challenging. This study proposes the gradient competition anisotropy technique to perform spinal cord centerline extraction and segmentation. The contribution of the proposed method is(More)
This study proposes the novel dilated divergence scale-space representation for multidimensional curve-like image structure analysis. In the proposed framework, image structures are modeled as curves with arbitrary thickness. The dilated divergence analyzes the structure boundaries along the curve normal space in a multi-scale fashion. The dilated(More)
This study investigates segmentation with a novel invariant compactness constraint. The proposed prior is a high-order fractional term, which is not directly amenable to powerful optimizers. We derive first-order Gateâux derivative approximations of our compactness term and adopt an iterative trust region paradigm by splitting our problem into constrained(More)