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We present a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. Neighboring anatomical structures in the human brain exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem,...
Registration of multiple medical images is required for analysis of both pathological and normal anatomical human body structures. In this study, we defined and automatically detected some anatomical landmarks which are considered to be unaffected from brain deformation such as symmetry axis of brain, nose tips, eye ball centers, and location of center of masses of certain slices. Registration is...
This paper presents a new method for multiple structure segmentation, using a maximum a posteriori (MAP) estimation framework, based on prior shape densities involving nonparametric multivariate kernel density estimation of multiple shapes. Our method is motivated by the observation that neighboring or coupling structures in medical images generate configurations and co-dependencies which could potentially...
Accurate staging of nodal cancer still relies on surgical exploration because many primary malignancies spread via lymphatic dissemination. The purpose of this study was to utilize nanoparticle-enhanced lymphotropic magnetic resonance imaging (LN-MRI) to explore semi-automated noninvasive nodal cancer staging. We present a joint image segmentation and registration approach, which makes use of the...
This paper presents a fast and efficient method to determine intervertebral disk orientation in a magnetic resonance (MR) image of the spine. The algorithm originates from active contour theory and enforces a shape constraint to avoid leaks through weak or non-existent boundaries. The method represents a vertebra as a rectangle, modeled as a semi-affine transformation applied to the unit square. A...
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