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In this paper, we propose two new, perceptually motivated strategies to better measure the similarity of 2D shape instances that are in the form of closed contours. The first strategy handles shapes that can be decomposed into a base structure and a set of inward or outward pointing “strand” structures, where a strand structure represents a(More)
Accurately identifying corresponded landmarks from a population of shape instances is the major challenge in constructing statistical shape models. In this paper, we address this landmark-based shape-correspondence problem for 3D cases by developing a highly efficient landmark-sliding algorithm. This algorithm is able to quickly refine all the landmarks in(More)
Accurately identifying corresponded landmarks from a population of shape instances is the major challenge in constructing statistical shape models. In general, shape-correspondence methods can be grouped into one of two categories: global methods and pair-wise methods. In this paper, we develop a new method that attempts to address the limitations of both(More)
The objective of this study is to evaluate machine learning algorithms aimed at predicting surgical treatment outcomes in groups of patients with temporal lobe epilepsy (TLE) using only the structural brain connectome. Specifically, the brain connectome is reconstructed using white matter fiber tracts from presurgical diffusion tensor imaging. To achieve(More)
For person identification, motion and anthropometric bio-metrics are known to be less sensitive to photometric differences and more robust to obstructions such as glasses, hair, and hats. Existing gait-based methods are based on the accurate identification and acquisition of the gait cycle. This typically requires the subject to repeatedly perform a single(More)
This paper introduces a new benchmark study to evaluate the performance of landmark-based shape correspondence used for statistical shape analysis. Different from previous shape-correspondence evaluation methods, the proposed benchmark first generates a large set of synthetic shape instances by randomly sampling a given statistical shape model that defines(More)
The major challenge in constructing a statistical shape model for a structure is shape correspondence, which identifies a set of corresponded landmarks across a population of shape instances to accurately estimate the underlying shape variation. Both global or pairwise shape-correspondence methods have been developed to automatically identify the(More)
Multi-atlas patch-based label fusion methods have been successfully used to improve segmentation accuracy in many important medical image analysis applications. In general, to achieve label fusion a single target image is first registered to several atlas images. After registration a label is assigned to each target point in the target image by determining(More)
Structural magnetic resonance imaging (MRI) is a very popular and effective technique used to diagnose Alzheimer's disease (AD). The success of computer-aided diagnosis methods using structural MRI data is largely dependent on the two time-consuming steps: 1) nonlinear registration across subjects, and 2) brain tissue segmentation. To overcome this(More)
Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are(More)