Feature-Based Correspondences to Infer the Location of Anatomical Landmarks

A methodology has been developed for automatically determining inter-image correspondences between cliques of features extracted from a reference and a query image. Cliques consist of up to threefeatures and correspondences between them are determined via a hierarchy of similarity metrics based on the inherent properties of the features and geometric relationships between those features. As opposed to approaches that determine correspondences solely by voxel intensity, features that also include shape description are used. Specifically, medial-based features areemployed because they are sparse compared to the number of image voxels and can be automatically extracted from the image.The correspondence framework has been extended to automatically estimate the location of anatomical landmarks in the query image by adding landmarks to the cliques. Anatomical landmark locationsare then inferred from the reference image by maximizing landmark correspondences. The ability to infer landmark locations has provided a means to validate the correspondence framework in thepresence of structural variation between images. Moreover, automated landmark estimation imparts the user with anatomical information and can hypothetically be used to initialize andconstrain the search space of segmentation and registration methods.Methods developed in this dissertation were applied to simulated MRI brain images, synthetic images, and images constructed from several variations of a parametric model. Results indicate that the methods are invariant to global translation and rotation and can operate in the presence of structure variation between images.The automated landmark placement method was shown to be accurate as compared to ground-truth that was established both parametrically and manually. It is envisioned that these automated methods could prove useful for alleviating time-consuming and tedious tasks in applications that currently require manual input, and eliminate intra-user subjectivity

GRADIENT-ORIENTED BOUNDARY PROFILES FOR SHAPE ANALYSIS USING MEDIAL FEATURES

Gradient-oriented boundary profiles have been developed as a novel method to parameterize boundaries. Boundary profiles are created at locations of high gradient magnitude by averaging intensity within a neighborhood of voxels oriented along the image gradient, making them rotationally invariant and relatively insensitive to image noise. A cumulative Gaussian is fit to the collection of averaged voxel intensities yielding estimates of (1) extrapolated intensity values for voxels located far inside and outside of a boundary and (2) anatomical boundary location. Intrinsic measures of confidence have been developed to eliminate low-confidence parameter estimates. Thresholds placed on these measures of confidence allow for high-confidence unsupervised classification of boundaries. The validity of gradient-oriented profiles is demonstrated on artificially generated three-dimensional test data and shown to accurately parameterize and classify the boundary. Applying the measures of confidence and establishing thresholds, the accuracy of boundary location and intensities estimates improved drastically, making them a high-quality replacement for simpler methods of boundary detection. Towards shape analysis, gradient-oriented boundary profiles are applied to an existing a medial-based approach to shape analysis, known as core atoms. Core atoms in their previous implementation were based on simple gradient direction and unable to form without a priori knowledge of object intensity relative to background. Boundary profiles were applied to core atoms permitting the formation of so called "core profiles". Core profiles remove any restriction on the object's or the background's intensity, allowing multiple objects of differing intensities to be located with a single application.Core profiles were applied to 3D computer-generated data, as well as RT3D ultrasound cardiac phantom data. It was shown on computer-generated data that calculating the volume with core profiles is more accurate then calculating the volume with core atoms, because of the improved accuracy of the boundary location. Two new methods of automatically measuring volume on non-parametric data with core profiles are proposed. Future work with includes constructing medial node models improved by gradient-oriented boundary profiles for automated left ventricular identification and measurement

Valuable assistance and advice were received

grant from the Whitaker Foundation to th