Given a known signal and perfect knowledge of the environment there exist few
detection and estimation problems that cannot be solved. Detection performance is
limited by uncertainty in the signal, an imperfect model, uncertainty in environmental
parameters, or noise. Complex environments such as the ocean acoustic waveguide and
the human anatomy are difficult to model exactly as they can differ, change with time,
or are difficult to measure. We address the uncertainty in the model or parameters by
incorporating their possibilities in our detection algorithm. Noise in the signal is not so
easily dismissed and we set out to provide cases in which what is frequently termed a
nuisance parameter might increase detection performance. If the signal and the noise
component originate from the same system then it might be reasonable to assume that
the noise contains information about the system as well.
Because of the negative effects of ionizing radiation it is of interest to maximize
the amount of diagnostic information obtained from a single exposure. Scattered
radiation is typically considered image degrading noise. However it is also dependent
on the structure of the medium and can be estimated using stochastic simulation. We
describe a novel Bayesian approach to signal detection that increases performance by
including some of the characteristics of the scattered signal. This dissertation examines
medical imaging problems specific to mammography. In order to model environmental
uncertainty we have written software to produce realistic voxel phantoms of the breast.
The software includes a novel algorithm for producing three dimensional distributions
of fat and glandular tissue as well as a stochastic ductal branching model.
The image produced by a radiographic system cannot be determined analytically
since the interactions of particles are a random process. We have developed a particle
transport software package to model a complete radiographic system including a
realistic x-ray spectrum model, an arbitrary voxel-based medium, and an accurate
material library. Novel features include an efficient voxel ray tracing algorithm that
reflects the true statistics of the system as well as the ability to produce separable images
of scattered and direct radiation.
Similarly, the ocean environment includes a high degree of uncertainty. A
pressure wave propagating through a channel produces a measurable collection of
multipath arrivals. By modeling changes in the pressure wave front we can estimate the
expected pattern that appears at a given location. For this purpose we have created an
ocean acoustic ray tracing code that produces time-domain multipath arrival patterns
for arbitrary 3-dimensional environments. This iterative algorithm is based on a
generalized recursive ray acoustics algorithm. To produce a significant gain in
computation speed we model the ocean channel as a linear, time invariant system. It
differs from other ocean propagation codes in that it uses time as the dependent variable
and can compute sound pressure levels along a ray path effectively measuring the
spatial impulse response of the ocean medium.
This dissertation also investigates Bayesian approaches to source localization in a
3-D uncertain ocean environment. A time-domain-based optimal a posteriori probability
bistatic source localization method is presented. This algorithm uses a collection of
acoustic time arrival patterns that have been propagated through a 3-D acoustic model
as the observable data. These replica patterns are collected for a possible range of
unknown environmental parameters. Receiver operating characteristics for a bistatic
detection problem are presented using both simulated and measured data. / Dissertation
| Identifer | oai:union.ndltd.org:DUKE/oai:dukespace.lib.duke.edu:10161/205 |
| Date | 10 May 2007 |
| Creators | Shorey, Jamie Margaret |
| Contributors | Nolte, Loren W., Krolik, Jeffery L., Liu, Qing H., Lo, Joseph Y., Samei, Ehsan |
| Source Sets | Duke University |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 7046863 bytes, application/pdf |
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