2000
@phdthesis{Ban2000,
vgclass = {thesis},
author = {Ravi Bansal},
title = {Information Theoretic Integrated Segmentation and
Registration of Dual {2D} Portal Images and {3D} {CT} Images},
school = {Departments of Diagnostic Radiology and Electrical
Engineering, Yale School of Medicine, Yale University},
address = {Brady Memorial Laboratory Room 332, 310 Cedar Street, PO
Box 208042, New Haven, CT 06520-8042, USA},
month = {May},
year = {2000},
url = {http://noodle.med.yale.edu/thesis/bansal.html},
url1 = {http://noodle.med.yale.edu/thesis/bansal.pdf},
abstract = {This thesis develops an information theoretic registration
framework where the segmentation and registration of dual
anterior-posterior and left lateral portal images to a treatment
planning three-dimensional computed tomography (CT) image is carried
out simultaneously and iteratively. The proposed registration framework
is termed the minimax entropy registration framework as it has two
steps, the max step and the min step. Appropriate entropies are
evaluated in each step in order to segment the portal images (the max
step) and to estimate the registration parameters (the min step). The
registration framework is based on the intuition that if some structure
can be segmented in the portal image, the segmented structure, in
addition to the gray-scale pixel intensity information, can be used to
better estimate the registration parameters. On the other hand, given
an estimate of the registration parameters, information from the high
resolution 3D CT image dataset can be used to guide segmentation of the
portal images. Performance analysis and comparisons to other
registration methods demonstrates the robustness and accuracy of the
proposed registration framework.
To further improve the estimated segmentation of the portal images and
the accuracy of the estimated registration parameters, correlation
among the image pixel intensities is modeled using a one-dimensional
Markov random process. Line processes are incorporated in the Markov
random process model which estimate the edges between the segmented
regions. As a future research direction, we propose to incorporate the
estimated edges in the min step to further improve the registration.
The proposed framework is independent of the image dataset and hence,
in general, can be straightforwardly extended to register any low
resolution, low contrast image to a high resolution, high contrast
image.},
}