Research
Projects
1 A
Framework for the Interactive Retrieval of Multimedia Objects
In this
project we are developing an efficient and effective framework to interactively
perform image segmentation and find “regions of interest” in a user input
multimedia object. Similar multimedia objects may then be retrieved from a
repository. The repository stores trained feature data of multimedia objects,
which are obtained by applying feature extraction and dimension reduction
analysis to the regions of interest. An advantage of our framework is that we
divide the task into two independent modules: objects are processed in the
image domain and the feature domain respectively. This two-module-based
framework makes the independent development and implementation of the software
for each domain possible. The framework has been successfully applied to image
databases and it can be easily modified to accommodate multimedia objects.
2 Wavelet Markov Models for Image Segmentation and
Denoising Applications
For statistical
modeling of images, an image is treated as a realization of a spatial
stochastic process defined on some domain in R2. One advantage of
statistical approaches for image modeling and processing is that they can
provide a unified view of learning, classification and generation. In this
project, several wavelet domain multiresolution
hidden Markov models are studied and proposed in terms of the applications to
image restoration and image segmentation. We also investigate the
shift-variance problem caused by real wavelet transforms. A new local hidden
Markov model is proposed based on the dual-tree complex wavelet transform that
is approximately shift-invariant. This algorithm is applied to denoising problems to remove additive white Gaussian noise
in an image. Our scheme outperforms those approaches based on real wavelet
transforms and provides state-of-the-art image denoising
performance.
3 Medical Image Registration
There are
a number of medical imaging technologies and one technology may image one
component of the anatomy, for example bone or soft tissue, better than another
technology. Registration is the alignment of images of the same area produced
by different technologies (multimodal images). The resulting aligned images
give medical professionals a much better view of the imaged region. Medical
image registration has been utilized in the diagnosis of breast cancer, cardiac
studies, and a variety of neurological disorders including brain tumors.
A new
approach for registration called maximization of mutual information, the
quantifying of the similarity between corresponding voxel
(a small 3-D region) intensities of two
images, has been demonstrated to be a very powerful criterion for
three-dimensional medical image registration. We proposed new implementation
approaches for multimodal brain image registration based on mutual information.
Each of these approaches gives improved accuracy or/and speed. We illustrate
these approaches by presenting the results of tests from the data of a group of
patients. Registration accuracy is evaluated as part of the Retrospective
Registration Evaluation Project. We also compare the performance of each
approach to results available in the literature. Our results have shown that
each approach can reach sub-voxel accuracy with no
loss of speed.
4 Retinal Image Registration
The
retinal image registration work develops an object-oriented software system for
automatic retinal image registration by mutual information maximization. For
maximum portability the software is written in Java, using MVC
(model-view-control) framework. We use the simplex downhill method as the
optimization algorithm which is easy to implement, and is quick in practice. We
demonstrate that this algorithm registers temporal and stereo retinal image
pairs with a very high success rate, a satisfactory registration accuracy
compared to point matching results, and within a clinically acceptable time.
5 Non-Rigid Medical Image Registration
The
clinical need for PET-CT registration (Positron Emission Tomography-Cat Scan, a
multimodal image registration) is becoming a necessity. This registration gives an interpretation of
functional and structural images. PET provides information regarding functional
abnormalities that in many cases are not detectable with CT imaging,
the details depicted on CT scans can help pinpoint the location of the disease
with a level of reliability that PET images alone can not match. In multimodal
registration images may not be the same size so non-rigid registration
approaches are necessary. In order to optimize the quality of CT-PET fused
images, non-rigid image registration methods are needed to find local
deformation between the two types of images. We propose a hybrid non-rigid medical
image registration method using both feature and intensity based
approaches.
6 Fast Region Growing Approaches for CT Angiography
Applications
Much
research on region growing has focused on the definition of the homogeneity
criterion or the growing and merging criterion. However, one disadvantage of
conventional region growing is redundancy. The redundancy causes a large memory
usage, and the computation-efficiency is very low, especially for 3D images. To
overcome this problem, a non-recursive single-pass 3D region growing was
implemented and successfully applied to 3D CT angiography applications for
vessel segmentation and bone removal. Clinical testings
of this algorithm on brain CT angiography show this technique
could effectively remove from the image the whole skull and most of the bones
on the skull base, and thus reveal the cerebral vascular structures clearly.
7 Decomposition of signals with a G-reductive sum of
Gaussians
For any
given signal, there is generally more than one, perhaps many, Gaussian
solutions within a given tolerance. A decomposition algorithm which tends to
find a smaller Gaussian solution than another decomposition algorithm is said
to be a more G-reductive algorithm. Our work here focuses on speeding up the
process of G-reductive Gaussian decomposition without significantly worsening
G-reduction. An effective parallel model for Gaussian decomposition is
discussed. Also a novel method is presented in which each
iteration has a low time complexity.
8 Edge Oriented and Region Oriented Segmentation
Methods
Segmentation
techniques are used to automate the process of isolating the relevant objects
within a digital image from the extraneous background. In an edge-oriented
scheme, the relevant object is identified by locating its outer edges. Such
methods may be used, for example, for the segmentation of license plate numbers
from an image of an automobile. In a region-oriented scheme, the relevant
object is identified by accumulating neighboring pixels of similar intensities.
This sort of method was developed to segment the soft tissue, lung and external
regions in a tomographic image obtained from nuclear
medicine imaging.