MPhil Thesis Defence


"Multi-modal Image Registration Using Ordinal Features and
Generalized Survival Exponential Entropy"

By

Mr. Shu Liao


Abstract

In this thesis, we develop a new multi-modal image registration framework
with the following two major contributions. First, ordinal features are
exploited for representing images in the registration tasks. The ordinal
features are extracted by passing through the images through the
generalized ordinal filter bank, which effectively encodes the spatial
information between neighboring voxels and specific micro-structural
information in the images. The ordinal features are then integrated with
the intensity to form a two-element attribute vector. Second, we propose a
new similarity measure function based on the generalized survival
exponential entropy and mutual information (GSEE-MI). GSEE is estimated
from the cumulative distribution function instead of the density function.
It is observed that the interpolation artifact can be reduced.

The proposed framework is evaluated on four real MR-CT data sets for rigid
multi-modal image registration. The experimental results show that the
proposed method is more robust than the conventional mutual information
based method and methods based on Gabor wavelets and gradient magnitudes.
The accuracy of our method is comparable with these methods. For non-rigid
mono-modal registration, the proposed framework is also evaluated by using
both synthetic and real 3D datasets, and compared with two approaches: FFD
using SSD alone and Demons. The experimental results show that the
proposed method gives the highest accuracy in both normal and noisy
environments among the compared methods.


Date:				Wednesday, 22 August 2007

Time:				4:00p.m.-6:00p.m.

Venue:				Room 3416
				Lifts 17-18

Committee Members:		Dr. Albert Chung (Supervisor)
				Dr. Huamin Qu (Chairperson)
				Dr. Dit-Yan Yeung


**** ALL are Welcome ****