Abstract3D Medical imaging techniques have become extremely important tools in
patient diagnosis. However, they produce large amounts of data that is difficult
to interpret, and can currently only be analysed by highly trained people.
Datasets are large – the female Visible Human dataset is around 40 Gb in size.
Processing any dataset of this size will obviously be computationally demanding.
Currently segmentation of images is a predominantly manual process. Tools that
are available allow segmentation to be done on a slice-by-slice basis, often
using a flood-fill or region growing approach based on colour or texture space.
This report outlines research into an automated texture based segmentation
technique. The research compared the effectiveness of using simple and energy
efficient DCT (Discrete Cosine Transform) and Haar transforms (in both 2D and
3D forms) as a description of texture at each location within an image. This
description was initially used as a vector in feature space, allowing segmentation
to be carried out using a Gaussian Mixture Model and some post processing
techniques. The transforms were then extended to make them independent of
variations in intensity, a common issue in medical imaging. However, although
now robust to intensity variations, the results were not of sufficient quality to be
useful in a real application.
To improve the quality of results, a model based approach based on an AAM
(Active Appearance Model) was considered. A traditional AAM uses an intensity
based appearance model, which while less computationally demanding than a
more complex texture based appearance model, can give poor results when
subjected to intensity variations. When complex texture descriptions are used to
create the appearance model results are much improved, but this is at the
expense of run time, which can make the techniques less practical.
A novel combination of mDCT (modified DCT, which is intensity invariant) and
an AAM was implemented and tested. When presented with 3D volumes which
had been subjected to intensity variations this was seen to generate much better
results than a traditional AAM, while maintaining a practical run time.
Using this approach the time taken to carry out segmentations was less than 10
minutes (when run in Matlab on a typical datacentre based Linux machine). This
showed the process to be practical in terms of quality of results, run time and
|Date of Award||2017|
|Supervisor||Ian T. Nabney (Supervisor)|
- Active Appearance Model
- discrete cosine transform
- Haar transform
- intensity invariant