<p> A new parameter-free texture feature-based seeded region growing
algorithm is proposed in this dissertation for automated segmentation of organs in
abdominal MR images. This algorithm requires that a user only mouse clicks
twice to identify the upper left and lower right corners of a rectangular region of
interest (ROI). With this given ROI, a seed point is automatically selected based
on homogeneity criteria. Intensity as well as four texture features: 20 cooccurrence
texture features, Gabor texture feature, and both 20 and 3D
semivariogram texture features are extracted from the image and a seeded region
growing algorithm is performed on these feature spaces. A threshold is then
obtained by taking a lower value just before the one which results in an
' explosion '. An optional Snake post-processing tool is also provided to obtain
better organ delineation. The comparative results of the texture features and
intensity are reported using both normal digital images and abdominal MR images
acquired from ten patients. Comparisons of Before and After Snake are also
presented. Generally, Gabor texture feature is found to perform the best among all
features . The experimental results of the proposed approach show that it is fast
and accurate when combined with Gabor texture feature or intensity feature and
should prove a boon to production radiological batch processing. </p> / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/19513 |
| Date | 03 1900 |
| Creators | Wu, Jie |
| Contributors | Kamath, Markad V., Poehlman, W. F. S., Computer Science |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
Page generated in 0.002 seconds