- Library Home /
- Search Collections /
- Open Collections /
- Browse Collections /
- UBC Theses and Dissertations /
- Prostate segmentation in ultrasound images using image...
Open Collections
UBC Theses and Dissertations
UBC Theses and Dissertations
Prostate segmentation in ultrasound images using image warping and ellipsoid fitting Badiei, Sara
Abstract
This thesis outlines an algorithm for 2D and 3D semi-automatic segmentation of the prostate from B-mode trans-rectal ultrasound (TRUS) images. In semi-automatic segmentation, a computer algorithm outlines the boundary of the prostate given a few initialization points. The algorithm is designed for prostate brachytherapy and has the potential to: i) replace pre-operative manual segmentation, ii) enable intra-operative segmentation, and iii) be integrated into a visualization tool for training residents. The segmentation algorithm makes use of image warping to make the 2D prostate boundary elliptical. A Star Kalman based edge detector is then guided along the elliptical shape to find the prostate boundary in the TRUS image. A second ellipse is then fit to the edge detected measurement points. Once all 2D slices are segmented in this manner an ellipsoid is fit to the 3D cloud of points. Finally a reverse warping algorithm gives us the segmented prostate volume. In-depth 2D and 3D clinical studies show promising results. In 2D, distance based metrics show a mean absolute difference of 0.67 ± 0.18mm between manual and semi-automatic segmentation and area based metrics show average sensitivity and accuracy over 97% and 93% respectively. In 3D, i) the difference between manual and semi-automatic segmentation is on the order of interobserver variability, ii) the repeatability of the segmentation algorithm is consistently better than the intra-observer variability, and iii) the sensitivity and accuracy are 97% and 85% respectively. The 3D algorithm requires only 5 initialization points and can segment a prostate volume in less than 10 seconds (approximately 40 times faster than manual segmentation). The novelties of this algorithm, in comparison to other works, are in the warping and ellipse/ ellipsoid fitting steps. These two combine to provide a simple solution that works well even with non-ideal images to produce accurate, real-time results.
Item Metadata
Title |
Prostate segmentation in ultrasound images using image warping and ellipsoid fitting
|
Creator | |
Publisher |
University of British Columbia
|
Date Issued |
2007
|
Description |
This thesis outlines an algorithm for 2D and 3D semi-automatic segmentation of the prostate from
B-mode trans-rectal ultrasound (TRUS) images. In semi-automatic segmentation, a computer
algorithm outlines the boundary of the prostate given a few initialization points. The algorithm
is designed for prostate brachytherapy and has the potential to: i) replace pre-operative manual
segmentation, ii) enable intra-operative segmentation, and iii) be integrated into a visualization
tool for training residents.
The segmentation algorithm makes use of image warping to make the 2D prostate boundary
elliptical. A Star Kalman based edge detector is then guided along the elliptical shape to find the
prostate boundary in the TRUS image. A second ellipse is then fit to the edge detected measurement
points. Once all 2D slices are segmented in this manner an ellipsoid is fit to the 3D cloud of points.
Finally a reverse warping algorithm gives us the segmented prostate volume.
In-depth 2D and 3D clinical studies show promising results. In 2D, distance based metrics show
a mean absolute difference of 0.67 ± 0.18mm between manual and semi-automatic segmentation
and area based metrics show average sensitivity and accuracy over 97% and 93% respectively. In
3D, i) the difference between manual and semi-automatic segmentation is on the order of interobserver
variability, ii) the repeatability of the segmentation algorithm is consistently better than
the intra-observer variability, and iii) the sensitivity and accuracy are 97% and 85% respectively.
The 3D algorithm requires only 5 initialization points and can segment a prostate volume in less
than 10 seconds (approximately 40 times faster than manual segmentation).
The novelties of this algorithm, in comparison to other works, are in the warping and ellipse/
ellipsoid fitting steps. These two combine to provide a simple solution that works well even
with non-ideal images to produce accurate, real-time results.
|
Genre | |
Type | |
Language |
eng
|
Date Available |
2011-02-24
|
Provider |
Vancouver : University of British Columbia Library
|
Rights |
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
|
DOI |
10.14288/1.0065585
|
URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
|
Campus | |
Scholarly Level |
Graduate
|
Aggregated Source Repository |
DSpace
|
Item Media
Item Citations and Data
Rights
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.