Automated Segmentation of the Pericardium Using a Feature Based Multi-atlas Approach
(2014) In Master's Theses in Mathematical Sciences FMA820 20141Mathematics (Faculty of Engineering)
- Abstract
- Multi-atlas segmentation is a widely used method that has proved to work well for the problem of segmenting organs in medical images. But standard methods are time consuming and the amount of data quickly grows to a point making use of these methods intractable. In this work we present a fully automatic method for segmentation of the pericardium in 3D CTA-images. We use a multi-atlas approach based on feature based registration (SURF) and use RANSAC to handle the large amount of outliers. The multi-atlas votes are fused by incorporating them into an MRF together with the intensity information of the target image and the optimal segmentation is found efficiently using graph cuts. We evaluate our method on a set of 10 CTA-volumes with manual... (More)
- Multi-atlas segmentation is a widely used method that has proved to work well for the problem of segmenting organs in medical images. But standard methods are time consuming and the amount of data quickly grows to a point making use of these methods intractable. In this work we present a fully automatic method for segmentation of the pericardium in 3D CTA-images. We use a multi-atlas approach based on feature based registration (SURF) and use RANSAC to handle the large amount of outliers. The multi-atlas votes are fused by incorporating them into an MRF together with the intensity information of the target image and the optimal segmentation is found efficiently using graph cuts. We evaluate our method on a set of 10 CTA-volumes with manual expert delineation of the pericardium and we show that our method provides comparable results to a standard multi-atlas algorithm but at a large gain in computational efficiency. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/4695207
- author
- Norlén, Alexander LU
- supervisor
-
- Fredrik Kahl LU
- Olof Enqvist LU
- organization
- course
- FMA820 20141
- year
- 2014
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- computer vision, medical image analysis, multi-atlas segmentation, feature based registration, Markov Random Fields, pericardium segmentation
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMA-3267-2014
- ISSN
- 1404-6342
- other publication id
- 2014:E53
- language
- English
- id
- 4695207
- date added to LUP
- 2014-12-15 13:24:38
- date last changed
- 2014-12-15 13:24:38
@misc{4695207, abstract = {{Multi-atlas segmentation is a widely used method that has proved to work well for the problem of segmenting organs in medical images. But standard methods are time consuming and the amount of data quickly grows to a point making use of these methods intractable. In this work we present a fully automatic method for segmentation of the pericardium in 3D CTA-images. We use a multi-atlas approach based on feature based registration (SURF) and use RANSAC to handle the large amount of outliers. The multi-atlas votes are fused by incorporating them into an MRF together with the intensity information of the target image and the optimal segmentation is found efficiently using graph cuts. We evaluate our method on a set of 10 CTA-volumes with manual expert delineation of the pericardium and we show that our method provides comparable results to a standard multi-atlas algorithm but at a large gain in computational efficiency.}}, author = {{Norlén, Alexander}}, issn = {{1404-6342}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master's Theses in Mathematical Sciences}}, title = {{Automated Segmentation of the Pericardium Using a Feature Based Multi-atlas Approach}}, year = {{2014}}, }