Chapter title |
Primal/dual linear programming and statistical atlases for cartilage segmentation.
|
---|---|
Chapter number | 65 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007
|
Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, January 2007
|
DOI | 10.1007/978-3-540-75759-7_65 |
Pubmed ID | |
Book ISBNs |
978-3-54-075758-0, 978-3-54-075759-7
|
Authors |
Ben Glocker, Nikos Komodakis, Nikos Paragios, Christian Glaser, Georgios Tziritas, Nassir Navab, Glocker, Ben, Komodakis, Nikos, Paragios, Nikos, Glaser, Christian, Tziritas, Georgios, Navab, Nassir |
Abstract |
In this paper we propose a novel approach for automatic segmentation of cartilage using a statistical atlas and efficient primal/dual linear programming. To this end, a novel statistical atlas construction is considered from registered training examples. Segmentation is then solved through registration which aims at deforming the atlas such that the conditional posterior of the learned (atlas) density is maximized with respect to the image. Such a task is reformulated using a discrete set of deformations and segmentation becomes equivalent to finding the set of local deformations which optimally match the model to the image. We evaluate our method on 56 MRI data sets (28 used for the model and 28 used for evaluation) and obtain a fully automatic segmentation of patella cartilage volume with an overlap ratio of 0.84 with a sensitivity and specificity of 94.06% and 99.92%, respectively. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 3 | 8% |
Switzerland | 1 | 3% |
Unknown | 36 | 90% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 14 | 35% |
Researcher | 8 | 20% |
Student > Master | 5 | 13% |
Professor | 4 | 10% |
Professor > Associate Professor | 2 | 5% |
Other | 4 | 10% |
Unknown | 3 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 22 | 55% |
Engineering | 10 | 25% |
Agricultural and Biological Sciences | 2 | 5% |
Medicine and Dentistry | 1 | 3% |
Nursing and Health Professions | 1 | 3% |
Other | 0 | 0% |
Unknown | 4 | 10% |