Abstract
Accurate segmentation of neonatal brain MR images remains challenging mainly due to poor spatial resolution, low tissue contrast, high intensity inhomogeneity. Most existing methods for neonatal brain segmentation are atlas-based and voxel-wise. Although parametric or geometric deformable models have been successfully applied to adult brain segmentation, to the best of our knowledge, they are not explored in neonatal images. In this paper, we propose a novel neonatal image segmentation method, combining local intensity information, atlas spatial prior and cortical thickness constraint, in a level set framework. Besides, we also provide a robust and reliable tissue surfaces initialization for our proposed level set method by using a convex optimization technique. Validation is performed on 10 neonatal brain images with promising results.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 1-10 |
| Number of pages | 10 |
| Volume | 6326 LNCS |
| DOIs | |
| Publication status | Published - 2010 Nov 9 |
| Externally published | Yes |
| Event | 5th International Workshop on Medical Imaging and Augmented Reality, MIAR 2010 - Beijing, China Duration: 2010 Sep 19 → 2010 Sep 20 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 6326 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 5th International Workshop on Medical Imaging and Augmented Reality, MIAR 2010 |
|---|---|
| Country | China |
| City | Beijing |
| Period | 10/9/19 → 10/9/20 |
Fingerprint
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Automatic segmentation of neonatal images using convex optimization and coupled level set method. / Wang, Li; Shi, Feng; Gilmore, John H.; Lin, Weili; Shen, Dinggang.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6326 LNCS 2010. p. 1-10 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6326 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Automatic segmentation of neonatal images using convex optimization and coupled level set method
AU - Wang, Li
AU - Shi, Feng
AU - Gilmore, John H.
AU - Lin, Weili
AU - Shen, Dinggang
PY - 2010/11/9
Y1 - 2010/11/9
N2 - Accurate segmentation of neonatal brain MR images remains challenging mainly due to poor spatial resolution, low tissue contrast, high intensity inhomogeneity. Most existing methods for neonatal brain segmentation are atlas-based and voxel-wise. Although parametric or geometric deformable models have been successfully applied to adult brain segmentation, to the best of our knowledge, they are not explored in neonatal images. In this paper, we propose a novel neonatal image segmentation method, combining local intensity information, atlas spatial prior and cortical thickness constraint, in a level set framework. Besides, we also provide a robust and reliable tissue surfaces initialization for our proposed level set method by using a convex optimization technique. Validation is performed on 10 neonatal brain images with promising results.
AB - Accurate segmentation of neonatal brain MR images remains challenging mainly due to poor spatial resolution, low tissue contrast, high intensity inhomogeneity. Most existing methods for neonatal brain segmentation are atlas-based and voxel-wise. Although parametric or geometric deformable models have been successfully applied to adult brain segmentation, to the best of our knowledge, they are not explored in neonatal images. In this paper, we propose a novel neonatal image segmentation method, combining local intensity information, atlas spatial prior and cortical thickness constraint, in a level set framework. Besides, we also provide a robust and reliable tissue surfaces initialization for our proposed level set method by using a convex optimization technique. Validation is performed on 10 neonatal brain images with promising results.
UR - http://www.scopus.com/inward/record.url?scp=78049449817&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78049449817&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15699-1_1
DO - 10.1007/978-3-642-15699-1_1
M3 - Conference contribution
AN - SCOPUS:78049449817
SN - 3642156983
SN - 9783642156984
VL - 6326 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 10
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -