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
Y1 - 2010
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
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
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 10
BT - Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings
T2 - 5th International Workshop on Medical Imaging and Augmented Reality, MIAR 2010
Y2 - 19 September 2010 through 20 September 2010
ER -