TY - GEN
T1 - Automatic centerline extraction of irregular tubular structures using probability volumes from multiphoton imaging
AU - Santamaría-Pang, A.
AU - Colbert, C. M.
AU - Saggau, P.
AU - Kakadiaris, I. A.
PY - 2007
Y1 - 2007
N2 - In this paper, we present a general framework for extracting 3D centerlines from volumetric datasets. Unlike the majority of previous approaches, we do not require a prior segmentation of the volume nor we do assume any particular tubular shape. Centerline extraction is performed using a morphology-guided level set model. Our approach consists of: i) learning the structural patterns of a tubular-like object, and ii) estimating the centerline of a tubular object as the path with minimal cost with respect to outward flux in gray level images. Such shortest path is found by solving the Eikonal equation. We compare the performance of our method with existing approaches in synthetic, CT, and multiphoton 3D images, obtaining substantial improvements, especially in the case of irregular tubular objects.
AB - In this paper, we present a general framework for extracting 3D centerlines from volumetric datasets. Unlike the majority of previous approaches, we do not require a prior segmentation of the volume nor we do assume any particular tubular shape. Centerline extraction is performed using a morphology-guided level set model. Our approach consists of: i) learning the structural patterns of a tubular-like object, and ii) estimating the centerline of a tubular object as the path with minimal cost with respect to outward flux in gray level images. Such shortest path is found by solving the Eikonal equation. We compare the performance of our method with existing approaches in synthetic, CT, and multiphoton 3D images, obtaining substantial improvements, especially in the case of irregular tubular objects.
UR - http://www.scopus.com/inward/record.url?scp=84883843470&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883843470&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-75759-7_59
DO - 10.1007/978-3-540-75759-7_59
M3 - Conference contribution
C2 - 18044604
AN - SCOPUS:84883843470
SN - 9783540757580
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 486
EP - 494
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007 - 10th International Conference, Proceedings
PB - Springer-Verlag
T2 - 10th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2007
Y2 - 29 October 2007 through 2 November 2007
ER -