TY - GEN
T1 - Data-driven interactive 3D medical image segmentation based on structured patch model
AU - Park, Sang Hyun
AU - Yun, Il Dong
AU - Lee, Sang Uk
PY - 2013
Y1 - 2013
N2 - In this paper, we present a novel three dimensional interactive medical image segmentation method based on high level knowledge of training set. Since the interactive system should provide intermediate results to an user quickly, insufficient low level models are used for most of previous methods. To exploit the high level knowledge within a short time, we construct a structured patch model that consists of multiple corresponding patch sets. The structured patch model includes the spatial relationships between neighboring patch sets and the prior knowledge of the corresponding patch set on each local region. The spatial relationships accelerate the search of corresponding patch in test time, while the prior knowledge improves the segmentation accuracy. The proposed framework provides not only fast editing tool, but the incremental learning system through adding the segmentation result to the training set. Experiments demonstrate that the proposed method is useful for fast and accurate segmentation of target objects from the multiple medical images.
AB - In this paper, we present a novel three dimensional interactive medical image segmentation method based on high level knowledge of training set. Since the interactive system should provide intermediate results to an user quickly, insufficient low level models are used for most of previous methods. To exploit the high level knowledge within a short time, we construct a structured patch model that consists of multiple corresponding patch sets. The structured patch model includes the spatial relationships between neighboring patch sets and the prior knowledge of the corresponding patch set on each local region. The spatial relationships accelerate the search of corresponding patch in test time, while the prior knowledge improves the segmentation accuracy. The proposed framework provides not only fast editing tool, but the incremental learning system through adding the segmentation result to the training set. Experiments demonstrate that the proposed method is useful for fast and accurate segmentation of target objects from the multiple medical images.
KW - 3D medical image
KW - interactive segmentation
KW - localized classifier
KW - structured patch model
UR - http://www.scopus.com/inward/record.url?scp=84901256363&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-38868-2_17
DO - 10.1007/978-3-642-38868-2_17
M3 - Conference contribution
C2 - 24683969
AN - SCOPUS:84901256363
SN - 9783642388675
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 196
EP - 207
BT - Information Processing in Medical Imaging - 23rd International Conference, IPMI 2013, Proceedings
T2 - 23rd International Conference on Information Processing in Medical Imaging, IPMI 2013
Y2 - 28 June 2013 through 3 July 2013
ER -