TY - GEN
T1 - A semi-automatic clustering-based level set method for segmentation of endocardium from MSCT images
AU - Su, Qi
AU - Wong, Kwan Yee K.
AU - Fung, George S.K.
PY - 2007/12/1
Y1 - 2007/12/1
N2 - Multi-slice Computed Tomography (MSCT) is an important medical imaging tool that provides dynamic three-dimensional (3D) volume data of the heart for diagnosis of various cardiac diseases. Due to the huge amount of data in MSCT, manual identification, segmentation and tracking of various parts of the heart are very labor intensive and inefficient. In this paper, we introduce a semi-automatic method for robustly segmenting the endocardium surface from cardiac MSCT images. A level set approach is adopted to define a flexible and powerful interface for capturing the complex anatomical structure of the heart. A novel speed function based on clustering the image intensities of the region of interest and the background is proposed for use with the level set method. The method introduced in this paper has the advantages of simple initialization and being capable of segmenting the blood pool with non-homogeneous intensities. Experiments on real data using the proposed speed function have been carried out with 2D, 3D and 4D implementations of the level sets respectively, and comparisons in terms of computational speed and segmentation results are presented.
AB - Multi-slice Computed Tomography (MSCT) is an important medical imaging tool that provides dynamic three-dimensional (3D) volume data of the heart for diagnosis of various cardiac diseases. Due to the huge amount of data in MSCT, manual identification, segmentation and tracking of various parts of the heart are very labor intensive and inefficient. In this paper, we introduce a semi-automatic method for robustly segmenting the endocardium surface from cardiac MSCT images. A level set approach is adopted to define a flexible and powerful interface for capturing the complex anatomical structure of the heart. A novel speed function based on clustering the image intensities of the region of interest and the background is proposed for use with the level set method. The method introduced in this paper has the advantages of simple initialization and being capable of segmenting the blood pool with non-homogeneous intensities. Experiments on real data using the proposed speed function have been carried out with 2D, 3D and 4D implementations of the level sets respectively, and comparisons in terms of computational speed and segmentation results are presented.
UR - http://www.scopus.com/inward/record.url?scp=57649232174&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=57649232174&partnerID=8YFLogxK
U2 - 10.1109/IEMBS.2007.4353721
DO - 10.1109/IEMBS.2007.4353721
M3 - Conference contribution
C2 - 18003387
AN - SCOPUS:57649232174
SN - 1424407885
SN - 9781424407880
T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
SP - 6023
EP - 6026
BT - 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07
T2 - 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07
Y2 - 23 August 2007 through 26 August 2007
ER -