TY - GEN
T1 - A learning framework for the automatic and accurate segmentation of cardiac tagged MRI images
AU - Qian, Zhen
AU - Metaxas, Dimitris N.
AU - Axel, Leon
PY - 2005
Y1 - 2005
N2 - In this paper we present a fully automatic and accurate segmentation framework for 2D tagged cardiac MR images. This scheme consists of three learning methods: a) an active shape model is implemented to model the heart shape variations, b) an Adaboost learning method is applied to learn confidence-rated boundary criterions from the local appearance features at each landmark point on the shape model, and c) an Adaboost detection technique is used to initialize the segmentation. The set of boundary statistics learned by Adaboost is the weighted combination of all the useful appearance features, and results in more reliable and accurate image forces compared to using only edge or region information. Our experimental results show that given similar imaging techniques, our method can achieve a highly accurate performance without any human interaction.
AB - In this paper we present a fully automatic and accurate segmentation framework for 2D tagged cardiac MR images. This scheme consists of three learning methods: a) an active shape model is implemented to model the heart shape variations, b) an Adaboost learning method is applied to learn confidence-rated boundary criterions from the local appearance features at each landmark point on the shape model, and c) an Adaboost detection technique is used to initialize the segmentation. The set of boundary statistics learned by Adaboost is the weighted combination of all the useful appearance features, and results in more reliable and accurate image forces compared to using only edge or region information. Our experimental results show that given similar imaging techniques, our method can achieve a highly accurate performance without any human interaction.
UR - https://www.scopus.com/pages/publications/33646710829
UR - https://www.scopus.com/pages/publications/33646710829#tab=citedBy
U2 - 10.1007/11569541_11
DO - 10.1007/11569541_11
M3 - Conference contribution
SN - 3540294112
SN - 9783540294115
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 93
EP - 102
BT - Computer Vision for Biomedical Image Applications - First International Workshop, CVBIA 2005, Proceedings
T2 - 1st International Workshop on Computer Vision for Biomedical Image Applications, CVBIA 2005
Y2 - 21 October 2005 through 21 October 2005
ER -