TY - GEN
T1 - Cardiac image segmentation using generalized polynomial chaos expansion and level set function
AU - Du, Yuncheng
AU - Du, Dongping
N1 - Funding Information:
This work was supported by the National Science Foundation (CMMI – 1646664) Y. Du is with the Department of Chemical and Biomolecular Engineering, Clarkson University, Potsdam, NY 13699 USA (e-mail: ydu@clarkson.edu ).
Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/13
Y1 - 2017/9/13
N2 - Cardiovascular Magnetic Resonance (CMR) images involves a great amount of uncertainties. Such uncertainties may originate from either intrinsic measurement limitations or heterogeneities among patients. Without properly considering these uncertainties, image analysis may provide inaccurate estimations of cardiac functions, and ultimately lead to false diagnosis and inappropriate treatment strategy. In this work, a stochastic image segmentation algorithm is developed to separate cardiac chambers from the background of CMR images. To account for noise and uncertainties in pixel values, a generalized polynomial chaos (gPC) expansion is integrated with a level set function to dynamically evolve boundaries of cardiac chambers. Two consecutive steps are developed: a deterministic segmentation to identify an immediate neighborhood of boundary, of which pixel values are used to calibrate the gPC model; and a stochastic segmentation applied to the neighborhood region to evolve boundaries of cardiac chambers in a stochastic manner. The proposed method can provide a probabilistic description of the segmented heart boundary, which will greatly improve the reliability of image analysis, and potentially enhanced cardiac function evaluation.
AB - Cardiovascular Magnetic Resonance (CMR) images involves a great amount of uncertainties. Such uncertainties may originate from either intrinsic measurement limitations or heterogeneities among patients. Without properly considering these uncertainties, image analysis may provide inaccurate estimations of cardiac functions, and ultimately lead to false diagnosis and inappropriate treatment strategy. In this work, a stochastic image segmentation algorithm is developed to separate cardiac chambers from the background of CMR images. To account for noise and uncertainties in pixel values, a generalized polynomial chaos (gPC) expansion is integrated with a level set function to dynamically evolve boundaries of cardiac chambers. Two consecutive steps are developed: a deterministic segmentation to identify an immediate neighborhood of boundary, of which pixel values are used to calibrate the gPC model; and a stochastic segmentation applied to the neighborhood region to evolve boundaries of cardiac chambers in a stochastic manner. The proposed method can provide a probabilistic description of the segmented heart boundary, which will greatly improve the reliability of image analysis, and potentially enhanced cardiac function evaluation.
UR - http://www.scopus.com/inward/record.url?scp=85032203610&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2017.8036909
DO - 10.1109/EMBC.2017.8036909
M3 - Conference contribution
C2 - 29059957
AN - SCOPUS:85032203610
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 652
EP - 655
BT - 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Y2 - 11 July 2017 through 15 July 2017
ER -