TY - JOUR
T1 - A joint shape evolution approach to medical image segmentation using expectation-maximization algorithm
AU - Farzinfar, Mahshid
AU - Teoh, Eam Khwang
AU - Xue, Zhong
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2011/11
Y1 - 2011/11
N2 - This study proposes an expectation-maximization (EM)-based curve evolution algorithm for segmentation of magnetic resonance brain images. In the proposed algorithm, the evolution curve is constrained not only by a shape-based statistical model but also by a hidden variable model from image observation. The hidden variable model herein is defined by the local voxel labeling, which is unknown and estimated by the expected likelihood function derived from the image data and prior anatomical knowledge. In the M-step, the shapes of the structures are estimated jointly by encoding the hidden variable model and the statistical prior model obtained from the training stage. In the E-step, the expected observation likelihood and the prior distribution of the hidden variables are estimated. In experiments, the proposed automatic segmentation algorithm is applied to multiple gray nuclei structures such as caudate, putamens and thalamus of three-dimensional magnetic resonance imaging in volunteers and patients. As for the robustness and accuracy of the segmentation algorithm, the results of the proposed EM-joint shape-based algorithm outperformed those obtained using the statistical shape model-based techniques in the same framework and a current state-of-the-art region competition level set method.
AB - This study proposes an expectation-maximization (EM)-based curve evolution algorithm for segmentation of magnetic resonance brain images. In the proposed algorithm, the evolution curve is constrained not only by a shape-based statistical model but also by a hidden variable model from image observation. The hidden variable model herein is defined by the local voxel labeling, which is unknown and estimated by the expected likelihood function derived from the image data and prior anatomical knowledge. In the M-step, the shapes of the structures are estimated jointly by encoding the hidden variable model and the statistical prior model obtained from the training stage. In the E-step, the expected observation likelihood and the prior distribution of the hidden variables are estimated. In experiments, the proposed automatic segmentation algorithm is applied to multiple gray nuclei structures such as caudate, putamens and thalamus of three-dimensional magnetic resonance imaging in volunteers and patients. As for the robustness and accuracy of the segmentation algorithm, the results of the proposed EM-joint shape-based algorithm outperformed those obtained using the statistical shape model-based techniques in the same framework and a current state-of-the-art region competition level set method.
KW - Active contours
KW - Expectation-maximization algorithm
KW - Level set methods
KW - Statistical shape model
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U2 - 10.1016/j.mri.2011.07.005
DO - 10.1016/j.mri.2011.07.005
M3 - Article
C2 - 21873011
AN - SCOPUS:80054783869
VL - 29
SP - 1255
EP - 1266
JO - Magnetic Resonance Imaging
JF - Magnetic Resonance Imaging
SN - 0730-725X
IS - 9
ER -