Automatic shape model building based on principal geodesic analysis bootstrapping
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Automatic shape model building based on principal geodesic analysis bootstrapping. / Dam, Erik B; Fletcher, P Thomas; Pizer, Stephen M.
In: Medical Image Analysis, Vol. 12, No. 2, 04.2008, p. 136-51.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Automatic shape model building based on principal geodesic analysis bootstrapping
AU - Dam, Erik B
AU - Fletcher, P Thomas
AU - Pizer, Stephen M
PY - 2008/4
Y1 - 2008/4
N2 - We present a novel method for automatic shape model building from a collection of training shapes. The result is a shape model consisting of the mean model and the major modes of variation with a dense correspondence map between individual shapes. The framework consists of iterations where a medial shape representation is deformed into the training shapes followed by computation of the shape mean and modes of shape variation. In the first iteration, a generic shape model is used as starting point - in the following iterations in the bootstrap method, the resulting mean and modes from the previous iteration are used. Thereby, we gradually capture the shape variation in the training collection better and better. Convergence of the method is explicitly enforced. The method is evaluated on collections of artificial training shapes where the expected shape mean and modes of variation are known by design. Furthermore, collections of real prostates and cartilage sheets are used in the evaluation. The evaluation shows that the method is able to capture the training shapes close to the attainable accuracy already in the first iteration. Furthermore, the correspondence properties measured by generality, specificity, and compactness are improved during the shape model building iterations.
AB - We present a novel method for automatic shape model building from a collection of training shapes. The result is a shape model consisting of the mean model and the major modes of variation with a dense correspondence map between individual shapes. The framework consists of iterations where a medial shape representation is deformed into the training shapes followed by computation of the shape mean and modes of shape variation. In the first iteration, a generic shape model is used as starting point - in the following iterations in the bootstrap method, the resulting mean and modes from the previous iteration are used. Thereby, we gradually capture the shape variation in the training collection better and better. Convergence of the method is explicitly enforced. The method is evaluated on collections of artificial training shapes where the expected shape mean and modes of variation are known by design. Furthermore, collections of real prostates and cartilage sheets are used in the evaluation. The evaluation shows that the method is able to capture the training shapes close to the attainable accuracy already in the first iteration. Furthermore, the correspondence properties measured by generality, specificity, and compactness are improved during the shape model building iterations.
KW - Algorithms
KW - Artificial Intelligence
KW - Computer Simulation
KW - Humans
KW - Image Enhancement
KW - Image Interpretation, Computer-Assisted
KW - Imaging, Three-Dimensional
KW - Models, Biological
KW - Pattern Recognition, Automated
KW - Reproducibility of Results
KW - Sensitivity and Specificity
KW - Evaluation Studies
KW - Journal Article
U2 - 10.1016/j.media.2007.08.004
DO - 10.1016/j.media.2007.08.004
M3 - Journal article
C2 - 18178124
VL - 12
SP - 136
EP - 151
JO - Medical Image Analysis
JF - Medical Image Analysis
SN - 1361-8415
IS - 2
ER -
ID: 187548939