“School of Cognitive Sciences”
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| Paper IPM / Cognitive Sciences / 11389 |
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| Abstract: | |||||
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We propose a three-dimensional,nonparametric, entropy-based, coupled, multi-shape approach to segment subcortical brain structures from magnetic resonance images (MRI). The proposed method uses PCA to develop shape models that capturestructural variability. It integrates geometrical
relationship between different structures into the algorithm by coupling them (limiting their independent
deformations). On the other hand, to allow variations
among coupled structures, it registers each structure
separately when building the shape models. It defines
an entropy-based energy function which is minimized
using quasi-Newton algorithm. To this end, probability
density functions (pdf) are estimated iteratively using
nonparametric Parzen window method. In the optimization algorithm, analytical derivatives are used to improve speed and accuracy. Quantitative results show the improvement in the segmentation quality due to the integration of the coupling information into the segmentation process.
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