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“School of Cognitive Sciences”

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Paper   IPM / Cognitive Sciences / 11388
School of Cognitive Sciences
  Title:   Constrained Optimization of Nonparametric Entropy-Based Segmentation of Brain Structures
1 . A. Akhondi-Asl
2 . Hamid Soltanianzadeh
  Status:   In Proceedings
  Proceeding: Presented at and Published in the Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI?08), Paris, France, May 14-17, 2008
  Year:  2008
  Supported by:  IPM
We propose a constrained, 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 capture structural 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, constraints are used to improve segmentation quality. These constraints are extracted from training data. Sample results are given for the segmentation of caudate, hippocampus, and putamen, illustrating highly superior performance of the proposed method compared to the most similar methods in the literature.

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