Effect of the Number of Coupled Structures on the Segmentation of Brain Structures from MRI

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 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, 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.

کلیدواژه ها:

Image segmentation ، medical image processing ، magnetic resonance imaging (MRI) ، shape modelling ، entropy ، nonparametric