Abstract
Liver is considered as a tissue with large shape variations. Representation of such a complex shape cannot be accurately performed using conventional SSM algorithms. We propose to decompose a human liver into its anatomical parts and use them as a guide when considering point correspondences. To cope with shape complexity, a modified coherent point drift (CPD) algorithm is proposed too. The modified CPD algorithm assigns fuzzy correspondences to points and follows a simulated annealing approach to convert fuzzy correspondences into binary ones. Our modification includes automatic parameter settings which results in robustness of the algorithm. The proposed algorithm was compared to the thin plate spline-robust point matching (TPS-RPM) and minimum description length (MDL) techniques. Our method is twice faster than the MDL algorithm. Compared to the TPS-RPM algorithm, our method improved mean Specificity of the right lobe by 0.09 and Compactness of the model by two less modes.
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