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- W2895544960 abstract "Active Shape Models (ASM) have been widely used in the literature for the extraction of the tibial and the femoral bones from MRI. These methods use Statistical Shape Models (SSM) to drive the deformation and make the segmentation more robust. One crucial step for building such SSM is the shape correspondence (SC). Several methods have been described in the literature. The goal of this paper is to compare two SC methods, the Iterative Median Closest Point-Gaussian Mixture Model (IMCP-GMM) and the Minimum Description Length (MDL) approaches for the creation of a SSM, and to assess the impact on the accuracy of the femur segmentation in MRI. 28 MRI of the knee have been used. The validation has been performed by using the leave-one-out cross-validation technique. An ASMMDL and an ASMIMCP-GMMM has been built with the SSMs computed respectively with the MDL and IMCP-GMM methods. The computation time for building both SSMs has been also measured. For 90% of data, the error is inferior to 1.78 mm and 1.85 mm fo..." @default.
- W2895544960 created "2018-10-12" @default.
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- W2895544960 date "2018-02-21" @default.
- W2895544960 modified "2023-09-24" @default.
- W2895544960 title "COMPARISON OF TWO STATISTICAL SHAPE MODELS FOR THE FEMUR SEGMENTATION IN MRI BY USING ACTIVE SHAPE MODELS" @default.
- W2895544960 hasPublicationYear "2018" @default.
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