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Automatic or interactive segmentation tools for 3D medical images have been developed to help the clinicians. Atlas-based methods are one of the most usual techniques to localized anatomical structures. They have shown to be efficient with various types of medical images and various types of organs. However, a registration step is needed to perform an atlas-based segmentation which can be very time consuming. Local atlases coupled with spatial relationships have been proposed to solve this issue. Local atlases are defined on a sub-part of the image enabling a fast registration step. The positioning of these local atlases on the whole image can be done automatically with learned spatial relationships or interactively by a user when the automatic positioning is not well performed. In this article, different classification methods possibly included in local atlases segmentation methods are compared. Human brain and sheep brain MRI images have been used as databases for the experiments. Depending on the choice of the method, segmentation quality and computation time are very different. Graph-cut or CNN segmentation methods have shown to be more suitable for interactive segmentation because of their low computation time. Multi-atlas based methods like local weighted majority voting are more suitable for automatic process.

A Comparative Study on Voxel Classification Methods for Atlas based Segmentation of Brain Structures from 3D MRI Images