image2
image3
image4
image5
image6
image7
image1

Comparison of statistical models performance in case of segmentation using a small amount of training datasets

Chung, F. and Schmid, J. and Magnenat-Thalmann, N. and and H. Delingette


Abstract: Model-based image segmentation has been extensively used in medical imaging to learn both shape and appearance of anatomical structures from training datasets. The more training datasets are used, the more accurate is the segmented model as we account for more information about its variability. However, training datasets of large size with a proper sampling of the population may not always be available. In this paper, we compare the performance of statistical models in the context of lower limb bones segmentation using MR images when only a small number of datasets is available for training. For shape, both PCA-based priors and shape memory strategies are tested. For appearance, methods based on intensity profiles are tested, namely mean intensity profiles, multivariate Gaussian distributions of profiles and multimodal profiles from EM clustering. Segmentation results show that local and simple methods perform the best when a small number of datasets is available for training. Conversely, statistical methods feature the best segmentation results when the number of training datasets is increased.


@article{553,
  journal = {The Visual Computer },
  author = {Chung, F. and Schmid, J. and Magnenat-Thalmann, N. and and H. Delingette},
  title = {Comparison of statistical models performance in case of segmentation using a small amount of training datasets},
  publisher = {Springer},
  volume = {27},
  number = {2},
  pages = {141-151},
  month = feb,
  year = {2011},
  topic = {Medical}
}