Comparison of statistical models performance in case of segmentation using a small amount of training datasets - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles The Visual Computer Year : 2011

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

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 pro- files 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.
Fichier principal
Vignette du fichier
comparison_of_statistical_models_performance_in_case_of_segmentation_using_a_small_amount_of_training_datasets.pdf (1.68 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

inria-00616199 , version 1 (08-07-2013)

Identifiers

Cite

François Chung, Jérôme Schmid, Nadia Magnenat-Thalmann, Hervé Delingette. Comparison of statistical models performance in case of segmentation using a small amount of training datasets. The Visual Computer, 2011, 27 (2), pp.141-151. ⟨10.1007/s00371-010-0536-9⟩. ⟨inria-00616199⟩
129 View
174 Download

Altmetric

Share

Gmail Facebook X LinkedIn More