Nonlocal Patch-Based Label Fusion for Hippocampus Segmentation
- 2.6k Downloads
Quantitative magnetic resonance analysis often requires accurate, robust and reliable automatic extraction of anatomical structures. Recently, template-warping methods incorporating a label fusion strategy have demonstrated high accuracy in segmenting cerebral structures. In this study, we propose a novel patch-based method using expert segmentation priors to achieve this task. Inspired by recent work in image denoising, the proposed nonlocal patch-based label fusion produces accurate and robust segmentation. During our experiments, the hippocampi of 80 healthy subjects were segmented. The influence on segmentation accuracy of different parameters such as patch size or number of training subjects was also studied. Moreover, a comparison with an appearance-based method and a template-based method was carried out. The highest median kappa value obtained with the proposed method was 0.884, which is competitive compared with recently published methods.
KeywordsPatch Size Segmentation Accuracy Training Subject Nonlinear Registration Stereotaxic Space
- 12.Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 60–65 (2005)Google Scholar
- 16.Nyul, L.G., Udupa, J.K.: Standardizing the MR image intensity scales: making MR intensities have tissue specific meaning. Medical Imaging 2000: Image Display and Visualization 1(21), 496–504 (2000)Google Scholar