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Proceedings Paper

Supervised segmentation methods for the hippocampus in MR images
Author(s): Marijn van Stralen; Mirjam I. Geerlings; Koen L. Vincken; Josien P. W. Pluim
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Paper Abstract

This study compares three different types of fully automated supervised methods for segmentation of the hippocampus in MR images. Many of such methods, trained using example data, have been presented for various medical imaging applications, but comparison of the methods is obscured because of optimization for, and evaluation on, different data. We compare three methods based on different methodological bases: atlas-based segmentation (ABS), active appearance model segmentation (AAM) and k-nearest neighbor voxel classification (KNN). All three methods are trained on 100 T1-weighted images with manual segmentations of the right hippocampus, and applied to 103 different images from the same study. Straightforward implementation of each of the three methods resulted in competitive segmentations, both mutually, as compared with methods currently reported in literature. AAM and KNN are favorable in terms of computational costs, requiring only a fraction of the time needed for ABS. The high accuracy and low computational cost make KNN the most favorable method based on this study. AAM achieves similar results as ABS in significantly less computation time. Further improvements might be achieved by fusion of the presented techniques, either methodologically or by direct fusion of the segmentation results.

Paper Details

Date Published: 14 March 2011
PDF: 10 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79622U (14 March 2011); doi: 10.1117/12.878323
Show Author Affiliations
Marijn van Stralen, Univ. Medical Ctr. Utrecht (Netherlands)
Mirjam I. Geerlings, Univ. Medical Ctr. Utrecht (Netherlands)
Koen L. Vincken, Univ. Medical Ctr. Utrecht (Netherlands)
Josien P. W. Pluim, Univ. Medical Ctr. Utrecht (Netherlands)

Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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