Supervised segmentation methods for the hippocampus in MR images

M. Stralen, van, M.I. Geerlings, K.L. Vincken, J.P.W. Pluim

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
Original languageEnglish
Title of host publicationMedical Imaging 2011: Image Processing, 14 February 2011 through 16 February 2011, Lake Buena Vista, FL
EditorsB.M. Dawant, D.R. Haynor
Place of PublicationWashington
PublisherSPIE
Pages79622U-1/10
ISBN (Print)9780819485045
DOIs
Publication statusPublished - 2011

Publication series

NameProceedings of SPIE
Volume7962
ISSN (Print)0277-786X

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