@inproceedings{6a6e0fb42e8f4e67bafcf28b809fb219,
title = "Prostate MR image segmentation using 3D active appearance models",
abstract = "This paper presents a method for automatic segmentation of the prostate from transversal T2-weighted images based on 3D Active Appearance Models (AAM). The algorithm consist of two stages. Firstly, Shape Context based non-rigid surface registration of the manual segmented images is used to obtain the point correspondence between the given training cases. Subsequently, an AAM is used to segment the prostate on 50 training cases. The method is evaluated using a 5-fold cross validation over 5 repetitions. The mean Dice similarity coefficient and 95% Hausdorff distance are 0.78 and 7.32 mm respectively.",
keywords = "METIS-289733, Active Appearance Model, EWI-22366, Shape Context, Prostate segmentation, IR-83366",
author = "Bianca Maan and {van der Heijden}, Ferdinand",
note = "Grand Challenges in Medical Image Analysis ; PRostate MR Image SEgmentation, PROMISE 2012 ; Conference date: 01-10-2012 Through 01-10-2012",
year = "2012",
month = oct,
day = "1",
language = "Undefined",
isbn = "not assigned",
series = "Grand Challenges in Medical Image Analysis",
publisher = "PROMISE12",
pages = "44--51",
booktitle = "PROMISE 12 : Miccai 2012 Grand Challenge onProstate MR Image SEgmentation",
}