DocumentCode :
3513026
Title :
Hippocampus segmentation using a stable maximum likelihood classifier ensemble algorithm
Author :
Wang, Hongzhi ; Suh, Jung Wook ; Das, Sandhitsu ; Altinay, Murat ; Pluta, John ; Yushkevich, Paul
Author_Institution :
Depts. of Radiol., Univ. of Pennsylvania, Philadelphia, PA, USA
fYear :
2011
fDate :
March 30 2011-April 2 2011
Firstpage :
2036
Lastpage :
2040
Abstract :
We develop a new algorithm to segment the hippocampus from MR images. Our method uses a new classifier ensemble algorithm to correct segmentation errors produced by a multi-atlas based segmentation method. Our classifier ensemble algorithm searches for the maximum likelihood solution via gradient ascent optimization. Compared to the additive regression based algorithm, LogitBoost, our algorithm avoids the numerical instability problem. Experiments on a hippocampus segmentation problem using the ADNI data show that our algorithm consistently converges faster and generalizes better than AdaBoost.
Keywords :
biomedical MRI; brain; image classification; image segmentation; maximum likelihood estimation; medical image processing; optimisation; regression analysis; ADNI data; LogitBoost; MR images; additive regression; gradient ascent optimization; hippocampus segmentation; multiatlas based segmentation method; numerical instability problem; stable maximum likelihood classifier ensemble algorithm; Additives; Error correction; Hippocampus; Image segmentation; Joints; Testing; Training; AdaBoost; LogitBoost; classifier ensemble; hippocampus segmentation; maximum likelihood;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on
Conference_Location :
Chicago, IL
ISSN :
1945-7928
Print_ISBN :
978-1-4244-4127-3
Electronic_ISBN :
1945-7928
Type :
conf
DOI :
10.1109/ISBI.2011.5872812
Filename :
5872812
Link To Document :
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