Smart histogram analysis applied to the skull-stripping problem in T1-weighted MRI

Authors:
André G. R. Balan;Agma J. M. Traina;Marcela X. Ribeiro;Paulo M. A. Marques;Caetano Traina Jr.
Affiliations:
Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, Brazil;Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, Brazil;Departamento de Computação, Universidade Federal de São Carlos, Brazil;Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Brazil;Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, Brazil
Venue:
Computers in Biology and Medicine
Year:
2012

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Abstract

In this paper we address the ''skull-stripping'' problem in 3D MR images. We propose a new method that employs an efficient and unique histogram analysis. A fundamental component of this analysis is an algorithm for partitioning a histogram based on the position of the maximum deviation from a Gaussian fit. In our experiments we use a comprehensive image database, including both synthetic and real MRI, and compare our method with other two well-known methods, namely BSE and BET. For all datasets we achieved superior results. Our method is also highly independent of parameter tuning and very robust across considerable variations of noise ratio.