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

Author(s)

Balan, Andre G. R. ^[1] ; Traina, Agma J. M. ^[2] ; Ribeiro, Marcela X. ^[3] ; Marques, Paulo M. A. ^[4] ; Traina, Jr., Caetano ^[2]

Affiliation

^[1] Univ Fed ABC, Ctr Matemat Comp & Cognicao, Santo Andre, SP - Brazil

^[2] Univ Sao Paulo, Inst Ciencias Matemat & Comp, BR-05508 Sao Paulo - Brazil

^[3] Univ Fed Sao Carlos, Dept Comp, BR-13560 Sao Carlos, SP - Brazil

^[4] Univ Sao Paulo, Fac Med Ribeirao Preto, BR-05508 Sao Paulo - Brazil

• Document type: Journal article
• Source: COMPUTERS IN BIOLOGY AND MEDICINE; v. 42, n. 5, p. 509-522, MAY 2012.
• Web of Science Citations: 3

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. (C) 2012 Elsevier Ltd. All rights reserved. (AU)

• FAPESP's process: 07/50285-0 - Development of techniques for segmentation and high level feature extraction of medical imaging for content-based image retrieval systems
• Grantee: André Guilherme Ribeiro Balan
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 05/04272-9 - MIRVisIM: mining, indexing, retrieval and data visualization in picture archiving and communication systems
• Grantee: Agma Juci Machado Traina
• Support Opportunities: Regular Research Grants