DocumentCode :
3517719
Title :
A novel model for inhomogeneous brain MR image segmentation
Author :
Gao, Shangbing ; Yang, Jian
Author_Institution :
Sch. of Comput. Sci. & Technol., Nanjing Univ. of Sci. & Technol., Nanjing, China
fYear :
2011
fDate :
28-28 Nov. 2011
Firstpage :
214
Lastpage :
218
Abstract :
Brain magnetic resonance (MR) images are significant for brain studies because of their excellent contrast of soft tissues, non invasive characteristic and a high spatial resolution. However, Intensity inhomogeneity is an undesired phenomenon that represents the main obstacle for brain MR image segmentation. In this paper, we propose a novel model which can overcome the intensity inhomogeneity problem of Brain MR images without the bias field correction. In this model, a simple and effective initialization method is taken to speed up the curve evolution toward final results; a new multiphase level set method is proposed to segment the brain tissues. This model not only extracts brain white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) simultaneously, but also provides smooth and accurate boundaries or surfaces of the object. We test our model on two-dimensional and three-dimensional brain MR images and obtain the satisfactory segmentation results. Furthermore, we also use our method to segment nature images and get the ideal results. Experimental results show that our method outperforms the state-of-art methods, yielding higher Tanimoto coefficient.
Keywords :
biomedical MRI; brain; image segmentation; medical image processing; brain magnetic resonance images; brain white matter; cerebrospinal fluid; curve evolution; gray matter; high spatial resolution; image segmentation; inhomogeneous brain MR image segmentation; intensity inhomogeneity problem; noninvasive characteristic; Brain modeling; Fitting; Image segmentation; Level set; Mathematical model; Nonhomogeneous media; Chan-Vese(CV) model; active contour model; intensity inhomogeneity; local binary fitting(LBF) model; magnetic resonance image;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ACPR), 2011 First Asian Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4577-0122-1
Type :
conf
DOI :
10.1109/ACPR.2011.6166531
Filename :
6166531
Link To Document :
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