Comparisons on segmentation of brain MR image

Author

Yang, Chunlan ; Wu, Shuicai ; Bai, Yanping ; Gao, Hongjian

Author_Institution

Coll. of Life Sci. & Bioeng., Beijing Univ. of Technol., Beijing, China

fYear

2009

fDate

16-19 Aug. 2009

Abstract

Image segmentation is a focused issue in image processing. Especially, brain segmentation is a key problem in neuroscience. In this study, our aim is to segment the real MR image into gray matter, white matter and cerebrospinal fluid. Several methods were compared. However, traditional methods such as fuzzy c-means, mixture Gaussian model can´t achieve a satisfied result successfully. Markov random field (MRF) model is used and the experimental results show that MRF method is robust to noise which can achieves a perfect segmentation.

Keywords

Markov processes; biomedical MRI; brain; image segmentation; medical image processing; neurophysiology; Gaussian mixture model comparison; MRF model; Markov random field model; brain MR image segmentation; cerebrospinal fluid; fuzzy c-means method comparison; gray matter; magnetic resonance imaging; neuroscience; white matter; Biomedical engineering; Brain modeling; Educational institutions; Focusing; Image processing; Image segmentation; Instruments; Markov random fields; Neuroscience; Noise robustness; brain MR image; fuzzy C-means; markov random field; mixture Gaussian model; segmentation;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronic Measurement & Instruments, 2009. ICEMI '09. 9th International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-3863-1

Electronic_ISBN

978-1-4244-3864-8

Type

conf

DOI

10.1109/ICEMI.2009.5274127

Filename

5274127