Multi-parameter segmentation of brain images

Author

Dhawan, Atam P. ; D´Alessandro, Brian

Author_Institution

Dept. of Electr. & Comput. Eng., New Jersey Inst. of Technol., Newark, NJ, USA

fYear

2009

fDate

April 29 2009-May 2 2009

Firstpage

222

Lastpage

225

Abstract

Recent advances in multi-parameter MR brain imaging has enabled multi-class tissue characterization for better quantitative analysis and understanding brain disorders and pathologies. This paper presents a maximum likelihood based method for multi-class segmentation that utilizes spatio-frequency features obtained from wavelet analysis along with the multi-parameter measurements. Results on MR brain images of a patient with stroke are presented.

Keywords

biological tissues; biomedical MRI; brain; feature extraction; image segmentation; maximum likelihood estimation; medical disorders; medical image processing; MR brain imaging; brain disorders; maximum likelihood-based method; multiparameter image segmentation; quantitative analysis; spatio-frequency feature extraction; stroke; tissue characterization; wavelet analysis; Brain; Image analysis; Image segmentation; Magnetic resonance imaging; Maximum likelihood detection; Neural engineering; Pathology; Pixel; USA Councils; Wavelet analysis; Multi-parameter segmentation; brain image analysis; tissue characterization;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Engineering, 2009. NER '09. 4th International IEEE/EMBS Conference on

Conference_Location

Antalya

Print_ISBN

978-1-4244-2072-8

Electronic_ISBN

978-1-4244-2073-5

Type

conf

DOI

10.1109/NER.2009.5109273

Filename

5109273