Title :
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
fDate :
April 29 2009-May 2 2009
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;
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
DOI :
10.1109/NER.2009.5109273
