Multi-parameter segmentation of brain images

Atam Dhawan; Brian D'Alessandro

doi:10.1109/NER.2009.5109273

Multi-parameter segmentation of brain images

Atam Dhawan, Brian D'Alessandro

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09
Pages	222-225
Number of pages	4
DOIs	https://doi.org/10.1109/NER.2009.5109273
State	Published - Oct 27 2009
Event	2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09 - Antalya, Turkey Duration: Apr 29 2009 → May 2 2009

Other

Other	2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09
Country/Territory	Turkey
City	Antalya
Period	4/29/09 → 5/2/09

All Science Journal Classification (ASJC) codes

Biomedical Engineering
Clinical Neurology
Neuroscience(all)

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10.1109/NER.2009.5109273

Cite this

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title = "Multi-parameter segmentation of brain images",

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.",

author = "Atam Dhawan and Brian D'Alessandro",

year = "2009",

month = oct,

day = "27",

doi = "10.1109/NER.2009.5109273",

language = "English (US)",

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AB - 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.

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M3 - Conference contribution

AN - SCOPUS:70350241354

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

T2 - 2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09

Y2 - 29 April 2009 through 2 May 2009

ER -

Multi-parameter segmentation of brain images

Abstract

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