Clinical dementia rating score prediction based on MR segmentation

Author

Seixas, Flavio L. ; de Souza, A.S. ; Plastino, A. ; Saade, D. C M ; Conci, A.

Author_Institution

Comput. Sci. Dept., Univ. Fed. Fluminense, Niteroi

fYear

2008

fDate

3-5 Nov. 2008

Firstpage

113

Lastpage

114

Abstract

This work aims at predicting the clinical dementia rating (CDR) score with a fully automated human brain volumetric segmentation method based on anatomical atlas using magnetic resonance (MR) images. The CDR prediction method uses a Bayesian classifier considering 371 individuals. Practical results were assessed using the classifier true-positive rate. CDR score prediction can indicate an underlying neurodegenerative disorder, such as Alzheimerpsilas disease. Its early detection allows precocious therapeutic intervention and better clinical results.

Keywords

Bayes methods; biomedical MRI; brain; data mining; diseases; image classification; image segmentation; medical image processing; neurophysiology; Bayesian classifier; MR segmentation; MRI; anatomical atlas; automated human brain volumetric segmentation method; automatic attribute selection method; clinical dementia rating score prediction; computer-aided diagnosis application; data mining classification method; magnetic resonance image; neurodegenerative disorder; precocious therapeutic intervention; Alzheimer´s disease; Anatomical structure; Bayesian methods; Brain modeling; Computer science; Dementia; Image segmentation; Magnetic resonance; Performance evaluation; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomeidcine Workshops, 2008. BIBMW 2008. IEEE International Conference on

Conference_Location

Philadelphia, PA

Print_ISBN

978-1-4244-2890-8

Type

conf

DOI

10.1109/BIBMW.2008.4686219

Filename

4686219