DocumentCode :
3510076
Title :
A new classifier feature space for an improved Multiple Sclerosis lesion segmentation
Author :
Tomas-Fernandez, X. ; Warfield, Simon K.
Author_Institution :
Dept. of Radiol., Children´´s Hosp. Boston, Boston, MA, USA
fYear :
2011
fDate :
March 30 2011-April 2 2011
Firstpage :
1492
Lastpage :
1495
Abstract :
Intensity based classification relies on contrast between tissue types adjacent in feature space and adequate signal compared to image noise. Contrast between brain tissue types in Multiple Sclerosis patients Magnetic Resonance Imaging is reduced due to the presence of lesions which intensity values overlap with healthy tissue, resulting in tissue misclassification. We propose a new, extended classifier feature space that is based in spatial locations, the intensity of which is abnormal when compared to the expected values in a healthy population in the same location. Segmentation results using our new extended feature space proves an improvement in both sensitivity and specificity in lesion classification.
Keywords :
biological tissues; biomedical MRI; brain; image classification; image segmentation; medical image processing; brain tissue; classifier feature space; contrast; image noise; intensity based classification; lesions; magnetic resonance imaging; multiple sclerosis lesion segmentation; spatial locations; Magnetic Resonance Imaging; Multiple Sclerosis; Segmentation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on
Conference_Location :
Chicago, IL
ISSN :
1945-7928
Print_ISBN :
978-1-4244-4127-3
Electronic_ISBN :
1945-7928
Type :
conf
DOI :
10.1109/ISBI.2011.5872683
Filename :
5872683
Link To Document :
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