DocumentCode
2240921
Title
Detection and delineation of multiple sclerosis lesions in gadolinium-enhanced 3D T1-weighted MRI data
Author
He, Renjie ; Narayana, Ponnada A.
Author_Institution
Dept. of Radiol., Texas Univ., Houston, TX, USA
fYear
2000
fDate
2000
Firstpage
201
Lastpage
205
Abstract
An automatic method for detecting and delineating Gd-enhanced lesions on T1-weighted magnetic resonance images in multiple sclerosis (MS) brains is described. In order to detect and limit the enhancements to the region defined by the brain mask, a combination of thresholding and mathematical morphological operations was implemented. A 3D connected component labeling algorithm is used for producing both the brain mask and labeling the enhanced lesions. False positives that arise from the enhancing vasculature and structures that do not exhibit a blood-brain barrier (BBB) were automatically detected and eliminated by spatially registering the Tl-weighted and the dual-echo affirmative images. Lesion enhancements were delineated using fuzzy connectedness. This technique is evaluated on MS patients with excellent results
Keywords
biomedical MRI; blood; brain; fuzzy logic; gadolinium; image enhancement; image registration; image segmentation; masks; mathematical morphology; medical image processing; 3D connected component labeling algorithm; Gd-enhanced 3D T1-weighted MRI data; blood-brain barrier; brain mask; dual-echo images; enhanced lesions; false positives; fuzzy connectedness; magnetic resonance images; mathematical morphological operations; multiple sclerosis lesion delineation; spatial image registration; thresholding; vasculature; Diseases; Helium; Histograms; Labeling; Lesions; Magnetic resonance imaging; Morphological operations; Multiple sclerosis; Rain; Robustness;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer-Based Medical Systems, 2000. CBMS 2000. Proceedings. 13th IEEE Symposium on
Conference_Location
Houston, TX
ISSN
1063-7125
Print_ISBN
0-7695-0484-1
Type
conf
DOI
10.1109/CBMS.2000.856900
Filename
856900
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