Unsupervised measurement of brain tumor volume on MR images.
We examined unsupervised methods of segmentation of MR images of the brain for measuring tumor volume in response to treatment. Two clustering methods were used: fuzzy c-means and a nonfuzzy clustering algorithm. Results were compared with volume segmentations by two supervised methods, k-nearest neighbors and region growing, and all results were compared with manual labelings. Results of individual segmentations are presented as well as comparisons on the application of the different methods with 10 data sets of patients with brain tumors. Unsupervised segmentation is preferred for measuring tumor volumes in response to treatment, as it eliminates operator dependency and may be adequate for delineation of the target volume in radiation therapy. Some obstacles need to be overcome, in particular regarding the detection of anatomically relevant tissue classes. This study shows that these improvements are possible.
Velthuizen
R P
RP
Department of Radiology, University of South Florida, Tampa 33624, USA.
Clarke
L P
LP
Phuphanich
S
S
Hall
L O
LO
Bensaid
A M
AM
Arrington
J A
JA
Greenberg
H M
HM
Silbiger
M L
ML
D000328:Adult; D000368:Aged; D000465:Algorithms; D001932:Brain Neoplasms / Q000175:diagnosis / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods; D008297:Male; D008577:Meningeal Neoplasms / Q000175:diagnosis / Q000473:pathology* / Q000532:radiotherapy; D008579:Meningioma / Q000175:diagnosis / Q000473:pathology* / Q000532:radiotherapy; D008875:Middle Aged; D008962:Models, Theoretical; D011856:Radiographic Image Enhancement