Delineation and quantitation of brain lesions by fuzzy clustering in positron emission tomography.
In this study, we investigate the application of the fuzzy clustering to the anatomical localization and quantitation of brain lesions in Positron Emission Tomography (PET) images. The method is based on the Fuzzy C-Means (FCM) algorithm. The algorithm segments the PET image data points into a given number of clusters. Each cluster is an homogeneous region of the brain (e.g. tumor). A feature vector is assigned to a cluster which has the highest membership degree. Having the label affected by the FCM algorithm to a cluster, one may easily compute the corresponding spatial localization, area and perimeter. Studies concerning the evolution of a tumor after different treatments in two patients are presented.
Laboratoire de Biophysique, Faculté de Médecine Alexis Carrel, Lyon, France. boudra@cimac-res.univ-lyonl.fr
A E
AE
Boudraa
J
J
Champier
L
L
Cinotti
J C
JC
Bordet
F
F
Lavenne
J J
JJ
Mallet
D000465:Algorithms; D001932:Brain Neoplasms / Q000000981:diagnostic imaging* / Q000378:metabolism / Q000628:therapy; D016000:Cluster Analysis; D003131:Combined Modality Therapy; D003847:Deoxyglucose / Q000378:metabolism; D005462:Fluorine Radioisotopes; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D014055:Tomography, Emission-Computed
D005462:Fluorine Radioisotopes; D003847:Deoxyglucose