Neuro-fuzzy systems for computer-aided myocardial viability assessment.
This paper describes a multimodality framework for computer-aided myocardial viability assessment based on neuro-fuzzy techniques. The proposed approach distinguishes two main levels: the modality-independent inference level and the modality-dependent application level. This two-level distinction releases the hard constraint of multimodality image registration. An abstract description template is used to describe the different myocardial functions (contractile function, perfusion, metabolism). Parameters extracted from different image modalities are combined to derive a diagnostic image. The neuro-fuzzy techniques make our system transparent, adaptive and easily extendable. Its effectiveness and robustness are demonstrated in a positron emission tomography/magnetic resonance imaging data fusion application.
Behloul
F
F
Department of Radiology, Leiden University Medical Center, The Netherlands.
Lelieveldt
B P
BP
Boudraa
A
A
Janier
M F
MF
Revel
D
D
Reiber
J H
JH
D001185:Artificial Intelligence; D002470:Cell Survival / Q000502:physiology; D019788:Fluorodeoxyglucose F18; D017143:Fuzzy Logic; D006321:Heart / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D009203:Myocardial Infarction / Q000175:diagnosis* / Q000401:mortality / Q000503:physiopathology; D009206:Myocardium / Q000378:metabolism; D016571:Neural Networks, Computer; D014055:Tomography, Emission-Computed / Q000379:methods*
D019788:Fluorodeoxyglucose F18