A feedback retina model for improving medical images fusion.
Image fusion has become a powerful technique for increasing the interpretation quality of images in medical applications. The diagnostic potential of brain positron emission tomography (PET) imaging is limited by low spatial resolution. For solving this problem, the high-frequency part of the MRI, which would be unrecoverable by the set PET acquisition system, is extracted and added to the PET image. The procedure has the potential of increasing the diagnostic value of a PET image. This paper presents a feedback retina model technique to reduce the spectral distortion and preserve high spatial resolution. Visual and statistical analyses show that the proposed feedback retina model significantly improves the fusion quality compared to non-feedback retina model.
Daneshvar
Sabalan
S
Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
Ghassemian
Hassan
H
D000465:Algorithms; D001921:Brain / Q000473:pathology / Q000503:physiopathology*; D003952:Diagnostic Imaging / Q000379:methods; D005246:Feedback; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D049268:Positron-Emission Tomography / Q000379:methods*; D015203:Reproducibility of Results; D012160:Retina / Q000502:physiology / Q000503:physiopathology*; D012984:Software; D014785:Vision, Ocular