Wavelet-based neural network with fuzzy-logic adaptivity for nuclear image restoration

• Published in: Proceedings of the IEEE Vol. 84; no. 10; pp. 1458 - 1473
• Main Authors: Wei Qian, Clarke, L.P.
• Format: Journal Article
• Language: English
• Published: IEEE 01-10-1996
• Subjects: Cameras; Degradation; Electromagnetic scattering; Image restoration; In vivo; Neural networks; Nuclear measurements; Nuclear medicine; Optical collimators; Particle scattering
• ISSN: 0018-9219; 1558-2256
• DOI: 10.1109/5.537111

A novel wavelet-based neural network with fuzzy-logic adaptivity (WNNFA) is proposed for image restoration using a nuclear medicine gamma camera based on the measured system point spread function. The objective is to restore image degradation due to photon scattering and collimator photon penetration with the gamma camera and allow improved quantitative external measurements of radionuclides in vivo. The specific clinical model proposed is the imaging of bremsstrahlung radiation using /sup 32/P and /sup 90/Y. The theoretical basis for four-channel multiresolution wavelet decomposition of the nuclear image into different subimages is developed with the objective of isolating the signal from noise. A fuzzy rule is generated to train a membership function using least mean squares to obtain an optimal balance between image restoration and the stability of the neural network, while maintaining a linear response for the camera to radioactivity dose. A multichannel modified Hopfield neural network architecture is then proposed for multichannel image restoration using the dominant signal subimages.