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

Journal Article · Tue Oct 01 04:00:00 EDT 1996 · Proceedings of the IEEE

DOI:https://doi.org/10.1109/5.537111· OSTI ID:398154

Qian, W; Clarke, L P ^[1]

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1. Univ. of South Florida, Tampa, FL (United States)

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 because of the enhanced image degradation effects of photon scattering, photon penetration and poor signal-to-noise ratio (SNR) in measurements of this type with the gamma camera. 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 (LMS) to obtain an optimal balance between image restoration and the stability of the neutral network (NN), while maintaining a linear response for the camera to radioactivity dose. A multichannel modified Hopfield neural network (HNN) architecture is then proposed for multichannel image restoration using the dominant signal subimages. This algorithm model avoids the common inverse problem associated with other image restoration filters such as the Wiener filter. The relative performance of the WNNFA for image restoration is compared to a previously reported order statistic neural network hybrid (OSNNH) filter by these investigators and a traditional Weiner filter and a modified HNN using simulated degraded images with different noise levels. Quantitative metrics such as the normalized mean square error (NMSE) and SNR are used to compare filter performance.

• OSTI ID: 398154
• Journal Information: Proceedings of the IEEE, Journal Name: Proceedings of the IEEE Journal Issue: 10 Vol. 84; ISSN IEEPAD; ISSN 0018-9219
• Country of Publication: United States
• Language: English

Related Subjects

55 BIOLOGY AND MEDICINE
BASIC STUDIES
FUZZY LOGIC
GAMMA CAMERAS
IMAGE PROCESSING
NEURAL NETWORKS
OPTIMIZATION
PERFORMANCE
PHOSPHORUS 32
W CODES
YTTRIUM 90