期刊
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 60, 期 12, 页码 3347-3353出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2013.2282461
关键词
Artificial neural network; fuzzy logic; image analysis; image fusion (IF); medical imaging (MI)
资金
- Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India
This paper addresses a novel approach to the multimodal medical image fusion (MIF) problem, employing multiscale geometric analysis of the nonsubsampled contourlet transform and fuzzy-adaptive reduced pulse-coupled neural network (RPCNN). The linking strengths of the RPCNNs' neurons are adaptively set by modeling them as the fuzzy membership values, representing their significance in the corresponding source image. Use of the RPCNN with a less complex structure and having less number of parameters leads to computational efficiency-an important requirement of point-of-care health care technologies. The proposed scheme is free from the common shortcomings of the state-of-the-art MIF techniques: contrast reduction, loss of image fine details, and unwanted image degradations, etc. Subjective and objective evaluations show better performance of this new approach compared to the existing techniques.
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