Abstract: There are four construction methods of fusion rules in Piella framework, according to the
first method, a pixel-level fusion algorithm of PET/CT based on non-subsampled contourlet transform
(NSCT) and compressed sensing is proposed. Firstly, NSCT is used to decompose the registered PET
and CT image. Secondly, in order to highlight the focal region of the low frequency image, the low
frequency components are fused by pulse coupled neural network (PCNN) which more high
sensitivity to the characteristics and regions. Thirdly, in high frequency, the Gauss random matrix is used
for compression measurement, in sub-block of high frequency, histogram distance is utilized in match measure, the regional energy is regarded as the activity measure. The fusion factor ‘d’ is calculated by
adaptive decision model using matching measure and activity measure. According to the fusion factor, the
high frequency measurement values are fused, and the high frequency fusion image are reconstructed by
using the orthogonal matching pursuit algorithm. Fourthly, the final fusing images are acquired through
the NSCT inverse transformation of low frequency fusion image and high frequency fusion image. Finally,
three experiments are done, comparison experiment with other image fusion algorithms, comparison with
different activity measures and different matching measures. The experimental results shown that the
algorithm can retain and show the edge and texture information of focus better. And the algorithm are
superior to other fusion algorithms from subjective and objective evaluation.

Key words: non-subsampled contourlet transform, pulse coupled neural network, compressed sensing, framework of Piella