Background:
Lung cancer is one of the common malignant tumors. The successful diagnosis
of lung cancer depends on the accuracy of the image obtained from medical imaging modalities.
Objective:
The fusion of CT and PET is combining the complimentary and redundant information
both images and can increase the ease of perception. Since the existing fusion method sare not perfect
enough, and the fusion effect remains to be improved, the paper proposes a novel method
called adaptive PET/CT fusion for lung cancer in Piella framework.
Methods:
This algorithm firstly adopted the DTCWT to decompose the PET and CT images into
different components, respectively. In accordance with the characteristics of low-frequency and
high-frequency components and the features of PET and CT image, 5 membership functions are
used as a combination method so as to determine the fusion weight for low-frequency components.
In order to fuse different high-frequency components, we select the energy difference of decomposition
coefficients as the match measure, and the local energy as the activity measure; in addition,
the decision factor is also determined for the high-frequency components.
Results:
The proposed method is compared with some of the pixel-level spatial domain image fusion
algorithms. The experimental results show that our proposed algorithm is feasible and effective.
Conclusion:
Our proposed algorithm can better retain and protrude the lesions edge information
and the texture information of lesions in the image fusion.
SSC: A Classifier Combination Method Based on Signal Strength