An Efficient Max-Min Rule Selection for Lung Image Fusion using Wavelet Energy Based Fuzzy Logic

Abstract

In this paper, the fusion of lung images using Positron Emission Tomography (PET) and Computed Tomography to identify the cancer part in (CT) images based on Discrete Wavelet Transform (DWT) is presented. The fused lung image is more informative than their individual counterpart and consists of the necessary information to diagnose diseases at the earliest. At first, the PET and CT images are decomposed by DWT, and it produces low and high-frequency subbands. Then Wavelet Energy based Fuzzy Logic (WEFL) is employed to select the MAX-MIN rule for the fusion of low and high-frequency subband coefficients of PET and CT image features to identify cancer parts in lungs. Finally, the fused lung image in low frequency and high-frequency components are combined to reconstruct the image. Results show that the performance of lung image fusion for the identification of the cancer part in CT and PET images.