A New Adaptive-Weighted Fusion Rule for Wavelet based PET/CT Fusion

다국어 초록 (Multilingual Abstract)

In recent years the Wavelet Transform (WT) had an important role in various applications of signal and image processing. In Image Processing, WT is more useful in many domains like image denoising, feature segmentation, compression, restoration, image fusion, etc. In WT based image fusion, initially the source images are decomposed into approximation and detail coefficients and followed by combining the coefficients using the suitable fusion rules. The resultant fused image is reconstructed by applying inverse WT on the combined coefficients. This paper proposes a new adaptive fusion rule for combining the approximation coefficients of CT and PET images. The Excellency of the proposed fusion rule is stamped by measuring the image information metrics, EOG, SD and ENT on the decomposed approximation coefficients. On the other hand, the detail coefficients are combined using several existing fusion rules. The resultant fused images are quantitatively analyzed using the non-reference image quality, image fusion and error metrics. The analysis declares that the newly proposed fusion rule is more suitable for extracting the complementary information from CT and PET images and also produces the fused image which is rich in content with good contrast and sharpness.

목차 (Table of Contents)

• Abstract
• 1. Introduction
• 2. Overview of Wavelet Transform
• 3. Image Fusion by Wavelet Transform
• 3.1. Fusion Rules
• 3.2. Newly Proposed AWA Fusion Rule
• 4. Experimental Data and Analysis
• 5. Results and Discussions
• 6. Conclusion
• References