Anatomical-Functional Image Fusion by Information of Interest in Local Laplacian Filtering Domain

Abstract

A novel method for performing anatomical (MRI)-functional (PET or SPECT) image fusion is presented. The method merges specific feature information from input image signals of a single or multiple medical imaging modalities into a single fused image while preserving more information and generating less distortion. The proposed method uses a local Laplacian filtering based technique realized through a novel multi-scale system architecture. Firstly, the input images are generated in a multi-scale image representation and are processed using local Laplacian filtering. Secondly, at each scale, the decomposed images are combined to produce fused approximate images using a local energy maximum scheme and produce the fused residual images using an information of interest-based scheme. Finally, a fused image is obtained using a reconstruction process that is analogous to that of conventional Laplacian pyramid transform. Experimental results computed using individual multi-scale analysis-based decomposition schemes or fusion rules clearly demonstrate the superiority of the proposed method through subjective observation as well as objective metrics. Furthermore, the proposed method can obtain better performance, compared to the state-of-the-art fusion methods.

Edge Information Based Image Fusion Metrics Using Fractional Order Differentiation and Sigmoidal Functions

“…The robustness of the proposed metrics on the gray-scale and pseudo color medical image fusion is discussed using three recent image fusion methods, namely, information of interest in local Laplacian filtering domain (II-LLF) [62], phase congruency and local Laplacian energy in NSCT domain (PC-LLE-NSCT) [63], and Laplacian re-decomposition (LRD) [64]. In II-LLF, LLF is first applied to source images, then the fused approximate image is obtained using a maximum local energy (MLE) rule whereas the fused residual images are generated by an information of interest based scheme and MLE rule.…”

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“…Simple pixel-level fusion can be treated as a linear summation of pixels from the distinct source images, 16 as shown in equation (1) Figure 1. Overall technical route of CDFO image fusion.…”

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