Two-Scale Multimodal Medical Image Fusion Based on Guided Filtering and Sparse Representation

Abstract

Medical image fusion techniques primarily integrate the complementary features of different medical images to acquire a single composite image with superior quality, reducing the uncertainty of lesion analysis. However, the simultaneous extraction of more salient features and less meaningless details from medical images by using multi-scale transform methods is a challenging task. This study presents a two-scale fusion framework for multimodal medical images to overcome the aforementioned limitation. In this framework, a guided filter is used to decompose source images into the base and detail layers to roughly separate the two characteristics of source images, namely, structural information and texture details. To effectively preserve most of the structural information, the base layers are fused using the combined Laplacian pyramid and sparse representation rule, in which an image patch selection-based dictionary construction scheme is introduced to exclude the meaningless patches from the source images and enhance the sparse representation capability of the pyramid-decomposed low-frequency layer. The detail layers are subsequently merged using a guided filtering-based approach, which enhances contrast level via noise filtering as much as possible. The fused base and detail layers are reconstructed to generate the fused image. We experimentally verify the superiority of the proposed method by using two basic fusion schemes and conducting comparison experiments on nine pairs of medical images from diverse modalities. The comparison of the fused results in terms of visual effect and objective assessment demonstrates that the proposed method provides better visual effect with an improved objective measurement because it effectively preserves meaningful salient features without producing abnormal details. INDEX TERMS Medical image fusion, guided filtering, sparse representation, salient features, meaningless details.

A Robust Medical Image Fusion Based on Synthetic Focusing Degree Criterion and Special Kernel Set for Clinical Diagnosis

“…The formation of synthetic images in a domain that has been changed by particular rules is the second phase. The max fusion principle (Li et al, 2021a), an average fusion principle (Pei et al, 2020), local gradient energy (Fu et al, 2020) and MSMG-WLE (Zhang et al, 2022) are a few examples of fusion principles that are introduced respectively. To generate a composite image, the elements generated through the transform domain are finally transferred to the spatial domain.…”

MRI Image Fusion Based on Sparse Representation with Measurement of Patch-Based Multiple Salient Features

“…For the second distinction, the MSTbased methods are sensitive to noise and misregistration with large decomposition level settings, while the SR-based methods with overlapping patch-wise modes are robust for misregistration, which guarantees the accuracy of the spatial location of tissues. Therefore, a wide range of research on SR-based medical image fusion has been conducted in recent years [29][30][31].…”

MRI Image Fusion Based on Sparse Representation with Patch-based Multiple Salient Features Measurement

“…For the latter distinction, the MST-based methods are sensitive to noise and misregistration with large decomposition level settings, while the SR-based methods with overlapping patch-wise mode are robust to mis-registration, and this guarantees the accuracy of spatial location of tissues. Therefore, a widely range of research on SR-based medical image fusion is attracted in recent years [25]- [27].…”