A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics

The advancements in automated diagnostic tools allow researchers to obtain more and more information from medical images. Recently, to obtain more informative medical images, multi-modality images have been used. These images have significantly more information as compared to traditional medical images. However, the construction of multi-modality images is not an easy task. The proposed approach, initially, decomposes the image into sub-bands using a non-subsampled contourlet transform (NSCT) domain. Thereafter, an extreme version of the Inception (Xception) is used for feature extraction of the source images. The multi-objective differential evolution is used to select the optimal features. Thereafter, the coefficient of determination and the energy loss based fusion functions are used to obtain the fused coefficients. Finally, the fused image is computed by applying the inverse NSCT. Extensive experimental results show that the proposed approach outperforms the competitive multi-modality image fusion approaches.

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Multi-modality medical image fusion technique using multi-objective differential evolution based deep neural networks.

A novel fusion method based on dynamic threshold neural P systems and nonsubsampled contourlet transform for multi-modality medical images

Combining Gabor energy with equilibrium optimizer algorithm for multi-modality medical image fusion

Multimodal medical image fusion has been attracting the attention of researchers in recent years because it supports doctors in enhancing clinical diagnosis. Improving the quality of fused images and keeping important information from the input images remain a significant challenge for the current algorithms. Therefore, an effective approach for medical image fusion is introduced in this paper to address the challenges posed above. Initially, the three-scale image decomposition method using the weighted mean curvature filter and two-scale image decomposition is introduced to decompose the input images into high- and low-frequency coefficients. Subsequently, an efficient fusion rule for high-frequency components using maximum Gabor energy is proposed. This rule allows the preservation of important information in the composite image. Ultimately, the low-frequency components are synthesized using the optimal parameters from the Marine predators algorithm. This fusion rule is designed to ensure a good quality output image. A total of 120 pairs of medical images are tested in our experiments. Five latest medical image fusion methods and six image quality metrics are used for comparing and evaluating the effectiveness of the proposed algorithm. Based on the analysis of the experimental results, the proposed algorithm has obtained better performance than the current latest algorithms.

An improved medical image synthesis approach based on marine predators algorithm and maximum Gabor energy

Sparse representation (SR) has been widely used in image fusion in recent years. However, source image, segmented into vectors, reduces correlation and structural information of texture with conventional SR methods, and extracting texture with the sliding window technology is more likely to cause spatial inconsistency in flat regions of multi-modality medical fusion image. To solve these problems, a novel fusion method that combines separable dictionary optimization with Gabor filter in non-subsampled contourlet transform (NSCT) domain is proposed. Firstly, source images are decomposed into high frequency (HF) and low frequency (LF) components by NSCT. Then the HF components are reconstructed sparsely by separable dictionaries with iterative updating sparse coding and dictionary training. In the process, sparse coefficients and separable dictionaries are updated by orthogonal matching pursuit (OMP) and manifold-based conjugate gradient method, respectively. Meanwhile, the Gabor energy as weighting factor is utilized to guide the LF components fusion, and this further improves the fusion degree of low-significant feature in the flat regions. Finally, the fusion components are transformed to obtain fusion image by inverse NSCT. Experimental results demonstrate the more competitive results of the proposal, leading to the state-of-art performance on both visual quality and objective assessment.

Multi-modality medical image fusion based on separable dictionary learning and Gabor filtering

Multi-modality image fusion technique is essential for target description. The complementary information can not only compensate the limitations of each image effectively, but also enhance visual effect to human eyes. To preserve structure information and perform detailed information of each source multi-modality image, a novel fusion framework with two-scale image reconstruction is proposed. In the proposal, an improved guided image filtering (GIF)-based weighted average via Gabor energy is put forward for the fusion of base layers contained large scale structure information, and a sparse representation-based separable dictionary learning is recommended to capture small scale detailed information of detail layers. Finally, according to the texture enhancement fusion rule, the fused base and detail layers are integrated to obtain the fusion image. Experimental results demonstrate that the proposed method exhibits significant performance than the basic GIF algorithm, and also outperforms the existing state-of-the-art methods in terms of better edge texture clarity. Moreover, the fusion results show abundant information and better visual effect.

Fengzhen Zhang

Papers

Multi-modality medical image fusion based on separable dictionary learning and Gabor filtering

Multi-modality image fusion combining sparse representation with guidance filtering