Recent Medical Image Fusion Techniques: A Review

Rani, V. Amala; Lalithakumari, S.

doi:10.5958/0976-5506.2019.01785.6

Cited by 7 publications

(2 citation statements)

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“…However, limited by the equipment, the quality of medical images is often unsatisfying. Two or more medical images are merged into one image with comprehensive features by fusion methods for the sake of improving the accuracy of clinical diagnosis (Li et al, 2017;Amala Rani and Lalithakumari, 2019;Huang et al, 2020). Medical image fusion aims at extracting more useful information of input images, improving the application of medical images, and helping doctors understand image contents better.…”

Section: Introductionmentioning

confidence: 99%

Two-Scale Multimodal Medical Image Fusion Based on Structure Preservation

Liu

Wang

Yin

et al. 2022

Front. Comput. Neurosci.

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Medical image fusion has an indispensable value in the medical field. Taking advantage of structure-preserving filter and deep learning, a structure preservation-based two-scale multimodal medical image fusion algorithm is proposed. First, we used a two-scale decomposition method to decompose source images into base layer components and detail layer components. Second, we adopted a fusion method based on the iterative joint bilateral filter to fuse the base layer components. Third, a convolutional neural network and local similarity of images are used to fuse the components of the detail layer. At the last, the final fused result is got by using two-scale image reconstruction. The contrast experiments display that our algorithm has better fusion results than the state-of-the-art medical image fusion algorithms.

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Section: Introductionmentioning

confidence: 99%

Two-Scale Multimodal Medical Image Fusion Based on Structure Preservation

Liu

Wang

Yin

et al. 2022

Front. Comput. Neurosci.

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“…Consequently, texture FE is a long-standing problem in medical image segmentation. To this end, multiresolution approaches has drawn considerable attraction in recent years [17]. Among them, Gabor and wavelet filters has emerged as an effective tool in providing multiscale view on texture and in enabling to extract features at different orientations [18].…”

Section: Introductionmentioning

confidence: 99%

Whale Optimization for Wavelet-Based Unsupervised Medical Image Segmentation: Application to CT and MR Images

Vaiyapuri¹,

Alaskar²

2020

IJCIS

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Image segmentation plays crucial role in medical image analysis and forms the basis for clinical diagnosis and patient's treatment planning. But the large variation in organ shapes, inhomogeneous intensities, poor contrast, organic nature of textures and complex boundaries in medical images makes segmentation process adverse and challenging. Further, the absence of annotated ground-truth dataset in medical field limits the advantages of the trending deep learning techniques causing several setbacks. Though numerous unsupervised methods are reported in literature to combat the challenges in medical domain, achieving better segmentation quality still remains as an open issue. This work aim to address this issue integrating the strength of multiresolution analysis and the meta-heuristic optimization techniques for unsupervised medical image segmentation. The proposed approach employs undecimated wavelet frames to extract translation invariant texture and gray information at different orientations to effectively characterize the textures in medical images. Next, the approach introduces the latest meta-heuristic whale optimization algorithm (WOA), the global optimizer to enhance the performance of unsupervised clustering algorithm with optimized cluster centers to cluster the extracted wavelet texture features. Moreover, the study contributes to fill the gap in literature investigating for the first time different intelligence algorithms such as fuzzy, genetic algorithm (GA) and particle swarm optimization (PSO) for unsupervised medical image segmentation to demonstrate the efficacy of the proposed approach. Evaluation was performed on medical CT and MR images based on feature similarity (FSIM), dice (DC) and feature of merits (FOMs). Experimental results demonstrates the supremacy of the proposed approach over other intelligence algorithms. Finally, statistical study with ANOVA analysis was carried out to confirm the significance of the proposed approach in determining the optimal solution and displaying promising segmentation results toward diagnostic support for radiologist.

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