Feature extraction using DPSO for medical image fusion based on NSCT

Mahima, Mahima; Padmavathi, N.; Karki, Maya V

doi:10.1109/rteict.2017.8256599

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…Firstly, NSCT processing is performed on the source image to obtain LF and HF in each direction, where a weighted fusion strategy based on gray mean deviation is used for LF, a weighted fusion strategy based on local energy is used for HF, and a fused image is transformed by inverse NSCT. In order to extract more useful feature information, Padmavathi et al [17] proposed a new fusion method which combines Darwinian particle swarm optimization algorithm with NSCT. Elements in particle swarm optimization (PSO) can be used to extract the required features and remove redundant parts [18].…”

Section: Computational and Mathematical Methods In Medicinementioning

confidence: 99%

A Review of Multimodal Medical Image Fusion Techniques

Huang

Yang

Yin

et al. 2020

Computational and Mathematical Methods in Medicine

132

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The medical image fusion is the process of coalescing multiple images from multiple imaging modalities to obtain a fused image with a large amount of information for increasing the clinical applicability of medical images. In this paper, we attempt to give an overview of multimodal medical image fusion methods, putting emphasis on the most recent advances in the domain based on (1) the current fusion methods, including based on deep learning, (2) imaging modalities of medical image fusion, and (3) performance analysis of medical image fusion on mainly data set. Finally, the conclusion of this paper is that the current multimodal medical image fusion research results are more significant and the development trend is on the rise but with many challenges in the research field.

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Section: Computational and Mathematical Methods In Medicinementioning

confidence: 99%

A Review of Multimodal Medical Image Fusion Techniques

Huang

Yang

Yin

et al. 2020

Computational and Mathematical Methods in Medicine

132

View full text Add to dashboard Cite

show abstract

Efficient Medical Image Fusion Using 2-Dimensional Double Density Wavelet Transform to Improve Quality Metrics

Rani

Lalithakumari

2021

IEEE Instrum. Meas. Mag.

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Optimized Dual-Tree Complex Wavelet Transform and Fuzzy Entropy for Multi-Modal Medical Image Fusion: A Hybrid Meta-Heuristic Concept

Kumar

Prasad²,

Prasad

2021

J. Mech. Med. Biol.

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In recent times, multi-modal medical image fusion has emerged as an important medical application tool. An important goal is to fuse the multi-modal medical images from diverse imaging modalities into a single fused image. The physicians broadly utilize this for precise identification and treatment of diseases. This medical image fusion approach will help the physician perform the combined diagnosis, interventional treatment, pre-operative planning, and intra-operative guidance in various medical applications by developing the corresponding information from clinical images through different modalities. In this paper, a novel multi-modal medical image fusion method is adopted using the intelligent method. Initially, the images from two different modalities are applied with optimized Dual-Tree Complex Wavelet Transform (DT-CWT) for splitting the images into high-frequency subbands and low-frequency subbands. As an improvement to the conventional DT-CWT, the filter coefficients are optimized by the hybrid meta-heuristic algorithm named as Hybrid Beetle and Salp Swarm Optimization (HBSSO) by merging the Salp Swarm Algorithm (SSA), and Beetle Swarm Optimization (BSO). Moreover, the fusion of the source images’ high-frequency subbands was done by the optimized type-2 Fuzzy Entropy. The upper and lower membership limits are optimized by the same hybrid HBSSO. The optimized type-2 fuzzy Entropy automatically selects high-frequency coefficients. Also, the fusion of the low-frequency sub-images is performed by the Averaging approach. Further, the inverse optimized DT-CWT on the fused image sets helps to obtain the final fused medical image. The main objective of the optimized DT-CWT and optimized type-2 fuzzy Entropy is to maximize the SSIM. The experimental results confirm that the developed approach outperforms the existing fusion algorithms in diverse performance measures.

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Feature extraction using DPSO for medical image fusion based on NSCT

Cited by 4 publications

References 11 publications

A Review of Multimodal Medical Image Fusion Techniques

A Review of Multimodal Medical Image Fusion Techniques

Efficient Medical Image Fusion Using 2-Dimensional Double Density Wavelet Transform to Improve Quality Metrics

Optimized Dual-Tree Complex Wavelet Transform and Fuzzy Entropy for Multi-Modal Medical Image Fusion: A Hybrid Meta-Heuristic Concept

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