MRI and SPECT Image Fusion Using a Weighted Parameter Adaptive Dual Channel PCNN

Panigrahy, Chinmaya; Seal, Ayan; Mahato, Nihar Kumar

doi:10.1109/lsp.2020.2989054

Cited by 86 publications

(37 citation statements)

References 39 publications

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“…Conventionally, the values of the PCNN parameters are selected based on successive trials, but recently Xu et al [28] presented different methods related to the selection of the PCNN parameters. Moreover, the other PCNN-based approaches are presented using a adapting linking parameter based on local contrast, entropy (EN), directional gradient, saliency, fractional dimension, local visibility and intensity of pixels or coefficients [16,[28][29][30][31][32]. They exhibited improved fusion performance, however, successive trial-based parameter selection is involved in those approaches.…”

Section: Related Workmentioning

confidence: 99%

“…13 and 14, it is observed that Liu et al [34] and the proposed methods can preserve more structural details but the colour information is better highlighted by the proposed method. Yin et al [31] and the [34], Yin et al [31], Li and Zhao [14], Tan et al [23], (left to right: second row) Panigrahy et al [32], Li et al [7], Proposed method…”

mentioning

confidence: 99%

“…Moreover, the proposed method ranks first for Q Y metric and gives 16.16% higher values than the method proposed by Li and Zhao [14], which indicates that the proposed method is best at integrating the complementary information present in source MR-SPECT images. Therefore, it can be concluded based on the experimental results that the proposed fusion method provides [34], Yin et al [31], Li and Zhao [14], Tan et al [23], (left to right: second row) Panigrahy et al [32], Li et al [7], Proposed method significantly better-fused images in both the terms of visual and quantitative performance for MR-SPECT image fusion.…”

mentioning

confidence: 99%

“…Performance evaluation of existing fusion approaches with the proposed method for MR-SPECT pair-2 shown in Fig. 14 Performance metricFusion methods Liu et al[34] Yin et al[31] Li and Zhao[14] Tan et al[23] Panigrahy et al[32] Li et al[7] Proposed method…”

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confidence: 99%

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NSST domain CT–MR neurological image fusion using optimised biologically inspired neural network

et al. 2020

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Diagnostic medical imaging plays an imperative role in clinical assessment and treatment of medical abnormalities. The fusion of multimodal medical images merges complementary information present in the multi-source images and provides a better interpretation with improved diagnostic accuracy. This paper presents a CT-MR neurological image fusion method using an optimised biologically inspired neural network in nonsubsampled shearlet (NSST) domain. NSST decomposed coefficients are utilised to activate the optimised neural model using particle swarm optimisation method and to generate the firing maps. Low and high-frequency NSST subbands get fused using max-rule based on firing maps. In the optimisation process, a fitness function is evaluated based on spatial frequency and edge index of the resultant fused image. To analyse the fusion performance, extensive experiments are conducted on the different CT-MR neurological image dataset. Objective performance is evaluated based on different metrics to highlight the clarity, contrast, correlation, visual quality, complementary information, salient information, and edge information present in the fused images. Experimental results show that the proposed method is able to provide better-fused images and outperforms other existing methods in both visual and quantitative assessments.

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Section: Related Workmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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NSST domain CT–MR neurological image fusion using optimised biologically inspired neural network

et al. 2020

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“…In general, the word image fusion means a method to amalgamate and coalesce all the useful complementary information acquired from multiple source images of a given scene into a single representation called fused image. Image fusion has a number of applications such as multi-focus [1]- [4], medical [5]- [9], infrared-visible [10]- [14], etc. Due to the tremendous advancement of fusion techniques, the problem of performance evaluation and assessment of image fusion techniques has gained a lot of attention within the research community [15].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Water wave optimized nonsubsampled shearlet transformation technique for multimodal medical image fusion

Amrita

Joshi²,

Kumar³

et al. 2023

Concurrency and Computation

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Medical image fusion has advanced to the point that it is now possible to combine multiple medical images for accurate disease diagnosis and treatment. The state-of-art techniques based on spatial and transform domains suffer from different limitations such as low fused image quality, spectral degradation, contrast reduction, low edge information preserving, lack of shift-invariance, high computational complexity, classification accuracy, and sensitivity to noise. The main motivation of this work is to generate a single image with excellent visual clarity that retains the features of the source images. This article proposes a water wave optimized nonsubsampled shearlet transformation technique (NSST) for multimodal medical image fusion, in which the water wave optimization (WWO) algorithm is used to allocate the weights of the NSST approach's high-frequency subbands. The NSST approach is primarily used in this work due to its ability to withstand shift-invariance and its potential to improve the visual clarity of the fused multimodal image by preserving the essential features present in the image's various directions and edges. We combined the NSST technique with the WWO algorithm, which processes the edges, details, and contourlets of medical images using a max selection strategy based on the fitness function, to improve image quality and computational costs. The WWO algorithm is mainly applied to the NSST to minimize the L1 distance between the fused and the source images. Hence to overcome this problem a condition CNN optimized with a hybrid tunicate swarm memetic (TSM) algorithm is used to incorporate both the benefits offered by the condition CNN-TSM algorithm and NSST. The TSM optimized condition CNN architecture is used to preserve the coefficients of the image and improve the perceiving capability of the high-frequency sub-bands. An inverse NSST is used for fused frequency sub-band integration. Finally, the efficiency of the proposed methodology is evaluated in terms of enhanced visual feature quality, edge detection, contour detection, and computational performance.

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MRI and SPECT Image Fusion Using a Weighted Parameter Adaptive Dual Channel PCNN

Cited by 86 publications

References 39 publications

NSST domain CT–MR neurological image fusion using optimised biologically inspired neural network

NSST domain CT–MR neurological image fusion using optimised biologically inspired neural network

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

Water wave optimized nonsubsampled shearlet transformation technique for multimodal medical image fusion

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