An Optimal Registration on Shearlet domain with Novel Weighted Energy fusion for Multi-Modal Medical Images

Nair, Rekha R; Singh, Tripty

doi:10.1016/j.ijleo.2020.165742

Cited by 46 publications

(14 citation statements)

References 31 publications

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“…Table 4 depicts the comparison of performance metrics with state‐of‐art methods. In this work, we have chosen five state‐of‐art methods, namely, pyramid based discrete wavelet transform (PBDWT), 27 non subsampled shearlet transform (NSST), 19 optimal wavelet coupled images factorization (OWCIF), 28 DOBWIF, 29 and the proposed method. These state‐of‐art methods are evaluated with different performance metrics such as SD ( S dv ), structural similarity index ( S SI ), feature similarity index ( F SI ), mutual information ( M if ), fusion quality ( F q ), and RMSE.…”

Section: Resultsmentioning

confidence: 99%

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Modified Global Flower Pollination Algorithm‐based image fusion for medical diagnosis using computed tomography and magnetic resonance imaging

Sumithra

Malathi²

2020

Int J Imaging Syst Tech

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Recently, the computed tomography (CT) and magnetic resonance imaging (MRI) medical image fusion have turned into a challenging issue in the medical field. The optimal fused image is a significant component to detect the disease easily. In this research, we propose an iterative optimization approach for CT and MRI image fusion. Initially, the CT and MRI image fusion is subjected to a multilabel optimization problem. The main aim is to minimize the data and smoothness cost during image fusion. To optimize the fusion parameters, the Modified Global Flower Pollination Algorithm is proposed. Here, six sets of fusion images with different experimental analysis are evaluated in terms of different evaluation metrics such as accuracy, specificity, sensitivity, SD, structural similarity index, feature similarity index, mutual information, fusion quality, and root mean square error (RMSE). While comparing to state‐of‐art methods, the proposed fusion model provides best RMSE with higher fusion performance. Experiments on a set of MRI and CT images of medical data set show that the proposed method outperforms a very competitive performance in terms of fusion quality.

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Section: Resultsmentioning

confidence: 99%

“…Figure 7 illustrates the RMSE performance based on state‐of‐art approaches. We use state‐of‐art approaches, namely, NSST, 19 DOBWIF, 29 Digital Subtraction Angiography (DSA), 17 wavelet transform (WT), 30 and proposed methods. Moreover, the CT and MRI medical image fusion effectiveness is computed with RMSE.…”

Section: Resultsmentioning

confidence: 99%

Modified Global Flower Pollination Algorithm‐based image fusion for medical diagnosis using computed tomography and magnetic resonance imaging

Sumithra

Malathi²

2020

Int J Imaging Syst Tech

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“…Several methods of image fusion [13][14] [15] advanced from the previous decades. Current fusion approaches were categorized onto the principle of the stage at which the fusion carried out.…”

Section: Introductionmentioning

confidence: 99%

“…Current fusion approaches were categorized onto the principle of the stage at which the fusion carried out. The 3 levels of fusion are, fusion at pixel values called pixel level [13], fusion based on a feature called feature level [14] and fusion based on decision called decision level [15]. The first and lower level of fusion is the pixel level and operations are carried out on pixel intensity values directly.…”

Section: Introductionmentioning

confidence: 99%

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Multiresolution approach on Medical Image Fusion by Modified Local Energy

Nair

Babu

2022

Preprint

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Human and machine perception data that are not redundant are very important in the medical field for diagnosis and treatment. The proposed work fuses medical images to extract valuable necessary information from dissimilar images to a single image in the Wavelet domain using a novel Modified Local Energy(MLE) fusion rule termed ModifiedLocal Energy Image Fusion(MLEIF). The image edges are essential information points that are used to display a clear visual image structure. Modified Local energy helps to provide edge characteristics more clearly in the fusion outcome than a single pixel-based fusion rule. The denoising property of the local energy is the additional advantage of the proposed fusion. The similarity of the fused image with the source images is improved by B-Spline registration in the pre-processing stage. Finally, the fused image is created with all the corresponding coefficients by transforming the inverse wavelet. SVM-based contouring of the lesion part helps observes to identify the lesion part from the fused image. Carried out experiments on real-time and standard data sets with expert radiologist subjective evaluation and quantitative analysis by well-known non-reference performance measures.

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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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An Optimal Registration on Shearlet domain with Novel Weighted Energy fusion for Multi-Modal Medical Images

Cited by 46 publications

References 31 publications

Modified Global Flower Pollination Algorithm‐based image fusion for medical diagnosis using computed tomography and magnetic resonance imaging

Modified Global Flower Pollination Algorithm‐based image fusion for medical diagnosis using computed tomography and magnetic resonance imaging

Multiresolution approach on Medical Image Fusion by Modified Local Energy

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

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