A novel approach in multimodality medical image fusion using optimal shearlet and deep learning

Velmurugan, S.; Sivakumar, P.; Sampson, Jenyfal

doi:10.1002/ima.22436

Cited by 28 publications

(7 citation statements)

References 35 publications

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“…It is known to us that just one evaluation index could not well demonstrate the quality of fused images in quantitative assessment. Thus, for the sake of making a comprehensive evaluation for the fusion images, six popular fusion evaluation metrics are introduced in this section, namely visual information fidelity for fusion (VIFF) [ 29 , 30 , 31 , 32 , 33 ], Q S [ 34 ], average gradient (AG) [ 20 , 35 , 36 ], correlation coefficient (CC) [ 20 , 37 , 38 ], spatial frequency (SF) [ 20 , 39 , 40 , 41 ], and Q W [ 34 , 42 ]. In terms of all the six metrics, the higher the value data of the evaluation index, the better the fusion performance will be.…”

Section: Resultsmentioning

confidence: 99%

Saliency-Guided Nonsubsampled Shearlet Transform for Multisource Remote Sensing Image Fusion

2021

Sensors

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The rapid development of remote sensing and space technology provides multisource remote sensing image data for earth observation in the same area. Information provided by these images, however, is often complementary and cooperative, and multisource image fusion is still challenging. This paper proposes a novel multisource remote sensing image fusion algorithm. It integrates the contrast saliency map (CSM) and the sum-modified-Laplacian (SML) in the nonsubsampled shearlet transform (NSST) domain. The NSST is utilized to decompose the source images into low-frequency sub-bands and high-frequency sub-bands. Low-frequency sub-bands reflect the contrast and brightness of the source images, while high-frequency sub-bands reflect the texture and details of the source images. Using this information, the contrast saliency map and SML fusion rules are introduced into the corresponding sub-bands. Finally, the inverse NSST reconstructs the fusion image. Experimental results demonstrate that the proposed multisource remote image fusion technique performs well in terms of contrast enhancement and detail preservation.

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Section: Resultsmentioning

confidence: 99%

Saliency-Guided Nonsubsampled Shearlet Transform for Multisource Remote Sensing Image Fusion

2021

Sensors

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“…To cope with the problem, a blob shape‐sensitive function can be formulated as

λ_{∥}^{S, θ} (x) = \sqrt{E [I_{j}^{2}] - {((), E ([], I_{j}))}^{2}} .

Here,

E

indicates an expected value operator. Inspired by the geometric representation of the Hessian matrix in shape description, 36,37 two line strength measures,

{normalℓ}_{\max}^{S, θ}

and

{normalℓ}_{\min}^{S, θ}

can be described as

{normalℓ}_{\max}^{S, θ} = λ_{⊥, \max}^{S, θ} (x) - κ \times λ_{∥}^{S, θ} (x),

{normalℓ}_{\min}^{S, θ} = λ_{⊥, \min}^{S, θ} (x) - κ \times λ_{∥}^{S, θ} (x) .

Here,

κ = 0.7

is a positive coefficient. Subsequently, Xiao et al designed two multidirectional integration functions to suppress clutters and other structures

F_{\max} (x) = \max (\max_{1 \leq i \leq 2 (L - 1)} (ℓ_{\max}^{S, θ_{i}}), 0),

…”

Section: Methodsmentioning

confidence: 99%

“…Here, E indicates an expected value operator. Inspired by the geometric representation of the Hessian matrix in shape description, 36,37 two line strength measures, ℓ S,θ max and ℓ S,θ min can be described as…”

Section: Multi-feature Fusionmentioning

confidence: 99%

Lung lobe segmentation in computed tomography images based on multi‐feature fusion and ensemble learning framework

Peng

Zhang

2023

Int J Imaging Syst Tech

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Lung lobe segmentation in computed tomography (CT) images can be regarded as essential supporting information for the diagnosis and treatment of lung diseases, yet it is a challenging uncertainty for the complex segmentation task due to the diverse structures like indistinguishable pulmonary arteries and veins, unpredictable pathological deformation and blurring pulmonary fissures. To circumvent these challenges, we present a productive deep learning network based on multi‐feature fusion and ensemble learning approach to highlight pulmonary fissure representation and suppress other structures for lung lobe segmentation in CT images. Considering that pulmonary fissures are the physical boundaries of lung lobes, a multi‐feature fusion approach is presented to integrate original images, enhanced fissure magnitude images, and enhanced fissure orientation images to highlight shape representation for semantic segmentation. In addition, a deep‐supervised ensemble learning network is employed to combine multiple inducers for lung lobe segmentation improvement. The performance of the proposed deep learning model is validated in experiments with an available dataset, it acquired a high IoU value and a low Hausdorff distance compared with manual references. Experimental results demonstrate that the proposed computational model based on multi‐feature fusion and deep‐supervised ensemble learning framework has an improved predictive performance than many state‐of‐the‐art deep neural networks in lung lobe segmentation.

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“…The image alignment is performed via rotating, shifting, and scaling. The performance instability to the changed data set and color distortion is considered as the important limitations during image fusion 11‐13 …”

Section: Introductionmentioning

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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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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A novel approach in multimodality medical image fusion using optimal shearlet and deep learning

Cited by 28 publications

References 35 publications

Saliency-Guided Nonsubsampled Shearlet Transform for Multisource Remote Sensing Image Fusion

Saliency-Guided Nonsubsampled Shearlet Transform for Multisource Remote Sensing Image Fusion

Lung lobe segmentation in computed tomography images based on multi‐feature fusion and ensemble learning framework

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

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