Multi-focus image fusion with Geometrical Sparse Representation

Tan, Jin; Zhang, Taiping; Zhao, Linchang; Luo, Xiaoliu; Tang, Yuan Yan

doi:10.1016/j.image.2020.116130

Cited by 18 publications

(6 citation statements)

References 46 publications

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“…Let the output image from the NSCT process be the Si and D be the database that acts as the repository and ρ be the coefficient introduced by JSR as mentioned in Eq. (8).…”

Section: Joint Sparse Representation (Jsr)mentioning

confidence: 99%

“…The proposed method of figure amalgamation is concentrated on medical image inputs and the inputs were in multiple frequency domain, and employs a hybrid concept of NSCT integrated with JSR to accept the multi-frequency domain input images like CT [8] images and MRI to form a multimodal amalgamated figure. This method holds good in medical domain applications, as the end results are highly informative based on which the medical treatment decision can be taken accurately.…”

Section: Introductionmentioning

confidence: 99%

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Non Sub-Sampled Contourlet with Joint Sparse Representation Based Medical Image Fusion

Kittusamy¹,

Kumar²

2023

Computer Systems Science and Engineering

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Medical Image Fusion is the synthesizing technology for fusing multimodal medical information using mathematical procedures to generate better visual on the image content and high-quality image output. Medical image fusion represents an indispensible role in fixing major solutions for the complicated medical predicaments, while the recent research results have an enhanced affinity towards the preservation of medical image details, leaving color distortion and halo artifacts to remain unaddressed. This paper proposes a novel method of fusing Computer Tomography (CT) and Magnetic Resonance Imaging (MRI) using a hybrid model of Non Sub-sampled Contourlet Transform (NSCT) and Joint Sparse Representation (JSR). This model gratifies the need for precise integration of medical images of different modalities, which is an essential requirement in the diagnosing process towards clinical activities and treating the patients accordingly. In the proposed model, the medical image is decomposed using NSCT which is an efficient shift variant decomposition transformation method. JSR is exercised to extricate the common features of the medical image for the fusion process. The performance analysis of the proposed system proves that the proposed image fusion technique for medical image fusion is more efficient, provides better results, and a high level of distinctness by integrating the advantages of complementary images. The comparative analysis proves that the proposed technique exhibits better-quality than the existing medical image fusion practices.

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“…Let the output image from the NSCT process be the Si and D be the database that acts as the repository and ρ be the coefficient introduced by JSR as mentioned in Eq. (8).…”

Section: Joint Sparse Representation (Jsr)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Non Sub-Sampled Contourlet with Joint Sparse Representation Based Medical Image Fusion

Kittusamy¹,

Kumar²

2023

Computer Systems Science and Engineering

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“…Restoration homographic matrices, dictionary based on k-means labeling, exemplar based inpainting manual initialization Deep learning based methods [11], [12], [13], [14], [15], [16], [17], [18], [19], [20] De-fencing adversarial, structural [21], [22], [23], [24], [25] Fusion multi-scale decomposition, dictionary-learning, nuclear norm regularizer, morphologies constraints, adaptive fusion rules, fractional differential coefficients, geometric sparse coefficients overcomplete dictionary, patch based clustering, single dictionary learning time efficiency, separate fusion and noise removal tasks, information loss due to channel-wise processing Model based methods [26], [27], [28], [29], [30], [31], [32], [33] [34], [35], [36], [37] Fusion SSIM, encoder features, K-means clustering, NSCT, Coupled-Neural-Ps consistency verification photo realistic fusion, blocking artifacts, post processing complete contours; and generator-discriminator setting for prediction of contour completion. In [3], subtraction based on gray-scale binarization is applied on multi-focus auxiliary images to obtain initial mask for image inpainting.…”

Section: Schemesmentioning

confidence: 99%

“…The scheme learns overcomplete dictionary using patch based clustering to transfer structural information in all-in-focus images. Similarly, Tan et al [25], used geometric sparse coefficients (obtained from single dictionary image) to generate focus regions, and preserve important information in resultant images. The scheme also improves time complexity as overcomplete dictionary training is not required.…”

Section: Schemesmentioning

confidence: 99%

De-Fencing and Multi-Focus Fusion Using Markov Random Field and Image Inpainting

2022

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Multi-focus image fusion aims at combining source information from differently focused images. Fusion of multi-focus images has great applications in machine vision. The paper focuses on removal of fence occlusions in multi-focus images. The proposed model extracts fence occlusion map using salient image features, and refined by morphological operators. Binary operators, and inpainting methods are used for fence removal and restoration. The proposed model estimates the fence area using statistical characteristics in focused regions. Similarly, binary filtering is used to perform thinning of enlarged areas for optimised restoration. The proposed model employs guided filtering for consistency verification. Fusion and restoration results are compared using several (reference and no-reference based) image quality metrics. Simulations show that proposed scheme achieves better results (visually and quantitatively) as compared to existing state-of-the-art techniques.

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“…The deep‐learning‐based medical image fusion method is a focus of current research. Its framework of the deep‐learning‐based fusion method 16 is primarily concentrated on convolutional neural networks (CNNs). These imaging techniques indicate that medical image fusion is a promising approach that can merge complementary information from medical images of different modalities.…”

Section: Introductionmentioning

confidence: 99%

Improving the classification ability of network utilizing fusion technique in contrast‐enhanced spectral mammography

Song

Zheng

et al. 2021

Medical Physics

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Contrast-enhanced spectral mammography (CESM) is an effective tool for diagnosing breast cancer with the benefit of its multiple types of images. However, few models simultaneously utilize this feature in deep learning-based breast cancer classification methods. To combine multiple features of CESM and thus aid physicians in making accurate diagnoses, we propose a hybrid approach by taking advantages of both fusion and classification models. Methods: We evaluated the proposed method on a CESM dataset obtained from 95 patients between ages ranging from 21 to 74 years, with a total of 760 images. The framework consists of two main parts: a generative adversarial network based image fusion module and a Res2Net-based classification module. The aim of the fusion module is to generate a fused image that combines the characteristics of dual-energy subtracted (DES) and low-energy (LE) images, and the classification module is developed to classify the fused image into benign or malignant. Results: Based on the experimental results, the fused images contained complementary information of the images of both types (DES and LE), whereas the model for classification achieved accurate classification results. In terms of qualitative indicators, the entropy of the fused images was 2.63, and the classification model achieved an accuracy of 94.784%, precision of 95.016%, recall of 95.912%, specificity of 0.945, F1_score of 0.955, and area under curve of 0.947 on the test dataset, respectively. Conclusions: We conducted extensive comparative experiments and analyses on our in-house dataset, and demonstrated that our method produces promising results in the fusion of CESM images and is more accurate than the state-ofthe-art methods in classification of fused CESM.

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Multi-focus image fusion with Geometrical Sparse Representation

Cited by 18 publications

References 46 publications

Non Sub-Sampled Contourlet with Joint Sparse Representation Based Medical Image Fusion

Non Sub-Sampled Contourlet with Joint Sparse Representation Based Medical Image Fusion

De-Fencing and Multi-Focus Fusion Using Markov Random Field and Image Inpainting

Improving the classification ability of network utilizing fusion technique in contrast‐enhanced spectral mammography

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