Integrated fusion framework using hybrid domain and deep neural network for multimodal medical images

Venkatesan, B.; Ragupathy, U. S.

doi:10.1007/s11045-021-00813-9

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…In addition to being visually appealing to the human eye, fused images are useful for various subsequent tasks [54][55][56]. Multimodal image fusion finds applications in medicine, surveillance, and remote sensing [57][58][59][60]. The extraction of features is typically the first step in multi-modal image fusion methods, followed by identifying and classifying image elements to determine the most notable features.…”

Section: Multi-modal Fusionmentioning

confidence: 99%

Pansharpening and spatiotemporal image fusion method for remote sensing

Anand,

Sharma

2024

Eng. Res. Express

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In last decades, remote sensing technology has rapidly progressed, leading to the development of numerous earth satellites such as Landsat 7, QuickBird, SPOT, Sentinel-2, and IKONOS. These satellites provide multispectral images with a lower spatial resolution and panchromatic images with a higher spatial resolution. However, satellite sensors are unable to capture images with high spatial and spectral resolutions simultaneously due to storage and bandwidth constraints, among other things. Image fusion in remote sensing has emerged as a powerful tool for improving image quality and integrating important features from multiple source images into one, all while maintaining the integrity of critical features. It is especially useful for high-resolution remote sensing applications that need to integrate features from multiple sources and hence a vital pre-processing step for various applications, including medical, computer vision, and satellite imaging. This review initially gives a basic framework for image fusion, followed by statistical analysis and a comprehensive review of various state-of-the-art image fusion methods, where they are classified based on the number of sensors used, processing levels, and type of information being fused. Subsequently, a thorough analysis of STF and pansharpening techniques for remote sensing applications has been covered, where the dataset of the DEIMOS-2 satellite is employed for evaluating various pansharpening methods while MODIS and Landsat images are employed in the spatiotemporal fusion method. A comparative evaluation of several approaches has been carried out to assess the merits and drawbacks of the current approaches. Several real-time applications of remote sensing image fusion have been explored, and current and future directions in fusion research for remote sensing have been discussed, along with the obstacles they present.

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Section: Multi-modal Fusionmentioning

confidence: 99%

Pansharpening and spatiotemporal image fusion method for remote sensing

Anand,

Sharma

2024

Eng. Res. Express

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“…Deep learning techniques have mostly been employed to improve incident photon timing resolution and localization accuracy with the objective of enhancing overall spatial and time-of-flight (TOF) resolutions in PET. The purpose of the research being done on quantitative SPECT and PET imaging right now is to get rid of the effects of noise, artifacts, and movement [16].…”

Section: Introductionmentioning

confidence: 99%

Multimodal Medical Image Fusion using Hybrid Domains

Naidu

Bhavana

2022

SCPE

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In a variety of clinical applications, image fusion is critical for merging data from multiple sources into a single, more understandable outcome. The use of medical image fusion technologies to assist the physician in executing combination procedures can be advantageous. The diagnostic process includes preoperative planning, intra operative supervision, an interventional treatment. In this thesis, a technique for image fusion was suggested that used a combination model of PCA and CNN. A method of real-time image fusion that employs pre-trained neural networks to synthesize a single image from several sources in real-time. A innovative technique for merging the images is created based on deep neural network feature maps and a convolution network. Picture fusion has become increasingly popular as a result of the large variety of capturing techniques available. The proposed design is implemented using deep learning technique. The accuracy of the proposed design is around 15% higher than the existing design. The proposed fusion algorithm is verified through a simulation experiment on different multimodality images. Experimental results are evaluated by the number of well-known performance evaluation metrics

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