Siamese networks and multi-scale local extrema scheme for multimodal brain medical image fusion

Ding, Zhiwei; Zhou, Dongming; Li, Haiyan; R, Hou; Liu, Yanyu

doi:10.1016/j.bspc.2021.102697

Cited by 30 publications

(5 citation statements)

References 20 publications

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“…In [14] Multi-Scale Local Extrema Scheme (MSLES) is used to breakdown the source pictures, making them more efficient and effective for practical implementation. To build the weight map, features are retrieved using a Siamese neural network, which results in increased Standard Deviation (SD) and Average Gradient (AG).…”

Section: Deep Learning-based Image Fusion Methodmentioning

confidence: 99%

Multi-modal image processing pipeline for NDE of structures and industrial assets

Shahsavarani¹,

López²,

Maldague³

2023

Thermosense: Thermal Infrared Applications XLV

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Infrared thermography (IRT) technology has evolved during the last decade extending its capabilities to the industrial and infrastructures level. Because of the necessity to perform regular inspections of in-service assets such as bridges, it becomes necessary to investigate and develop efficient inspection technologies that can adapt to the needs of the industry. So, IRT is considered an effective technology to perform NDE. However, its integration with other sensing technologies such as visible cameras still needs to be further investigated so the inspection and maintenance strategy can be more effective when inspecting large structures and assets. Hence, this project investigates fusion strategies and proposes a multi-modal processing pipeline using a deep learning-based panorama stitching method for infrared and visible images. Then, an image registration method to fuse infrared and visible images so identification of defects in visible and thermal spectra becomes more efficient.

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Section: Deep Learning-based Image Fusion Methodmentioning

confidence: 99%

Multi-modal image processing pipeline for NDE of structures and industrial assets

Shahsavarani¹,

López²,

Maldague³

2023

Thermosense: Thermal Infrared Applications XLV

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“…In recent years, deep learning-based methods for medical image fusion have been proposed. For example, an image fusion technique that combines Convolutional Neural Network (CNN) and NSCT was presented by Wang et al (2021b) The combination of SSN and the MSLES was proposed by Ding et al (2021). The CNN was used and merged with SR by Shibu and Priyadharsini (2021).…”

Section: Introductionmentioning

confidence: 99%

A Robust Medical Image Fusion Based on Synthetic Focusing Degree Criterion and Special Kernel Set for Clinical Diagnosis

Lepcha,

Goyal,

Dogra

et al. 2024

Journal of Computer Science

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Medical imaging has been widely used to diagnose diseases over the last two decades. Medical professionals still struggle to diagnose diseases using a single modality since there is a shortage of data in this domain. Therefore, images of specific organs with diseases from a variety of medical imaging systems can be combined using a technique called image fusion. Medical image fusion has prompted immense requisite applications in clinical applications in recent years. However, the fusion of medical images still facing a variety of challenges due to the input image quality. Protonema such as noise and low-contrast input medical images significantly reduces the quality of medical images. Still, recent image fusion methods are not significantly able to address the image quality problems. In order to address these problems, this study introduces a novel image fusion method that provides effective fusion performance even if the input images are noisy or low-contrast by combining the benefits of synthetic focusing degree criterion with a special kernel set. First, a Gaussian Curvature Filter (GCF) is used to sharpen the images in order to perform a Salient Feature Extraction (SFE). Then, we create a synthetic Focusing Degree Condition (FDC) that combines the Spatial Frequency (SF) and the Local Variance (LV) of the images to get the coarse fusion maps. The course fusion maps are then processed using median and morphological filters. The weighted fusion technology is used to generate the fused image. Finally, image enhancement is achieved by adding a special kernel to the fused image to obtain the final fusion result. Experimental results on the publicly available datasets exhibited that the proposed research article obtains the best results in terms of noisy and low-contrast medical images. Overall, it achieves significant performance both qualitatively and quantitively when compared to other competing state-of-the-art methods.

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“…The integration of control and estimation techniques with machine learning, computational methods, and hardware innovation has catalyzed significant advancements across sectors, including smart microgrids, vehicle battery monitoring, and robotics. This synergy has not only enhanced the efficiency and adaptability of technological systems but also propelled forward innovations in healthcare diagnostics [1][2][3][4][5][6][7][8][9], bioinformatics [10][11][12][13][14], image processing [15][16][17][18][19][20][21][22][23][24], and robotics [25][26][27][28][29][30][31][32][33][34][35][36]. Breakthroughs range from multi-modal image classification for COVID-19 detection to AI-driven medical diagnostics and the optimization of IoT devices in vehicles.…”

Section: Introductionmentioning

confidence: 99%

The scientific footprint of SWIR detectors

Obaideen,

Faroukh,

Bonny

et al. 2024

Advanced Optics for Imaging Applications: UV Through LWIR IX

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This paper illuminates the scientific footsteps of the Short-Wave Infrared (SWIR) detectors, tracing the journey of the growth and development in this critical technology area from its early days in 1969 to its swift ascent in 2023. Employing bibliometric information and VOSviewer examination, we unveil major tendencies, thematic clusters, and the dynamic swelling of the scientific publications, spotlighting the burgeoning interest and technological evolution of SWIR detectors. Our research pieces together the key stages of SWIR technology emergence, originating from the initial revelations, advances in material science and also expansion of application spheres from military surveillance to environmental monitoring and medical imaging. Thus, we shed a light on the tremendous sway of SWIR detectors on the optical engineering and also photonics, attributable to their distinct capabilities to resolve current challenges. The findings throw a lot of light on the maturation within the SWIR detector research, underlining its heightening significance in both scientific and also industrial applications. This paper is a very comprehensive overview of the SWIR detector landscape that elaborates the successes of the past and also the trajectory of the future for the researchers, practitioners, and policymakers journeying in this transformative field.

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Siamese networks and multi-scale local extrema scheme for multimodal brain medical image fusion

Cited by 30 publications

References 20 publications

Multi-modal image processing pipeline for NDE of structures and industrial assets

Multi-modal image processing pipeline for NDE of structures and industrial assets

A Robust Medical Image Fusion Based on Synthetic Focusing Degree Criterion and Special Kernel Set for Clinical Diagnosis

The scientific footprint of SWIR detectors

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