Current trends in medical image registration and fusion

El-Gamal, Fatma El-Zahraa A.; Elmogy, Mohammed; Atwan, Ahmed

doi:10.1016/j.eij.2015.09.002

Cited by 178 publications

(63 citation statements)

References 76 publications

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“…This process is often based on predefined similarity criteria such as landmark and edge-based measures. In addition to the computational power and time consumed by these predefined feature-based methods, some are sensitive to initializations, chosen similarity features and the reference image 78 . Deep learning methods could handle complex tissue deformations through more advanced non-rigid registration algorithms while providing better motion compensation for temporal image sequences.…”

Section: Impact On Oncology Imagingmentioning

confidence: 99%

Artificial intelligence in radiology

et al. 2018

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Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this O pinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

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Section: Impact On Oncology Imagingmentioning

confidence: 99%

Artificial intelligence in radiology

et al. 2018

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“…To allow comparison between different temporal bone specimens, knowing that the mastoid air cell system can vary tremendously in shape, image registration needs to be done. As briefly introduced in [23], image registration can be defined as the process of mapping input images with a reference image. Indeed, when the number of bone specimens available is low (<10), a permutation approach can be used where image registration between two temporal bone specimens is performed many times.…”

Section: Technical Aspectsmentioning

confidence: 99%

Structural properties of the mastoid using image analysis and visualization

Cros¹

2017

Linköping Studies in Science and Technology. Dissertations

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Volume rendering of a 3D shape analysis where medial balls are fitting the mastoid air cell system of a human temporal bone specimen, overlaid over a manually cropped version of the original data. The different colours represent the different size classes: from very small in dark blue to vary large in purple. The bone is rendered with a modified transfer function so as to reveal the micro-channels.Copyright © 2017 Olivier Cros, unless otherwise noted. AbstractThe mastoid, located in the temporal bone, houses an air cell system whose cells have a variation in size that can go far below current conventional clinical CT scanner resolution. Therefore, the mastoid air cell system is only partially represented in a clinical CT scan. Where the conventional clinical CT scanner lacks level of minute details, micro-CT scanning provides an overwhelming amount of fine details. The temporal bone being one of the most complex in the human body, visualization of micro-CT scanning of this bone awakens the curiosity of the experimenter, especially with the correct visualization settings.This thesis first presents a statistical analysis determining the surface area to volume ratio of the mastoid air cell system of human temporal bone, from micro-CT scanning using methods previously applied for conventional clinical CT scans. The study compared current results with previous studies, with successive downsampling the data down to a resolution found in conventional clinical CT scans. The results from the statistical analysis showed that all the small mastoid air cells, that cannot be detected in conventional clinical CT scans, do heavily contribute to the estimation of the surface area, and in consequence to the estimation of the surface area to volume ratio by a factor of about 2.6. Such a result further strengthens the idea of the mastoid to play an active role in pressure regulation and gas exchange.Discovery of micro-channels through specific use of a non-traditional transfer function was then reported, where a qualitative and a quantitative preanalysis were performed and reported. To gain more knowledge about these micro-channels, a local structure tensor analysis was applied where structures are described in terms of planar, tubular, or isotropic structures. The results from this structural tensor analysis suggest these micro-channels to potentially be part of a more complex framework, which hypothetically would provide a separate blood supply for the mucosa lining the mastoid air cell system.The knowledge gained from analysing the micro-channels as locally providing blood to the mucosa, led to the consideration of how inflammation of the mucosa could impact the pneumatization of the mastoid air cell system. Though very primitive, a 3D shape analysis of the mastoid air cell system was carried out. The mastoid air cell system was first represented in a compact form through a medial axis, from which medial balls could be used. The medial balls, representative of how large the mastoid air cells can be locally, were used in two co...

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“…Al. [10] [2015] again considered medical images with registration and fusion. They presented the current challenges with medical image registration as well as fusion.…”

Section: Introductionmentioning

confidence: 99%

Multi-Focus Image Fusion using Non-Local Mean Filtering and Stationary Wavelet Transform

Joshi¹,

Joshi²,

Diwakar³

et al. 2019

IJITEE

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Today’s research era, image fusion is a actual step by step procedure to develop the visualization of any image. It integrates the essential features of more than a couple of images into a individual fused image without taking any artifacts. Multifocus image fusion has a vital key factor in fusion process where it aims to increase the depth of field using extracting focused part from different multiple focused images. In this paper multi-focus image fusion algorithm is proposed where non local mean technique is used in stationary wavelet transform (SWT) to get the sharp and smooth image. Non-local mean function analyses the pixels belonging to the blurring part and improves the image quality. The proposed work is compared with some existing methods. The results are analyzed visually as well as using performance metrics.

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Current trends in medical image registration and fusion

Cited by 178 publications

References 76 publications

Artificial intelligence in radiology

Artificial intelligence in radiology

Structural properties of the mastoid using image analysis and visualization

Multi-Focus Image Fusion using Non-Local Mean Filtering and Stationary Wavelet Transform

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