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 168 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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“…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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“…In particular, we treat the Medical Image Registration (MIR) case of different modalities. The big range of applications in medical imaging, goes from computer assisted diagnosis to computer aided therapy and surgery [2]. MIR is treated as an optimization problem with the goal of finding the spatial mapping that will bring a moving image into alignment with a fixed image.…”

mentioning

confidence: 99%

3D Medical Image Registration using Mutual Information and Scatter Search

Gimenez¹,

Lopez²,

Cespedes³

et al. 2017

JCIS

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Image Registration (IR) is a challenge that arises in many image processing applications when several images must be aligned. In particular, we treat the Medical Image Registration (MIR) case of different modalities. The big range of applications in medical imaging, goes from computer assisted diagnosis to computer aided therapy and surgery [2]. MIR is treated as an optimization problem with the goal of finding the spatial mapping that will bring a moving image into alignment with a fixed image. Deterministic algorithms are mainly used to solve it, together with some stochastic ones. A drawback of the latter is that many of them become stuck in local optima, especially in multimodal registration with several parameters [3], [4]. In this context, one of the most popular metric approaches is the Mutual Information based methods using deterministic optimization algorithms to compute the cost function with the mentioned problems. This work is aimed to overcome this disadvantage using the Scatter Search optimization algorithm [5] with the mutual information approach, proposed by Mattes et. al. [8]. The optimizer was tested and contrasted with RegularStep Descent Optimizer [9] and the 1+1 evolutionary algorithm [12]. Multimodal, rigid, 3D/3D, image registration of tomographic brain images was performed over a database available in RIRE 2 project. It was used the Insight Segmentation and Registration Toolkit (ITK) 3 , which is a set of libraries in C++ designed for the development of registration methods [10]. Qualitative and quantitative validation of the results are satisfactory, the results proves the accuracy and applicability of the proposal method comparing with conventional methods, and not being stuck in local optima.

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Current trends in medical image registration and fusion

Cited by 168 publications

References 76 publications

Artificial intelligence in radiology

Artificial intelligence in radiology

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

3D Medical Image Registration using Mutual Information and Scatter Search

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