A Review on Medical Image Registration Techniques
@article{Mambo2018ARO, title={A Review on Medical Image Registration Techniques}, author={Shadrack Maina Mambo and Karim D Djouani and Yskandar Hamam and Barend J. van Wyk and Patrick Siarry}, journal={World Academy of Science, Engineering and Technology, International Journal of Computer and Information Engineering}, year={2018}, volume={5}, pages={48-55} }
This paper discusses the current trends in medical image registration techniques and addresses the need to provide a solid theoretical foundation for research endeavours. Methodological analysis and synthesis of quality literature was done, providing a platform for developing a good foundation for research study in this field which is crucial in understanding the existing levels of knowledge. Research on medical image registration techniques assists clinical and medical practitioners in…
7 Citations
A Comprehensive Study of Medical Image Analysis
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The vital goal of this review is to identify the current state of the art of medical image analysis methods as a reference paradigm in order to accelerate the performance of existing methods.
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A comparative study of particle swarm optimization (PSO) and the grey wolf optimizer (GWO) for registration of a section of the human brain for human brain recognition is presented.
An overview of deep learning methods for image registration with focus on feature-based approaches
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A comparative performance analysis of convolutional neural networks that have shown good performance across feature extraction, matching and transformation estimation in featured-based registration is presented.
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An improved sine cosine algorithm (ISCA) resulting from the hybridization of the SCA and PSO algorithms to register the CT images of the lung of the people infected by COVID-19 is proposed.
OtoPair: Combining Right and Left Eardrum Otoscopy Images to Improve the Accuracy of Automated Image Analysis
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- 2021
A novel approach, called OtoPair, which uses paired eardrum images together rather than using a single eardrums image to classify them as ‘ normal’ or ‘abnormal’, and increases the accuracy from 78.7% (±0.1%) to 85.8% on a three-fold cross-validation method.
Solid Modelling for Manufacturing: From Voelcker's Boundary Evaluation to Discrete Paradigms
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Grey Wolf Optimizer for Multimodal Medical Image Registration
- Computer Science2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS)
- 2020
Through the obtained simulation results, it appears that the proposed approach achieves high-resolution and robust registration compared to other methods such as Particle Swarm optimization PSO and sine cosine algorithm (SCA).
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