ArticleFrontiers in public health2022
Digital Analysis of Smart Registration Methods for Magnetic Resonance Images in Public Healthcare.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Synthesis of Amyloid Images Using a Generative Adversarial Network from 2-DimensionalJournal of nuclear medicine technology · 2026Article
- Constructing the KOR152 Korean Young Adult Brain Atlas Utilizing the State-of-the-Art Method for the Age-Specific Population.Psychiatry investigation · 2024Article
- A Straightforward Bifurcation Pattern-Based Fundus Image Registration Method.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors.
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Abstract
Brain development and atrophy accompany people's life. Brain development diseases, such as autism and Alzheimer's disease, affect a large part of the population. Analyzing brain development is very important in public healthcare, and image registration is essential in medical brain image analysis. Many previous studies investigate registration accuracy by the "ground truth" dataset, marker-based similarity calculation, and expert check to find the best registration algorithms. But the evaluation of image registration technology only at the accuracy level is not comprehensive. Here, we compare the performance of three publicly available registration techniques in brain magnetic resonance imaging (MRI) analysis based on some key features widely used in previous MRI studies for classification and detection tasks. According to the analysis results, SPM12 has a stable speed and success rate, and it always works as a guiding tool for newcomers to medical image analysis. It can preserve maximum contrast information, which will facilitate studies such as tumor diagnosis. FSL is a mature and widely applicable toolkit for users, with a relatively stable success rate and good performance. It has complete functions and its function-based integrated toolbox can meet the requirements of different researchers. AFNI is a flexible and complex tool that is more suitable for professional researchers. It retains most details in medical image analysis, which makes it useful in fine-grained analysis such as volume estimation. Our study provides a new idea for comparing registration tools, where tool selection strategy mainly depends on the research task in which the selected tool can leverage its unique advantages.
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