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Multimedia Tools and Applications 期刊征稿
期刊：Multimedia Tools and Applications
專刊：Special Issue on Advances in Computational Intelligence for Multimodal Biomedical Imaging (ATSIP 2017)
領(lǐng)域：計算機圖形學(xué)與多媒體
難度：★★★
CCF分類：C類
影響因子：1.331
全文截稿：2017-09-15
網(wǎng)址：http://www.springer.com/journal/11042/about
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Nowadays, many modalities such as CT, X-ray scanners, MRI/fMRI, PET scan, etc. generate complex images with a large amount of data that are becoming extremely difficult to handle. This growing mass of data requires new strategies for the diagnosis of diseases and new therapies.
In recent years, particular attention has been paid to computational intelligence methods in multimodal biomedical imaging applications. Inspired by artificial intelligence, mathematics, biology and other fields, these methods can find relationships between different categories of this complex data and provide a set of tools for the diagnosis and monitoring of the disease.
The topics of this special issue include the following computational intelligence based methods for multimodal biomedical
imaging systems and applications, but are not limited to:
- Bio-inspired methods and neural modelling
- Learning theory for biomedical image processing
- Machine, deep and manifold learning for biomedical imaging systems
- Pattern recognition and big data in medical imaging systems methodologies
- Compressive sensing and time series analysis
- Evolutionary algorithms and metaheuristics optimization for medical imaging
- Neural networks and genetic algorithms for biomedical imaging systems
- Applications (diagnosis, classification, denoising, registration, segmentation, security, augmented reality-aided surgery, brain-computer interface etc ...)
- Modalities (X-ray, CT, MRI, fMRI, PET scan etc ...)
