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Multi-level fuzzy contourlet-based image fusion for medical applications

机译:基于多层模糊轮廓波的医学图像融合

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Multi-modal images fusion is one of the most truthful and useful diagnostic techniques in medical imaging system. This study proposes an image fusion system for medical engineering based on contourlet transform and multi-level fuzzy reasoning technique in which useful information from two spatially registered medical images is integrated into a new image that can be used to make clinical diagnosis and treatment more accurate. The system applies pixel-based fuzzy fusion rule to contourlet??s coefficients of high-frequency details and feature-based fuzzy fusion to its low-frequency approximations, which can help the development of sophisticated algorithms that consider not only the time cost but also the quality of the fused image. The developed fusion system eliminates undesirable effects such as fusion artefacts and loss of visually vital information that compromise their usefulness by means of taking into account the physical meaning of contourlet coefficients. The experimental results show that the proposed fusion system outperforms the existing fusion algorithms and is effective to fuse medical images from different sensors with applications in brain image processing.
机译:多模态图像融合是医学成像系统中最真实,最有用的诊断技术之一。这项研究提出了一种基于Contourlet变换和多级模糊推理技术的医学工程图像融合系统,该系统将来自两个空间记录的医学图像的有用信息集成到一个新图像中,可以使临床诊断和治疗更加准确。该系统将基于像素的模糊融合规则应用于轮廓波的高频细节系数,并将基于特征的模糊融合应用于低频近似,这有助于开发不仅考虑时间成本而且考虑时间因素的复杂算法。融合图像的质量。所开发的融合系统通过考虑轮廓波系数的物理含义,消除了诸如融合文物和视觉上至关重要的信息丢失之类的不良影响,这些信息损害了其实用性。实验结果表明,所提出的融合系统优于现有的融合算法,能够有效融合来自不同传感器的医学图像及其在脑图像处理中的应用。

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    《Image Processing, IET》 |2013年第7期|694-700|共7页
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