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Multimodal Sensor Medical Image Fusion Based on Type-2 Fuzzy Logic in NSCT Domain

机译:NSCT域中基于2型模糊逻辑的多模态传感器医学图像融合

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摘要

Multimodal medical image fusion plays a vital role in different clinical imaging sensor applications. This paper presents a novel multimodal medical image fusion method that adopts a multiscale geometric analysis of the nonsubsampled contourlet transform (NSCT) with type-2 fuzzy logic techniques. First, the NSCT was performed on preregistered source images to obtain their high- and low-frequency subbands. Next, an effective type-2 fuzzy logic-based fused rule is proposed for fusion of the high-frequency subbands. In the presented fusion approach, the local type-2 fuzzy entropy is introduced to automatically select high-frequency coefficients. However, for the low-frequency subbands, they were fused by a local energy algorithm based on the corresponding image’s local features. Finally, the fused image was constructed by the inverse NSCT with all composite subbands. Both subjective and objective evaluations showed better contrast, accuracy, and versatility in the proposed approach compared with state-of-the-art methods. Besides, an effective color medical image fusion scheme is also given in this paper that can inhibit color distortion to a large extent and produce an improved visual effect.
机译:多峰医学图像融合在不同的临床成像传感器应用中起着至关重要的作用。本文提出了一种新颖的多峰医学图像融合方法,该方法采用类型2模糊逻辑技术对非下采样轮廓波变换(NSCT)进行多尺度几何分析。首先,对预先注册的源图像执行NSCT,以获得其高频和低频子带。接下来,提出了一种有效的基于2型模糊逻辑的融合规则,用于高频子带的融合。在提出的融合方法中,引入了局部2型模糊熵来自动选择高频系数。但是,对于低频子带,它们是根据相应图像的局部特征通过局部能量算法进行融合的。最后,通过逆NSCT构造具有所有复合子带的融合图像。与最先进的方法相比,该方法在主观和客观评估上均显示出更好的对比度,准确性和多功能性。此外,本文还提出了一种有效的彩色医学图像融合方案,该方案可以在很大程度上抑制色彩失真并提高视觉效果。

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