基于NSST域Frei-Chen掩模的医学图像融合

Padma Ganasala, A. D. Prasad
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引用次数: 6

摘要

医学图像融合将不同成像方式的信息呈现在一张合成图像中。融合影像为各种疾病的精确诊断、治疗计划和随访研究提供了更多的临床信息。提出了一种基于非下采样shearlet变换(NSST)域Frei-Chen算子的医学图像融合方法。在四个多模态医学图像数据集上对该方法进行了测试。对融合结果的目视和定量评价显示了所提出的图像融合方法的优越性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Medical Image Fusion based on Frei-Chen Masks in NSST Domain
Medical image fusion presents the information given by different imaging modalities in a single composite image. Fused image provides more clinical information useful for the precise diagnosis, treatment planning and follow-up studies of various diseases. A medical image fusion method based on Frei-Chen operators in nonsubsampled shearlet transform (NSST) domain is presented in this research paper. The proposed method is tested on four multimodality medical image datasets. The visual and quantitative evaluation of fusion results exhibited the superior performance of the proposed image fusion approach.
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