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Fusion Of Multi-Modality Medical Images: A Fuzzy Approach

机译:多模态医学图像融合:一种模糊方法

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The advancements in technology have touched many domains and Medical Domain is one such beneficiary with advancements like Radiation Oncology, Real time imaging, 4-D respiratory gating etc. This paper deals with the Multi-Modality images and its fusion so that efficient, accurate and especially low cost high-end treatment is available to all. For Diagnostic and Treatment Planning, Medical images are the vital source of information. In this study we have employed the Harvard Database. The Medical images come with different modalities like CT, PET, MRI are medical images with different modality. These modalities are fused such that the best information is available in the fused image and to fulfill that, this paper puts forward Fuzzy Logic Inference system based image fusion. The proposed technique uses CT and MRI as input and the fusion is applied using Fuzzy Logic. The evaluation of output is done by the metrics: PSNR, SNR and MSE. The fused image attained through fuzzy logic is more informative when compared with the wavelet based fusion method.
机译:技术的进步已经触及许多领域,而医学领域就是诸如此类的受益者,例如放射肿瘤学,实时成像,4-D呼吸门控等。本文对多模态图像及其融合进行了研究,以使其高效,准确,实用。所有人都可以享受到特别是低成本的高端治疗。对于诊断和治疗计划,医学图像是信息的重要来源。在这项研究中,我们采用了哈佛数据库。医学图像具有不同的形式,如CT,PET,MRI,是具有不同形式的医学图像。这些模式被融合在一起,以便在融合图像中获得最佳信息,并为此而实现,本文提出了基于模糊逻辑推理系统的图像融合。所提出的技术使用CT和MRI作为输入,并使用模糊逻辑进行融合。输出评估通过以下指标完成:PSNR,SNR和MSE。与基于小波的融合方法相比,通过模糊逻辑获得的融合图像更具参考价值。

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