多模态医学图像融合:一种模糊方法

Harmeet Kaur, Satish Kumar
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引用次数: 3

摘要

技术的进步已经触及了许多领域,医学领域就是这样一个受益者,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,以期为所有人提供高效、准确、特别是低成本的高端治疗。对于诊断和治疗计划,医学图像是重要的信息来源。在这项研究中,我们使用了哈佛数据库。医学图像有不同的模态,如CT、PET、MRI是不同模态的医学图像。将这些模式进行融合,使融合后的图像能获得最佳的信息,为实现这一目标,本文提出了基于模糊逻辑推理的图像融合系统。该方法以CT和MRI为输入,采用模糊逻辑进行融合。输出的评价是由指标完成:PSNR, SNR和MSE。与基于小波的融合方法相比,模糊逻辑得到的融合图像信息量更大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fusion Of Multi-Modality Medical Images: A Fuzzy Approach
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.
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