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首页> 外文期刊>International Journal of Rough Sets and Date Analysis >Multimodality Medical Image Fusion using M-Band Wavelet and Daubechies Complex Wavelet Transform for Radiation Therapy
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Multimodality Medical Image Fusion using M-Band Wavelet and Daubechies Complex Wavelet Transform for Radiation Therapy

机译:使用M波段小波和Daubechies复数小波变换的多模态医学图像融合用于放射治疗

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摘要

The process of enriching the important details from various modality medical images by combining them into single image is called multimodality medical image fusion. It aids physicians in terms of better visualization, more accurate diagnosis and appropriate treatment plan for the cancer patient. The combined fused image is the result of merging of anatomical and physiological variations. It allows accurate localization of cancer tissues and more helpful for estimation of target volume for radiation. The details from both modalities (CT and MRI) are extracted in frequency domain by applying various transforms and combined them using variety of fusion rules to achieve the best quality of images. The performance and effectiveness of each transform on fusion results is evaluated subjectively as well as objectively. The fused images by algorithms in which feature extraction is achieved by M-Band Wavelet Transform and Daubechies Complex Wavelet Transform are superior over other frequency domain algorithms as per subjective and objective analysis.
机译:通过将各种模态医学图像中的重要细节组合成单个图像来丰富其重要细节的过程称为多模态医学图像融合。它可以帮助医生为癌症患者提供更好的可视化,更准确的诊断和适当的治疗计划。合并的融合图像是解剖和生理变化合并的结果。它可以精确定位癌组织,并且更有助于估算放射线的目标体积。通过应用各种变换在频域中提取两种模态(CT和MRI)的细节,并使用各种融合规则将它们组合起来,以实现最佳图像质量。主观和客观地评估每个融合结果的性能和有效性。在主观和客观分析方面,通过M波段小波变换和Daubechies复数小波变换实现特征提取的算法所融合的图像优于其他频域算法。

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