基于提升小波变换的医学图像融合

目的 将不同模态的医学图像(如CT/MRI图像)进行科学融合,可以有效地丰富图像的信息,提高信息的利用效能,这对于医学临床诊断具有重要的理论研究意义和应用价值。方法 基于提升小波变换的特性,对多模态医学图像的融合算法进行研究。首先,对已配准的源图像进行多尺度分解,得到低频子带和多层高频子带;进而,根据低频子带的特点和各层高频子带的噪声含量不同,提出了低频子带系数采用基于区域平均能量的加权融合规则;对噪声含量较低的低层高频子带采用基于计盒分维法获取分维数,而对噪声含量较高的高层高频子带提出了基于区域梯度能量加权融合规则。结果 分别对灰度图像和彩色图像进行了大量融合实验,并分别在主观视觉特性及客观评价指标下对不同融合算法产生的融合图像的质量进行了分析对比,表明本文算法具有较好的边缘保持度。结论 实验结果表明,较现有算法产生的融合图像,应用本文融合算法得到的图像具有更丰富的信息,更能使图像灰度级分散,具有更良好的视觉特性和评价指标。

Medical image fusion based on lifting wavelet transform

Objective Medical image fusion is important in the field of disease diagnosis because it can improve the availability of information contained in images. Method To address the problem of multi-modal medical image fusion, this study proposes a new algorithm for medical image fusion based on the characteristics of lifting wavelet transform. First, the source multi-modal medical images after registration are decomposed into low and high frequency sub-bands by applying lifting wavelet transform. Second, image fusion rules are put forward according to the different features of the low and high frequency sub-bands. A fusion rule based on weighted region average energy is adopted for the low-frequency sub-band coefficients. For the high-frequency sub-band coefficients, the weighed box-counting method is applied in the fusion rules of low-rise sub-bands with low noise content, and the fusion rule of the weighed local area energy of the image gradient is used for high-rise sub-bands with high noise content. Result Several experiments that compare the previous with new medical image fusion algorithms are conducted for gray and color images. The experiment results are then analyzed in terms of visual quality and objective evaluation. The proposed algorithm can effectively preserve edge information. Conclusion This study demonstrates that the proposed algorithm based on lifting wavelet transform can effectively preserve a large amount of information and significantly improve the performance of fusion images in terms of visual quality and objective evaluation index.