Al-Azzawi Nemir, Sakim Harsa Amylia Mat, Abdullah Ahmed K Wan, Ibrahim Haidi
School of Electrical and Electronic Engineering, USM, Malaysia.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:5813-6. doi: 10.1109/IEMBS.2009.5335180.
We present an efficient method for the fusion of medical captured images using different modalities that enhances the original images and combines the complementary information of the various modalities. The contourlet transform has mainly been employed as a fusion technique for images obtained from equal or different modalities. The limitation of directional information of dual-tree complex wavelet (DT-CWT) is rectified in dual-tree complex contourlet transform (DT-CCT) by incorporating directional filter banks (DFB) into the DT-CWT. The DT-CCT produces images with improved contours and textures, while the property of shift invariance is retained. To improve the fused image quality, we propose a new method for fusion rules based on principle component analysis (PCA) which depend on frequency component of DT-CCT coefficients (contourlet domain). For low frequency components, PCA method is adopted and for high frequency components, the salient features are picked up based on local energy. The final fusion image is obtained by directly applying inverse dual tree complex contourlet transform (IDT-CCT) to the fused low and high frequency components. The experimental results showed that the proposed method produces fixed image with extensive features on multimodality.
我们提出了一种高效的方法，用于融合使用不同模态捕获的医学图像，该方法可增强原始图像并结合各种模态的互补信息。轮廓波变换主要被用作从相同或不同模态获得的图像的融合技术。通过将方向滤波器组（DFB）并入双树复小波变换（DT-CWT），在双树复轮廓波变换（DT-CCT）中纠正了双树复小波变换（DT-CWT）方向信息的局限性。DT-CCT产生具有改进轮廓和纹理的图像，同时保留了平移不变性的特性。为了提高融合图像的质量，我们基于主成分分析（PCA）提出了一种新的融合规则方法，该方法依赖于DT-CCT系数（轮廓波域）的频率分量。对于低频分量，采用PCA方法，对于高频分量，基于局部能量提取显著特征。通过直接对融合的低频和高频分量应用逆双树复轮廓波变换（IDT-CCT）获得最终的融合图像。实验结果表明，所提出的方法在多模态上产生具有广泛特征的固定图像。
