Seal Ayan, Bhattacharjee Debotosh, Nasipuri Mita, Rodríguez-Esparragón Dionisio, Menasalvas Ernestina, Gonzalo-Martin Consuelo
Department of Computer Science and Engineering, PDPM IIITDM Jabalpur, Jabalpur, India.
Department of Computer Science and Engineering, Jadavpur University, Kolkata, India.
Int J Numer Method Biomed Eng. 2018 Mar;34(3). doi: 10.1002/cnm.2933. Epub 2017 Dec 1.
New image fusion rules for multimodal medical images are proposed in this work. Image fusion rules are defined by random forest learning algorithm and a translation-invariant à-trous wavelet transform (AWT). The proposed method is threefold. First, source images are decomposed into approximation and detail coefficients using AWT. Second, random forest is used to choose pixels from the approximation and detail coefficients for forming the approximation and detail coefficients of the fused image. Lastly, inverse AWT is applied to reconstruct fused image. All experiments have been performed on 198 slices of both computed tomography and positron emission tomography images of a patient. A traditional fusion method based on Mallat wavelet transform has also been implemented on these slices. A new image fusion performance measure along with 4 existing measures has been presented, which helps to compare the performance of 2 pixel level fusion methods. The experimental results clearly indicate that the proposed method outperforms the traditional method in terms of visual and quantitative qualities and the new measure is meaningful.
本研究提出了用于多模态医学图像的新图像融合规则。图像融合规则由随机森林学习算法和平移不变的小波变换（AWT）定义。所提出的方法有三个步骤。首先，使用AWT将源图像分解为近似系数和细节系数。其次，随机森林用于从近似系数和细节系数中选择像素，以形成融合图像的近似系数和细节系数。最后，应用逆AWT重建融合图像。所有实验均在一名患者的198层计算机断层扫描和正电子发射断层扫描图像上进行。基于Mallat小波变换的传统融合方法也已在这些切片上实现。提出了一种新的图像融合性能度量以及4种现有度量，这有助于比较两种像素级融合方法的性能。实验结果清楚地表明，所提出的方法在视觉和定量质量方面优于传统方法，并且新度量是有意义的。
