Fusion of Multi Modal Lumber Spine Scans using Wavelet and Convolutional Neural Network

Abstract

Multiple medical images of different modalities are fused together to generate a new more informative image thereby reducing the treatment planning time of medical practitioners. In recent years, wavelets and deep learning methods have been widely used in various image processing applications. In this study, we present convolutional neural network and wavelet based fusion of MR and CT images of lumber spine to generate a single image which comprises all the important features of MR and CT images. Both CT and MR images are first decomposed into detail and approximate coefficients using wavelets. Then the corresponding detail and approximate coefficients are fused using convolutional neural network framework. Inverse wavelet transform is then used to generate fused image. The experimental results indicate that the proposed approach achieves good performance as compared to conventional methods.