Fusion of Multi Modal Lumber Spine Scans Using Wavelet and Convolutional Neural Network
Bhakti Palkar1, Dhirendra Mishra2

1Bhakti Palkar, Department of Computer Engineering, MPSTME, NMIMS,Mumbai, Mumbai, Maharashtra India.
2Dhirendra Mishra, Department of Computer Engineering, MPSTME, NMIMS, Mumbai, Maharashtra India.

Manuscript received on 30 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 1704-1709 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8476078919/19©BEIESP | DOI: 10.35940/ijitee.I8476.078919
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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.
keyword: Medical image fusion, Multi-modal image fusion, Wavelets, Deep Leaning, Convolutional Neural Network.

Scope of the Article: Deep Leaning