Title: Fusion of registered medical images using deep learning convolutional neural network with statistics based steered image filter

Authors: Suneetha Rikhari; Sandeep Jaiswal

Addresses: School of Engineering and Technology, Mody University of Science and Technology, Lakshmangarh, Rajasthan, 332311, India ' School of Engineering and Technology, Mody University of Science and Technology, Lakshmangarh, Rajasthan, 332311, India

Abstract: Medical image fusion technique plays an increasingly critical role in many clinical applications by deriving the complementary information from medical images with different modalities. In this, a novel MR and CT image fusion approach is proposed which utilises the deep learning convolutional neural networks (CNNs) with statistics based steered image filter (SSIF). In our method, a deep learning convolutional network is adopted to generate a weight map which integrates the pixel activity information from MR and CT images. The fusion process is conducted via SSIF fusion rule which computes the weights of obtained detail layers using image statistics. In addition, weighted average method is utilised to obtain the fused image. Further, proposed fusion algorithm is extended to applicable for RGB image fusion. Experimental results demonstrate that the proposed method can achieve promising results in terms of both visual quality and objective assessment.

Keywords: image registration; medical image fusion; CNNs; convolutional neural networks; steered image filter; image statistics and image quality metrics.

DOI: 10.1504/IJBRA.2021.117170

International Journal of Bioinformatics Research and Applications, 2021 Vol.17 No.3, pp.262 - 274

Received: 23 Nov 2018
Accepted: 03 Jun 2019
Published online: 20 Aug 2021 *

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