Title: Particle swarm optimisation aided weighted averaging fusion strategy for CT and MRI medical images

Authors: Madheswari Kanmani; Venkateswaran Narasimhan

Addresses: Department of Computer Science and Engineering, SSN College of Engineering, Chennai, India ' Department of Electronics and Communication Engineering, SSN College of Engineering, Chennai, India

Abstract: Multimodal medical image fusion is a technique that combines two or more images into a single output image in order to enhance the accuracy of clinical diagnosis. In this paper, a non-subsampled contourlet transform (NSCT) image fusion framework that combines CT and MRI images is proposed. The proposed method decomposes the source images into low and high frequency bands using NSCT and the information across the bands are combined using weighted averaging fusion rule. The weights are optimised by particle swarm optimisation (PSO) with an objective function that jointly maximises the entropy and minimises root mean square error to give improved image quality, which makes different from existing fusion methods in NSCT domain. The performance of the proposed fusion framework is illustrated using five sets of CT and MRI images and various performance metrics indicate that the proposed method is highly efficient and suitable for medical application in better decision making.

Keywords: image fusion; CT image; MRI image; non-subsampled contourlet transform; NSCT; particle swarm optimisation; PSO; weighted average fusion strategy.

DOI: 10.1504/IJBET.2019.102975

International Journal of Biomedical Engineering and Technology, 2019 Vol.31 No.3, pp.278 - 291

Received: 16 Nov 2016
Accepted: 23 Mar 2017
Published online: 14 Oct 2019 *

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