Open Access

ARTICLE

Medical Image Fusion Based on Anisotropic Diffusion and Non-Subsampled Contourlet Transform

Bhawna Goyal^1,*, Ayush Dogra², Rahul Khoond¹, Dawa Chyophel Lepcha¹, Vishal Goyal³, Steven L. Fernandes⁴

1 Department of ECE, Chandigarh University, Mohali, 140413, Punjab, India
2 Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
3 Department of Electronics and Communication Engineering, GLA University, Mathura, India
4 Department of Computer Science, Design and Journalism, Creighton University, NE, USA

* Corresponding Author: Bhawna Goyal. Email:

(This article belongs to the Special Issue: Susceptibility to Adversarial Attacks and Defense in Deep Learning Systems)

Computers, Materials & Continua 2023, 76(1), 311-327. https://doi.org/10.32604/cmc.2023.038398

Received 11 December 2022; Accepted 20 April 2023; Issue published 08 June 2023

Abstract

The synthesis of visual information from multiple medical imaging inputs to a single fused image without any loss of detail and distortion is known as multimodal medical image fusion. It improves the quality of biomedical images by preserving detailed features to advance the clinical utility of medical imaging meant for the analysis and treatment of medical disorders. This study develops a novel approach to fuse multimodal medical images utilizing anisotropic diffusion (AD) and non-subsampled contourlet transform (NSCT). First, the method employs anisotropic diffusion for decomposing input images to their base and detail layers to coarsely split two features of input images such as structural and textural information. The detail and base layers are further combined utilizing a sum-based fusion rule which maximizes noise filtering contrast level by effectively preserving most of the structural and textural details. NSCT is utilized to further decompose these images into their low and high-frequency coefficients. These coefficients are then combined utilizing the principal component analysis/Karhunen-Loeve (PCA/KL) based fusion rule independently by substantiating eigenfeature reinforcement in the fusion results. An NSCT-based multiresolution analysis is performed on the combined salient feature information and the contrast-enhanced fusion coefficients. Finally, an inverse NSCT is applied to each coefficient to produce the final fusion result. Experimental results demonstrate an advantage of the proposed technique using a publicly accessible dataset and conducted comparative studies on three pairs of medical images from different modalities and health. Our approach offers better visual and robust performance with better objective measurements for research development since it excellently preserves significant salient features and precision without producing abnormal information in the case of qualitative and quantitative analysis.

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