Open Access

ARTICLE

Multimodal Medical Image Registration and Fusion for Quality Enhancement

Muhammad Adeel Azam¹, Khan Bahadar Khan^2,*, Muhammad Ahmad³, Manuel Mazzara⁴

1 Department of Electronic Engineering, The Islamia University of Bahawalpur, 63100, Pakistan
2 Department of Telecommunication Engineering, The Islamia University of Bahawalpur, 63100, Pakistan
3 Department of Computer Science, National University of Computer and Emerging Sciences, Chiniot, 35400, Pakistan
4 Institute of Software Development and Engineering, Innopolis University, Innopolis, 420500, Russia

* Corresponding Author: Khan Bahadar Khan. Email:

(This article belongs to the Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)

Computers, Materials & Continua 2021, 68(1), 821-840. https://doi.org/10.32604/cmc.2021.016131

Received 24 December 2020; Accepted 24 January 2021; Issue published 22 March 2021

Abstract

For the last two decades, physicians and clinical experts have used a single imaging modality to identify the normal and abnormal structure of the human body. However, most of the time, medical experts are unable to accurately analyze and examine the information from a single imaging modality due to the limited information. To overcome this problem, a multimodal approach is adopted to increase the qualitative and quantitative medical information which helps the doctors to easily diagnose diseases in their early stages. In the proposed method, a Multi-resolution Rigid Registration (MRR) technique is used for multimodal image registration while Discrete Wavelet Transform (DWT) along with Principal Component Averaging (PCAv) is utilized for image fusion. The proposed MRR method provides more accurate results as compared with Single Rigid Registration (SRR), while the proposed DWT-PCAv fusion process adds-on more constructive information with less computational time. The proposed method is tested on CT and MRI brain imaging modalities of the HARVARD dataset. The fusion results of the proposed method are compared with the existing fusion techniques. The quality assessment metrics such as Mutual Information (MI), Normalize Cross-correlation (NCC) and Feature Mutual Information (FMI) are computed for statistical comparison of the proposed method. The proposed methodology provides more accurate results, better image quality and valuable information for medical diagnoses.

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