Image Fusion for Enhancing Diagnostic Accuracy in Medical Imaging Using FDCT, HSV, and DC-PCNN

Muhammad Shafiq; Waeal J. Obidallah; Qun Wang; Tahir Kamal; Anas Bilal

doi:10.5566/ias.3781

Authors

Muhammad Shafiq School of Computer Science, Shandong Xiehe University, Jinan, China.
Waeal J. Obidallah College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
Qun Wang School of Computer Science, Shandong Xiehe University, Jinan, China.
Tahir Kamal School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China.
Anas Bilal College of Information Science & Technology, Hainan Normal University, Haikou, China.

DOI:

https://doi.org/10.5566/ias.3781

Keywords:

diagnostic imaging, Dual-Channel Pulse-Coupled Neural Network, FDCT, HSV color model, image fusion model, image processing, medical image fusion, medical imaging, MRI

Abstract

Image-based disease diagnosis and treatment have long been used to enhance human health and well-being. Recent advancements in imaging and image processing technologies have spurred significant research in this field, leading to improvement in image modalities that allow better representation of features, ultimately helping healthcare practitioners make more precise diagnoses and treatment plans. Various medical image modalities, including X-rays, Positron Emission Tomography (PET), Computer Tomography (CT), and Magnetic Resonance Imaging (MRI), are widely used. Each modality has its strengths; for instance, MRI provides detailed anatomical information, while PET reveals functional and metabolic data. However, using these modalities separately limits diagnostic potential, as they cannot provide a comprehensive view of both structure and function. The Image Fusion Model (IFM) is designed to overcome this limitation by combining features from both images. Existing IFMs often face challenges such as the loss of high-frequency details, insufficient retention of structural data, and poor preservation of functional information. The proposed model integrates the Fast Discrete Cosine Transform (FDCT), the HSV color model, and a Dual-Channel Pulse-Coupled Neural Network (DC-PCNN) to address these challenges. The model was evaluated using eight MRI and PET image pairs from the Harvard Medical School Image Database, demonstrating competitive performance in terms of spatial frequency (SF), mutual information (MI), image entropy (IE), image quality index (IQI), and margin information retention (MIR). The results show that the proposed model outperforms traditional methods, particularly in preserving high-frequency details while maintaining both structural and functional data integrity.

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