Research Article Open Access

A Robust Medical Image Fusion Based on Synthetic Focusing Degree Criterion and Special Kernel Set for Clinical Diagnosis

Dawa Chyophel Lepcha1, Bhawna Goyal1, Ayush Dogra2, Ahmed Alkhayyat3, Sanjeev Kumar Shah4 and Vinay Kukreja2
  • 1 Department of ECE and UCRD, Chandigarh University, Mohali, Punjab, 140413, India
  • 2 Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
  • 3 Department of Engineering, College of Technical Engineering, Islamic University, Najaf, Iraq
  • 4 Department of ECE, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, India

Abstract

Medical imaging has been widely used to diagnose diseases over the last two decades. Medical professionals still struggle to diagnose diseases using a single modality since there is a shortage of data in this domain. Therefore, images of specific organs with diseases from a variety of medical imaging systems can be combined using a technique called image fusion. Medical image fusion has prompted immense requisite applications in clinical applications in recent years. However, the fusion of medical images still facing a variety of challenges due to the input image quality. Protonema such as noise and low-contrast input medical images significantly reduces the quality of medical images. Still, recent image fusion methods are not significantly able to address the image quality problems. In order to address these problems, this study introduces a novel image fusion method that provides effective fusion performance even if the input images are noisy or low-contrast by combining the benefits of synthetic focusing degree criterion with a special kernel set. First, a Gaussian Curvature Filter (GCF) is used to sharpen the images in order to perform a Salient Feature Extraction (SFE). Then, we create a synthetic Focusing Degree Condition (FDC) that combines the Spatial Frequency (SF) and the Local Variance (LV) of the images to get the coarse fusion maps. The course fusion maps are then processed using median and morphological filters. The weighted fusion technology is used to generate the fused image. Finally, image enhancement is achieved by adding a special kernel to the fused image to obtain the final fusion result. Experimental results on the publicly available datasets exhibited that the proposed research article obtains the best results in terms of noisy and low-contrast medical images. Overall, it achieves significant performance both qualitatively and quantitively when compared to other competing state-of-the-art methods.

Journal of Computer Science
Volume 20 No. 4, 2024, 389-399

DOI: https://doi.org/10.3844/jcssp.2024.389.399

Submitted On: 7 August 2023 Published On: 6 February 2024

How to Cite: Lepcha, D. C., Goyal, B., Dogra, A., Alkhayyat, A., Shah, S. K. & Kukreja, V. (2024). A Robust Medical Image Fusion Based on Synthetic Focusing Degree Criterion and Special Kernel Set for Clinical Diagnosis. Journal of Computer Science, 20(4), 389-399. https://doi.org/10.3844/jcssp.2024.389.399

  • 263 Views
  • 114 Downloads
  • 0 Citations

Download

Keywords

  • Medical Imaging
  • Image Fusion
  • Research
  • Gaussian Curvature Filter (GCF)
  • Spatial Frequency
  • Technology