Multimodal medical image fusion in NSST domain with structural and spectral features enhancement

Sajid Ullah Khan; Fahim Khan; Shahid Ullah; YoungmoonLee; Sami ulQudoos; Bumshik Lee

doi:10.1016/j.heliyon.2023.e17334

Multimodal medical image fusion in NSST domain with structural and spectral features enhancement

Heliyon. 2023 Jun 16;9(6):e17334. doi: 10.1016/j.heliyon.2023.e17334. eCollection 2023 Jun.

Authors

Sajid Ullah Khan¹, Fahim Khan², Shahid Ullah³, YoungmoonLee⁴, Sami ulQudoos⁵, Bumshik Lee¹

Affiliations

¹ Multimedia Information Processing Lab, Department of Information and Communication Engineering, Chosun University, Gwangju, South Korea.
² Department of Computer Engineering, Gachon University, Seongnam-si13120, South Korea.
³ Faculty of Engineering, University Malaysia Sarawak, Malaysia.
⁴ Department of Robotics, Hanyang University, Ansan-si 15558, South Korea.
⁵ Department of Computer Science, Comsats University, WahCantt, Pakistan.

Abstract

For the past 25 years, medical imaging has been extensively used for clinical diagnosis. The main difficulties in medicine are accurate disease recognition and improved therapy. Using a single imaging modality to diagnose disease is challenging for clinical personnel. In this paper, a novel structural and spectral feature enhancement method in NSST Domain for multimodal medical image fusion (MMIF) is proposed. Initially, the proposed method uses the Intensity, Hue, Saturation (IHS) method to generate two pairs of images. The input images are then decomposed using the Non-Subsampled Shearlet Transform (NSST) method to obtain low frequency and high frequency sub-bands. Next, a proposed Structural Information (SI) fusion strategy is employed to Low Frequency Sub-bands (LFS's). It will enhance the structural (texture, background) information. Then, Principal Component Analysis (PCA) is employed as a fusion rule to High Frequency Sub-bands (HFS's) to obtain the pixel level information. Finally, the fused final image is obtained by employing inverse NSST and IHS. The proposed algorithm was validated using different modalities containing 120 image pairs. The qualitative and quantitative results demonstrated that the algorithm proposed in this research work outperformed numerous state-of-the-art MMIF approaches.

Keywords: Multimodal medical image fusion; Non-subsampled Shearlet transform; Principle component analysis; Structural and spectral features.