A novel approach using the local energy function and its variations for medical image fusion

ABSTRACT

Medical image fusion plays a pivotal role in facilitating clinical diagnosis. However, the quality of input medical images may be marred by noise, low contrast, and lack of sharpness, presenting numerous challenges for medical image synthesis algorithms. Additionally, several fusion rules may degrade the brightness and contrast of the fused image. To this end, this paper presents a novel image synthesis approach to tackle the aforementioned issues. First, the input images undergo pre-processing to enhance their quality. Subsequently, we introduce the three-layer image decomposition (TLID) technique, which decomposes an image into three distinct layers: the base layer ($L_B$), the small-scale structure layer ($L_{SS}$), and the large-scale structure layer ($L_{LS}$). Next, we synthesize the base layers utilizing adaptive rules based on the Marine predators algorithm (MPA), ensuring that the output image is not degraded. Finally, we propose an efficient synthesis method for $L_{SS}$ and $L_{LS}$ layers, based on combining the local energy function with its variations. This fusion technique preserves the intricate details present in the original image. We evaluated our approach on 156 medical images using six evaluation metrics and compared it with seven state-of-the-art image synthesis techniques. Our results demonstrate that our method successfully generates high-quality output images and preserves detailed information throughout the image synthesis process.

KEYWORDS:

• Marine predators algorithm (MPA)
• three-layer image decomposition (TLID)
• rolling guidance filter (RGF)
• weighted mean curvature filter (WMCF)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Phu-Hung Dinh

Phu-Hung Dinh was born in Vietnam. Currently, he is a lecturer at the Faculty of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam. His research interests include signal processing, image processing, meta-heuristic optimization, and computer vision. Furthermore, he is also a reviewer for many prestigious journals, such as Expert systems with applications, Artificial intelligence review, Computers in biology and medicine, Biomedical signal processing & control, ISA transactions, BMC Bioinformatics, Applied Intelligence, Soft Computing, Imaging science journal, and Journal of Supercomputing.