Medical Image Fusion

Abstract

Image fusion is a pivotal technique in medical imaging, particularly in combining complementary information from different modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) to enhance diagnostic accuracy. This paper presents a comprehensive review of CT and MRI image fusion methodologies, focusing on the integration of Wavelet Transform (WT) and Log Gabor Filter with Gaussian and Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement techniques. The Wavelet Transform has gained significant attention due to its multi-resolution analysis capability, enabling the decomposition of images into different frequency bands. Log Gabor filters, on the other hand, excel in capturing texture information with orientation selectivity. Integrating these techniques offers a synergistic approach for extracting and preserving relevant features from CT and MRI images. Moreover, Gaussian and CLAHE enhancement techniques are employed to improve the contrast and visibility of anatomical structures in both CT and MRI images. Gaussian filtering smoothens the images while preserving edges, whereas CLAHE enhances local contrast by adaptive histogram equalization. This review discusses the theoretical foundations, methodologies, and applications of CT and MRI image fusion techniques employing Wavelet Transform and Log Gabor Filter with Gaussian and CLAHE enhancement. Furthermore, the review highlights the significance of image quality assessment metrics in evaluating the performance of fusion techniques, including objective metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSI), and subjective evaluation through visual inspection by medical experts. At last, the integration of Wavelet Transform and Log Gabor Filter with Gaussian and CLAHE enhancement techniques holds immense potential in CT and MRI image fusion, offering enhanced diagnostic capabilities and improved clinical decision-making in medical imaging applications. Future research directions and challenges in this field are also discussed to stimulate further advancements in image fusion methodologies.