Medical Image Fusion in Wavelet and Ridgelet Domains: A Comparative Evaluation

1.1. Overview of Medical Image Fusion

‘Medical Image Fusion’ is the process of combining and merging complementary information into a single image from two or more source images to maximize the information content of images and minimize the distortion and artifacts in the resulting image (Dasarathy 2012). The complementary nature of medical imaging sensors of different modalities, (X-ray, Magnetic Resonance Imaging (MRI), Computed Tomography (CT)) have brought a great need of image fusion for the retrieval of relevant diagnostic information from medical images. The significance of fusion process is important for multimodal imaging modalities as images obtained from single modality provides only specific information; thus it is not feasible to get all the requisite information from image generated by single modality (Schoder et al. 2004; Nakamoto et al. 2009). To elaborate further, CT helps in accessing the extent of disease; yet it is limited in soft-tissue contrast, needed for differentiating tumors from scar tissues. On the other hand, MRI scores over CT in terms of soft tissue discrimination. This is necessary because the soft tissue contrast allows better visualization of tumors. This highlights the need towards the development of multimodality medical imaging sensors for extracting clinical information to explore the possibility of data reduction along with better visual representation. It is noteworthy here that the visualization of the region of interest can be further improved by application of non-linear contrast and edge enhancement techniques (Pandey et al., 2012, 2013; Bhateja et al., 2014).