Combining the information from two or more images into a solitary image is known as image combination that can keep down all the essential features of the first images. The principal target of image combination is to generate an image which depicts a scene preferred or considerably higher over any single image concerning some important properties giving a useful image. These combination techniques are most vital in diagnosing and treating growth in therapeutic fields. This article focuses on the development and analysis of medical image enhancement using principal component-based nonsubsampled contourlet transform (PCA-NSCT) fusion methodology with comparison to various enhancement techniques using discrete wavelet transform (DWT), stationary wavelet transform (SWT), fast discrete curvelet transform (F-DCT), NSCT-based fusion algorithms. Further, the quality of the fused image also evaluated using a set of quality metrics which is known as image quality assessment (IQA) such as peak signal-to-noise ratio (PSNR), correlation coefficient (CC), entropy (E), and root mean square error (RMSE).