Image Fusion Based on Medical Images Using DWT and PCA Methods

Image fusion is the process of combining relevant information from a set of images into a single image in which the fused image contains more information than any of the input images. This technique improves the quality of data. Image fusion is one of the important re-processing steps in digital image reconstruction. Now-a-days, medical image fusion is one of the upcoming fields which helps in easy diagnostics and helps to bring down the time gap between the diagnosis of the disease and the treatment. In Magnetic Resonance Image (MRI), anatomy and soft tissues are visible and it has high spatial resolution. In Computed Tomography (CT) images bony structures appears brighter. Analysis is done to determine the image fusion algorithm which is more suitable for clinical diagnosis. Analysis is also done on image quality assessment parameters of image fusion. This paper presents image fusion techniques and image quality assessment parameters such as primitive fusion (Averaging Method, Select Maximum, and Select Minimum), Discrete Wavelet transform based fusion, Principal component analysis (PCA) based fusion etc. Comparison of all the techniques concludes the better approach for its future research