PROJECT TITLE :

Dwt based medical image fusion with maximum Local extreme - 2016

ABSTRACT:

In clinical applications fusion of images plays a vital role for best diagnosis. Here computed tomography(CT) image provides best info on denser tissues like bones and magnetic resonance image(MRI) provides higher info on soft tissues. The fusion of those images can offer higher info concerning the patient medical status. This paper aims to propose a completely unique algorithm to enhance the quality and quantity of an image using DWT. Here DWT preserve more detail in source pictures and any improves the standard of fused image. The Proposed algorithm uses approximation and detailed layer fusion rules and therefore the performance are comparing with the multi-level local extrema (MLE) methodology [one]. The analysis of the proposed technique has been performed with several sets of medical images. The Performances of fused multi-model images will be computed using PSNR (peak signal to noise ratio), MI (mutual information) and SSIM (structural similarity index). By observing the results, it shows that the proposed technique is efficient for fusion process.