J. Srikanth et al Int. Journal of Engineering Research and Application www.ijera.com ISSN : 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.252-256 www.ijera.com 252 | Page Image Fusion Based On Wavelet Transform For Medical Diagnosis J. Srikanth*, C.N Sujatha** *(Department of Electronics and Communication Engineering, Sree Nidhi Institute of science and technology, Hyderabad-501301) ** (Department of Electronics and Communication Engineering, Sree Nidhi Institute of science and technology) ABSTRACT In the image fusion scheme presented in this paper, the wavelet transforms of the input images are appropriately combined, the new image is obtained by taking the inverse wavelet transform of the fused wavelet co-efficients. The idea is to improve the image content by fusing images like computer tomography (CT) and magnetic resonance imaging (MRI) images, so as to provide more information to the doctor and clinical treatment planning system. This paper aims to demonstrate the application of wavelet transformation to multi- modality medical image fusion. This work covers the selection of wavelet function, the use of wavelet based fusion algorithms on medical image fusion of CT and MRI, implementation of fusion rules and the fusion image quality evaluation. The fusion performance is evaluated on the basis of the root mean square error (RMSE). Keywords - Medical image fusion, Multimodality images, Wavelet transforms, Fusion rules. I. INTRODUCTION 1.1 About Image fusion Image fusion is the process of combining relevant information from two or more images into a single image. The resulting image will be more informative than any of the input images. Image fusion involves two or more images to attain the most useful features for some specific applications. For Instance, doctors can annually combine the CT and MRI medical images of a patient with a tumour to make a more accurate diagnosis, but it is inconvenient and tedious to finish this job. And more importantly, using the same images, doctors with different experiences make inconsistent decisions. Thus, it is necessary to develop the efficiently automatic image fusion system to decrease doctor’s workload and improve the consistence of diagnosis. Image fusion has wide application domain in Medicinal diagnosis. Medical images have difference species such as CT, MRI, PET, ECT, and SPECT. These different images have their respective application ranges. For instance, functional information can be obtained by PET, SPECT. They contain relative low spatial resolution, but they can provide information about visceral metabolism and blood circulation. And that anatomical image contains high spatial resolution such as CT, MRI, B- mode ultrasonic, etc. Medical fusion image is to combine functional image and anatomical image together into one image. This image can provide abundance information to doctor to diagnose clinical disease. 1.2 Methods involved in Image Fusion: The simplest way of image fusion is to take the average of the two images pixel by pixel. However, this method usually leads to undesirable side effect such as reduced contrast. Other methods based on intensity-hue saturation (IHS), principal component analysis (PCA), synthetic variable ratio (SVR) etc. have also been developed. Due to the multiresolution transform can contribute a good mathematical model of human visual system and can provide information on the contrast changes, the multiresolution techniques have then attracted more and more interest in image fusion. The multiresolution techniques involve two types, viz. pyramidal transform and wavelet transform. The pyramid method was firstly introduced by Burt and Adelson and then was extended by Toet. However, for the reason of the pyramid method fails to introduce any spatial orientation selectivity in the decomposition process and usually contains blocking effects in the fusion results, the wavelet transform has then been used more widely than other methods. In this paper, a novel approach for the fusion of computed tomography (CT) and magnetic resonance images (MR) images based on wavelet transform has been presented. Different fusion rules are then performed on the wavelet coefficients of low and high frequency portions. The registered computer tomography (CT) and magnetic resonance imaging (MRI) images of the same people and same spatial parts have been used for the analysis. RESEARCH ARTICLE OPEN ACCESS