International Journal of Scientific & Engineering Research, Volume 5, Issue 2, February-2014 772 ISSN 2229-5518 IJSER © 2014 http://www.ijser.org Medical Image Fusion Based on Wavelet Transform Hari Om Shankar Mishra, Smriti Bhatnagar, Amit Shukla, Amit Tiwari Abstract- The objective of Image fusion is to combine information from multiple images of the same scene in to a single image retaining the important and required features from each of the original image. Nowadays, with the rapid development in high-technology and modern instrumentations, medical imaging has become a vital component of a large number of applications, including diagnosis, research, and treatment. Medical image fusion has been used to derive useful information from multimodality medical image data. For medical diagnosis, Computed Tomography (CT) provides the best information on denser tissue with less distortion. Magnetic Resonance Image (MRI) provides better information on soft tissue with more distortion [1]. In this case, only one kind of image may not be sufficient to provide accurate clinical requirements for the physicians. Therefore, the fusion of the multimodal medical images is necessary [3]. This paper aims to demonstrate the application of wavelet transformation to multimodality 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) and peak signal to noise ratio (PSNR). Index Terms - Medical Image Fusion, Computed Tomography(CT), Magnetic Resonance Image(MRI), Root mean square error (RMSE), Peak signal to noise ratio (PSNR), Multimodality images, Discrete wavelet Transform(DWT). ——————————  —————————— 1 INTRODUCTION The term fusion means in general an approach to extraction of information acquired in several domains. The objective of Image fusion is to combine information from multiple images of the same scene in to a single image retaining the important and required features from each of the original image. The main task of image fusion is integrating complementary information from multiple images in to single image [6]. The resultant fused image will be more informative and complete than any of the input image and is more suitable for human visual and machine perception. Image fusion is the process that combines information from multiple images of the same scene. The object of the image fusion is to retain the most desirable characteristics of each image. Thus the new image contains a more accurate description of the scene than any of the individual image. It also reduces the storage cost by storing just the single fused image instead of multiple images. For medical image fusion, the fusion of image provides additional clinical information which is otherwise not apparent in the separate images. However, the instruments are not capable of providing such information either by design or because of observational constraints, one possible solution for this is image fusion. Medical image fusion is the technology that could compound two mutual images in to one according to certain rules to achieve clear visual effect. By observing medical fusion image, doctor could easily confirm the position of illness. Medical imaging provides a variety of modes of image information for clinical diagnosis, such as CT, X-ray, DSA, MRI, PET, SPECT etc. Different medical images have different characteristics, which can provide structural information of different organs. For example, CT (Computed tomography) and MRI (Magnetic resonance image) with high spatial resolution can provide anatomical structure information of organs. And PET (Positive electron tomography) and SPECT (Emission computed tomography) with relatively poor spatial resolution, but provides information on organ metabolism [3] [6]. Thus, a variety of imaging for the same organ, they are contradictory, but complementary and interconnected. Therefore the appropriate image fusion of different features becomes urgent requirement for clinical diagnosis. Doctors can annually combine the CT and MRI medical images of a patient with a tumor 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. 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 [12]. 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. II. IMAGE FUSION BASED ON WAVELET TRANSFORM (A) WAVELET TRANSFORM IJSER