V.R.S.Mani et al./ Elixir Adv. Engg. Info. 55A (2013) 13160-13163 13160 Introduction The goal of image fusion is to integrate complementary information from various modalities, so that the new image should be more suitable for the purpose of human visual perception and further analysis. Image fusion has become a common term used within medical diagnostics and treatment. The term fusion is used when multiple patient images are registered and overlaid or merged to provide additional information. Fused images may be created from multiple images of the same imaging modality, or by combining information from multiple modalities, such as Magnetic Resonanace Image (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and Single Photon Emission Computed Tomography (SPECT). In radiology, these images serve different medical purposes. For this reason, the radiologists prefer integrating multiple imaging modalities to obtain more details from input images. Commonly, a successful fusion should extract complete information from the source images, and should form the resulting image without introducing any artifacts or inconsistencies. Overview of Image Fusion The medical image fusion mainly uses pixel based fusion techniques. Usually, the pixel level fusion is broadly classified into three main categories. 1. Spatial Domain Techniques: (PCA, Averaging, etc.) 2. Optimization Approach: (Bayesian Approach) 3. Transform Domain Approach: (Multi-resolution Techniques) Initially, the fusion techniques are based on spatial domain techniques. The basic techniques which were used are Principal Component Analysis (PCA), Averaging, Weighted Averaging, etc. the main advantage of this spatial domain technique is, they are easy to implement. But there are few drawbacks in these methods. This technique produces spatial distortion in images. Also, some of the image details will not be present in the final fused image with respect to the input images. The second one is Bayesian approach, which suffers from the problem of computational complexity. The third one is based on transform domain approach .based on redundant multi resolution decomposition techniques capable of preserving structural characteristics. The transform decomposes the image into several components and the various components are fused based on their structural and functional importance and finally using an inverse transform the fused image is reconstructed. It provides more information for further analysis and diagnosis of various diseases.. The different multi scale transform domain techniques are wavelet, curvelet, contourlet, etc. Proposed methods There are some major drawbacks in the wavelet transform. First, it doesn’t provide shift invariance, and it does not capture the edges properly. Another major drawback in the wavelet transform is, it provides limited information along the horizontal, vertical and diagonal direction. Dual Tree Complex Wavelet Transform The above said drawbacks are removed using the proposed technique. In the proposed technique Multimodal images are decomposed using Dual Tree Complex Wavelet transform (DT- CWT). 2D – DT-CWT is the combination of two 1-D transforms. In wavelet transform, it has 1-D real filters. But in dual tree, there are two trees containing complementary filter values, one tree corresponds to real values and the other one is imaginary. Designing complex filters is not an easy task, but it makes the process more efficient. Also this Dual tree complex wavelet transform produces Approximate Shift invariance and it also provides limited directional information when compared to the wavelet transform. Also this technique captures more edge information when compared to wavelet transform. Contourlet Transform The Contourlet transform is used to decompose the image at different scales and orientations. Contourlet transform is an extension of wavelet transform and it uses directional filte Tele: E-mail addresses: vrsece@rediffmail.com © 2013 Elixir All rights reserved Multimodal image fusion using multiresolution techniques V.R.S.Mani 1 , S. Arivazhagan 2 and J. Jason Braino 3 1 Department of ECE, NEC, Kovilpatti. 2 Department of ECE, Mepco Schlenk Engg. College, Sivakasi. 3 Communication Systems, NEC, Kovilpatti. ABSTRACT Multimodal Image Fusion techniques combine information from different sensors together to produce a more accurate and efficient representation which is more useful for further analysis. A Multi resolution based Multimodal Image Fusion Algorithm is this paper, an automatic algorithm based on multi resolution technique for fusing multimodal images is proposed. The multimodal images are decomposed using the Dual Tree Complex Wavelet Transform and Contourlet Transform and they are fused using some efficient and robust fusion rules. Finally, they are reconstructed using the Inverse transform and a new fused image with more information content is obtained. The basic idea of all multiresolution fusion schemes is motivated by the human visual system being primarily sensitive to local contrast changes, e.g. the edges or corners. These techniques also provide better directional information. The DT-CWT and Contourlet Transform methods are good at faithfully retaining the salient structural information present in both the multimodal images. © 2013 Elixir All rights reserved. ARTICLE INFO Article history: Received: 7 June 2012; Received in revised form: 13 February 2013; Accepted: 22 February 2013; Keywords Image Fusion, Dual Tree Complex Wavelet Transform, Contourlet Transform. Elixir Adv. Engg. Info. 55A (2013) 13160-13163 Advanced Engineering Informatics Available online at www.elixirpublishers.com (Elixir International Journal)