Comparison of Transform Techniques Based Image Fusion for Effective Disease Diagnosis

Abstract

Medical image fusion is pivotal to extract accurate information from medical images for disease diagnosis. This is used to amalgamate various images from same or different sources. In this work, Computed Tomography (CT) and Positron Emission Tomography (PET) images are fused by means of Discrete Wavelet Transform (DWT), curve let transform and multimodal image fusion. The current researches for disease diagnosis mainly focus on the identification of optimal method that consumes less time with high accuracy in order to ensure patient safety. Therefore, this work aims to find the suitable method by comparing various transform techniques for accurate analysis of medical images. The CT and PET images are used in this paper to obtain both anatomical and physiological information of human body and these images are utilized for image fusion by principal component analysis (PCA) and maximum method. The reliability of the techniques is calculated by performance analysis and is hence inferred that multimodal image fusion provides better accuracy than the rest of the methods. However, the future work will be to validate the results for a greater number of input images.