Performance Analysis of Wavelet Functions in Fusion of MRI and CT Images

Abstract

The fusion of images is the mechanism by which two or more images are merged into one image with important features. Fusion is an important technology in many different areas, including remote sensing, robotics and medical applications. The image fusion results in a composite image that is ideally suited for human and machine perception or external image processing tasks. In medical imaging technology, the Magnetic Resonance Image (MRI) highlights the soft tissue of the body and Computed Tomography (CT) provides a better view on hard tissue highlighting bones so their fusion will lead to better information content. Highlight of this particular image fusion is, one of the most useful diagnoses of tumor it provides the identification of gross tumor volume and clinical target volume by 80% more comparing to the MRI and CT images can provide by itself. In this paper, we compared the efficiency of different fusion techniques. The wavelet based image fusion techniques comprises of two steps among which the first step is Discrete Wavelet Transform (DWT) based decomposition of two input images into four coefficients each such as approximation, vertical, horizontal and diagonal and fusion of each respective coefficients is performed based on some particular fusion rules. the fusion rules can be used for this particular application are Maximum, Minimum and Mean. Various parameters like Entropy, Mutual Information and Standard Deviations were used to evaluate the fused image.