Performance Analysis Of Multi Source Fused Medical Images Using Multiresolution Transforms

Abstract

Image fusion combines information from multiple images of the same scene to get a composite image that is more suitable for human visual perception or further image-processing tasks. In this paper the multi source medical images like MRI (Magnetic Resonance Imaging), CT (computed tomography) & PET (positron emission tomography) are fused using different multi scale transforms. We compare various multi resolution transform algorithms, especially the latest developed methods, such as; Non Subsampled Contourlet Transform, Fast Discrete Curvelet, Contourlet, Discrete Wavelet transform and Hybrid Method (combination of DWT & Contourlet) for image fusion. The fusion operations are performed with all Multi resolution transforms. The fusion rules like local maxima and spatial frequency techniques are used for selection in the low frequency and high frequency subband coefficients, which can preserve more information and quality in the fused image. The fused output obtained after the inverse transform of fused sub band coefficients. The experimental results show that the effectiveness of fusion approaches in fusing multi source images.