Fusion of PET and MRI images using adaptive neuro-fuzzy inference system

Abstract

Fusion of the PET and MRI images provides complete information, better visualization and higher diagnostic accuracy. This paper proposes the fusion of PET and MRI images using adaptive neuro-fuzzy inference system. The PET and MRI images areinitially decomposed using the second generation wavelets. The second generation wavelet transform is shift invariant. The second generation wavelet coefficients are then fused using the adaptive neuro-fuzzy inference system. Then the inverse transform is applied to the fused coefficients and the fused image is obtained. The performance of this algorithm is validated both qualitatively and quantitatively. The metrics used for the analysis are entropy, average gradient, average, standard deviation, mean square error and peak signal to noise ratio. It is also compared with the existing methods of image fusion. The proposed algorithm extracts more

information from the source images, provides better contrast and brightness as compared to the existing fusion techniques.