Feature Based Fusion of Multimodal Medical Image Slices with Combined Transforms

Medical image fusion plays an important role in radiological practice, helping the doctors in exact diagnosis and medical treatment planning. The proposed feature based fusion of medical image slices in combined transform improves the quality of the fused image. The image is initially analysed using integer wavelet transform and then by discrete ripplet transform. Significant features of the low pass and the high pass regions are extracted. Decision rules are framed based on the features extracted, and the fused coefficients are obtained. Then, the fused coefficients are synthesized by applying the inverse transforms. In the proposed work, a generalised algorithm is applied to different pairs of medical images like i) CT and MRI image slices, ii) PET and MRI image slices and iii) SPECT and MRI image slices. The fused image is validated, and the quantitative results show that the proposed method is better than the existing methods and it has better visual quality with clear edges.