Abstract
Multi-modality medical image fusion plays an important role in medical analysis and diagnosis. Using the technologies of image fusion, we can combine the multimodality medical image information efficiently which is very helpful for clinical diagnoses and treatment. The new image is reconstructed by image fusion, which provides richer visual information than the original images. The result shows that retrieval results fit more closely with human perception and computer vision. A medical image fusion algorithm is proposed combined the advantages of multi-scale and multiple directions in Contourlet transformation. The multi-scale and multiple directions decomposition coefficients are obtained through Contourlet transformation. Then fusion rules are proposed by analyzing the characteristics of Contourlet transformation coefficients. Among them, for the low frequency and high frequency coefficients, we present different fusion rules based on the weighted ratios and condition weighted rule of the main image. The different fusion rules are adopted in view of the image detail characteristics and the edge detail characteristics. The experiments are done on the medical images including CT and MRI. Different fusion rules based on Contourlet transformation and different fusion method are analyzed. The fusion results are analyzed and compared with the measurement of human visual system and objective evaluation. Compare the new fusion method with other classical fusion algorithm to confirm the advantages of the new method. The simulation result on three groups of the multimodal medical images indicated that the algorithm is able to obtain fused images of higher clarity and complementary information compared with traditional methods. Multi-Modality fusion is one of the hottest discussed issues in the current research of medical image processing and it has a deep impact on the cognitive science and clinical treatment.