Research On Methods Of Preprocessing And Fusion For Multimodal Medical Images

Numerous different modality medical images are available with the fastdevelopment of medical imaging technology. In order to overcome the limitations thatthe single-modal medical images only describe the local detailed information,multimodal medical image fusion technology is proposed. Through extracting andcombining information from different modal medical images, the proposed multimodalfusion technology can obtain more clear, comprehensive, accurate and reliable imagedescription of the focal areas, thus providing a reliable basis for doctors to diagnosedisease and to establish reasonable treatment methods. Multimodal medical imagefusion is an important branch of multi-source image fusion in medical field. As amulti-disciplinary and emerging research field, it not only has important scientific value,but also is closely related to people’s everyday life. After developing for nearly30years,multimodality medical image fusion has made many achievements, and formed somemature theories and methods. However, there remains many problems to be solved onthe several key steps of the medical image fusion. In order to solve these problems, theauthor focuses on several key steps of the fusion process, including "MRI image grayinhomogeneity correction","source image registration","multispectral andpanchromatic medical image fusion" and "salient information preservation of medicalimage fusion" etc., and carries out research work on the medical image preprocessingand fusion. In this paper, the main contents and contributions are summarized asfollows:As to medical image preprocessing, proposing an MRI image gray inhomogeneitycorrection algorithm based on simplified PCNN model, and medical image registrationalgorithm based on cascaded PCNN model. The former uses pulse synchronizationmechanism of PCNN to estimate the image offset field, insuring the effect of correctionand meanwhile improving the real-time performance of the algorithm. The latter usesthe cascaded PCNN model to extract the foveations in targeted image area andcombines FCM clustering and blocked coordinate system to complete medical imageregistration. In the study of multispectral and panchromatic medical image fusion, proposingthe image fusion algorithm based on IHS and PCA. In order to further improve thespectral characteristics of the fused image, the retina inspired model is introduced intothe original algorithm. The improved algorithm not only improves the spatial resolutionof the image, but also maintains the spectrum information of the source image so thatspectral distortions are avoided substantially.To highlight important information transfer from source image in the process ofimage fusion, proposing the multimodal medical images fusion algorithm based on thesaliency preservation. Through the saliency weighted on pixels in local areas of thesource image, the algorithm transfers the important information contained in the imagepixels from source image to the fusion image. In order to highlight the differentcharacteristics of pixels at different positions (texture, strong edges, weak edges, cornersand smooth areas etc.), on the basis of original algorithm, the characteristics weightedon pixels in the area is introduced. The improved algorithm performs better than theoriginal algorithm in terms of both the visual effect of fusion image and the informationdescription.In order to further improve the quality of fused image, proposing two fusionalgorithms based on the initial fused image. Based on weighted average fusion image,the first algorithm combines the guided filter and pixel screening strategy to obtain thefinal fusion image. The results of the algorithm reserve the defects of the weightedaverage fusion image, that is, the contrast is low and the texture details are relativelyobscured. The other algorithm firstly obtains the initial fused image using image blockreplacement, and then acquires the final fusion image by reinforcing the edges based onthe initial fused image. The original fusion image of the second algorithm is better thanthe first one from the viewpoint of both image contrast and details description, and sothe final fusion result of the second algorithm outperforms the first one.