Image fusion enhancement of deformable human structures using a two-stage warping-deformable strategy: A content-based image retrieval consideration

In medical image registration and content-based image retrieval, the rigid transformation model is not adequate for anatomical structures that are elastic or deformable. For human structures such as abdomen, registration would involve global features such as abdominal wall as well as local target organs such as liver or spleen. A general non-rigid registration may not be sufficient to produce image matching of both global and local structures. In this study, a warping-deformable model is proposed to register images of such structures. This model uses a two-stage strategy for image registration of abdomen. In the first stage, the global-deformable transformation is used to register the global wall. The warping-transformation is used in second stage to register the liver. There is a good match of images using the proposed method (mean similarity index = 0.73545).The image matching correlation coefficients calculated from eight pairs of CT and MR images of abdomen indicates that the warping-deformable transformation gives better matching of images than those without transformation (p < 0.001, paired t-test). This study has established a model for image registration of deformable structures. This is particularly important for data mining of image content retrieval for structures which are non-rigid. The result obtained is very promising but further clinical evaluation is needed