Method and application on the localization of feature-point pairs in multi-modal medical images

The multi-model image registration is widely used in medical clinical diagnosis. However, the feature descriptor pairs are hard to detect between different modalities, which is a common dilemma faced in multi-modal image registration. In this paper, we detect feature descriptor pairs in the structure representation by convolutional neural network (CNN). On the one hand, structure representation can transform different modalities into a third-type modality, and it shows the potential information which do not appear at multi-modal medical image commonly. On the other hand, the matched feature points pairs which are computed by CNN can move back from structure representation to original image. Finally, the effective feature points are matched by 2NN algorithm. Extensive experiments on group-wise registration prove that this algorithm overcome the dilemma in extracting feature descriptor pairs among the multi-modal medical images.