Daubechies wavelet-based local feature descriptor for multimodal medical image registration

A new local feature descriptor recursive Daubechies pattern (RDbW) is developed by defining and encoding the Daubechies wavelet decomposed center-neighbour pixel relationship in the local texture. RDbW features are applied in spatial alignment (registration) of multimodal medical images using a Procrustes analysis (PA)-based affine transformation function and the registered images are further fused by employing a wavelet-based fusion method. A significant amount of experiments is conducted and the registration and fusion accuracy of the proposed feature descriptor is compared with the prominent existing methods such as local binary patterns (LBP), local tetra pattern (LTrP), local diagonal extrema pattern (LDEP), and local diagonal Laplacian pattern (LDLP). Experimental results show the present registration method improves the average registration accuracy by 38, 47, 71, and 76% in contrast to LDLP, LDEP, LTrP, and LBP, respectively. Further, the fusion results of the current approach exhibit an average improvement in entropy by 11%, standard deviation by 6% edge strength by 12%, sharpness by 23%, and average gradient by 16% when compared with all other feature descriptors used for registering the images. Concepts presented here can be used widely in analysing the combined information present in multimodal medical images.