Abstract
A new solution to overcome the constraints of multimodality medical intra-subject image registration is proposed, using the mutual information (MI) of image histogram-oriented gradients as a new matching criterion. We present a rigid, multi-modal image registration algorithm based on linear transformation and oriented gradients for the alignment of T2-weighted (T2w) images (as a fixed reference) and diffusion tensor imaging (DTI) (b-values of 500 and 1250 s/mm2) as floating images of three patients to compensate for the motion during the acquisition process. Diffusion MRI is very sensitive to motion, especially when the intensity and duration of the gradient pulses (characterized by the b-value) increases. The proposed method relies on the whole brain surface and addresses the variability of anatomical features into an image stack. The sparse features refer to corners detected using the Harris corner detector operator, while dense features use all image pixels through the image histogram of oriented gradients (HOG) as a measure of the degree of statistical dependence between a pair of registered images. HOG as a dense feature is focused on the structure and extracts the oriented gradient image in the x and y directions. MI is used as an objective function for the optimization process. The entropy functions and joint entropy function are determined using the HOGs data. To determine the best image transformation, the fiducial registration error (FRE) measure is used. We compare the results against the MI-based intensities results computed using a statistical intensity relationship between corresponding pixels in source and target images. Our approach, which is devoted to the whole brain, shows improved registration accuracy, robustness, and computational cost compared with the registration algorithms, which use anatomical features or regions of interest areas with specific neuroanatomy. Despite the supplementary HOG computation task, the computation time is comparable for MI-based intensities and MI-based HOG methods.
Highlights
Brain image registration is a fundamental component of the medical image analysis pipeline and consists of an optimization of the spatial correspondence between pairs of images by aligning corresponding structures to ease the comparison between different brain images
We present a rigid and multi-modal image registration algorithm based on linear transformation and oriented gradients for the alignment of T2w images and diffusion tensor imaging (DTI)
For the sparse feature determination, an experiment based on Harris corner detector operator was carried out
Summary
Brain image registration is a fundamental component of the medical image analysis pipeline and consists of an optimization of the spatial correspondence between pairs of images by aligning corresponding structures to ease the comparison between different brain images. The images of interest are acquired either at different times (multi-temporal analysis) or using various devices/sensors (multi-modal analysis) and may belong to the same patient or to different patients [1–10]. The same structure taken from different viewpoints (multi-view analysis) may be considered. Despite all these efforts, the diversity of images to be registered and the wide range of types of image degradations cause a universal registration method to be intractable. Being an automatic diagnosis tool, image registration adds value to imagery information media by allowing MR image modalities like T2w and diffusion tensor imaging (DTI) brain images to be analyzed in the same coordinate system, especially to correct the motion [6–8].
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