3D dense local point descriptors for mouse brain gene expression images

Anatomical landmarks play an important role in many biomedical image analysis applications (e.g., registration and segmentation). Landmark detection can be computationally very expensive, especially in 3D images, because every single voxel in a region of interest may need to be evaluated. In this paper, we introduce two 3D local image descriptors which can be computed simultaneously for every voxel in a volume. Both our proposed descriptors are extensions of the DAISY descriptor, a popular descriptor that is based on the histograms of oriented gradients and was named after its daisy-flower-like configuration. Our experiments on mouse brain gene expression images indicate that our descriptors are discriminative and are able to reduce the detection errors of landmark points more than 30% when compared with SIFT-3D, an extension in 3D of SIFT (scale-invariant feature transform). We also demonstrate that our descriptors are more computationally efficient than SIFT-3D and n-SIFT (an extension SIFT in n-dimensions) for densely sampled points. Therefore, our descriptors can be used in applications that require computation of the descriptors at densely sampled points (e.g., landmark point detection or feature-based registration).