Fast Scalar and Vectorial Grayscale Based Invariant Features for 3D Cell Nuclei Localization and Classification

Since biology and medicine apply increasingly fast volumetric imaging techniques and aim at extracting quantitative data from these images, the need for efficient image analysis techniques like detection and classification of 3D structures is obvious. A common approach is to extract local features, e.g. group integration has been used to gain invariance against rotation and translation. We extend these group integration features by including vectorial information and spherical harmonics descriptors. From our vectorial invariants we derive a very robust detector for spherical structures in low-quality images and show that it can be computed very fast. We apply these new invariants to 3D confocal laser-scanning microscope images of the Arabidopsis root tip and extract position and type of the cell nuclei. Then it is possible to build a biologically relevant, architectural model of the root tip.