Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Image Registration of Sectioned Brains
International Journal of Computer Vision
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We present an approach for the analysis of phenotypic diversity in morphology and internal composition of biological specimen by means of high resolution 3-D models of developing barley grains. Three-dimensional histological structures are resolved by reconstructing specimen from large stacks of serially sectioned material, which is a preliminary for the spatial assignment of key tissues in differentiation. By sampling and constructing models at different developmental time steps from multiple individuals, we address two aims in a computational phenomics context: i) Generation of averaging atlases as structural references for integration of functional data, and ii) building the basis for a mathematical model of grain morphogenesis. We have established an algorithmic pipeline for automated processing of large image stacks towards phenotypic 3-D models and data-integration, comprising registration, multi-label segmentation, and alignment of functional measurements. The described algorithms allow high-throughput reconstruction and tissue recognition of datasets comprising thousands of images. The usefulness of the approach is demonstrated by automated model generation, allowing volumetric measurements of tissue composition, three-dimensional analysis of diversity, and the integration of MALDI-IMS data by mutual information based registration, which is a significant contribution to a systematic analysis of differentiation and development.