title: Smooth 3-D reconstruction for 2-D histological images.
abstract: We present an image driven approach to the reconstruction of 3-D volumes from stacks of 2-D post-mortem sections (histology, cryoimaging, autoradiography or immunohistochemistry) in the absence of any external information. We note that a desirable quality of the reconstructed volume is the smoothness of its notable structures (e.g. the gray/white matter surfaces in brain images). Here we propose to use smoothness as a means to drive the reconstruction process itself. From an initial rigid pair-wise reconstruction of the input 2-D sections, we extract the boundaries of structures of interest. Those are then evolved under a mean curvature flow modified to constrain the flow within 2-D planes. Sparse displacement fields are then computed, independently for each slice, from the resulting flow. A variety of transformations, from globally rigid to arbitrarily flexible ones, can then be estimated from those fields and applied to the individual input 2-D sections to form a smooth volume. We detail our method and discuss preliminary results on both real histological data and synthetic examples.
authors.affiliation: School of Computer Science, University of Nottingham, Nottingham, UK. rzc@cs.nott.ac.uk
authors.forename: Amalia
authors.initials: A
authors.lastname: Cifor
authors.forename: Tony
authors.initials: T
authors.lastname: Pridmore
authors.forename: Alain
authors.initials: A
authors.lastname: Pitiot
mesh_terms: D000465:Algorithms; D019412:Anatomy, Cross-Sectional / Q000379:methods*; D001706:Biopsy / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity