A dynamic-shape-prior guided snake model with application in visually tracking dense cell populations

This paper proposes a dynamic-shape-prior guided snake (DSP G-snake) model that is designed to improve the overall stability of the point-based snake model. The dynamic shape prior is first proposed for snakes, that efficiently unifies different types of high-level priors into a new force term. To be specific, a global-topology regularity is first introduced that settles the inherent self-intersection problem with snakes. The problem that a snake’s snaxels tend to unevenly distribute along the contour is also handled, leading to good parameterization. Unlike existing methods that employ learning templates or commonly enforce hard priors, the dynamic-template scheme strongly respects the deformation flexibility of the model, while retaining a decent global topology for the snake. It is verified by experiments that the proposed algorithm can effectively prevent snakes from selfcrossing, or automatically untie an already self-intersected contour. In addition, the proposed model is combined with existing forces and applied to the very challenging task of tracking dense biological cell populations. The DSP G-snake model has enabled an improvement of up to 30% in tracking accuracy with respect to regular model-based approaches. Through experiments on real cellular datasets, with highly dense populations and relatively large displacements, it is confirmed that the proposed approach has enabled superior performance, in comparison to modern active-contour competitors as well as the state-of-the-art cell tracking frameworks

A Non-Stationary Subdivision Scheme for the Construction of Deformable Models with Sphere-Like Topology

We present an affine-invariant non-stationary subdivision scheme for the recursive refinement of any triangular mesh that is regular or has extraordinary vertices of valence 4. In particular, when applied to an arbitrary convex octahedron, it produces a $G ^{ 1 }$ -continuous surface with a blob-like shape as the limit of the recursive subdivision process. In case of a regular octahedron, the subdivision process provides an accurate representation of ellipsoids. Our scheme allows us to easily construct a new interactive 3D deformable model for use in the delineation of biomedical images, which we illustrate by examples that deal with the characterization of 3D structures with sphere-like topology such as embryos, nuclei, or brains

AUTOMATED ANALYSIS OF NEURONAL MORPHOLOGY: DETECTION, MODELING AND RECONSTRUCTION

Ph.DDOCTOR OF PHILOSOPH

Biological image analysis

In biological research images are extensively used to monitor growth, dynamics and changes in biological specimen, such as cells or plants. Many of these images are used solely for observation or are manually annotated by an expert. In this dissertation we discuss several methods to automate the annotating and analysis of bio-images. Two large clusters of methods have been investigated and developed. A first set of methods focuses on the automatic delineation of relevant objects in bio-images, such as individual cells in microscopic images. Since these methods should be useful for many different applications, e.g. to detect and delineate different objects (cells, plants, leafs, ...) in different types of images (different types of microscopes, regular colour photographs, ...), the methods should be easy to adjust. Therefore we developed a methodology relying on probability theory, where all required parameters can easily be estimated by a biologist, without requiring any knowledge on the techniques used in the actual software. A second cluster of investigated techniques focuses on the analysis of shapes. By defining new features that describe shapes, we are able to automatically classify shapes, retrieve similar shapes from a database and even analyse how an object deforms through time