A multi-scale template method for shape detection with bio-medical applications

In this paper we present a novel methodology based on non-parametric deformable prototype templates for reconstructing the outline of a shape from a degraded image. Our method is versatile and fast and has the potential to provide an automatic procedure for classifying pathologies. We test our approach on synthetic and real data from a variety of medical and biological applications. In these studies it is important to reconstruct accurately the shape of the object under investigation from very noisy data. Here we assume that we have some prior knowledge about the object outline represented by a prototype shape. Our procedure deforms this shape by means of non-affine transformations and the contour is reconstructed by minimizing a newly developed objective function that depends on the transformation parameters. We introduce an iterative template deformation procedure in which the scale of the deformation decreases as the algorithm proceeds. We compare our results with those from a Gaussian Mixture Model segmentation and two state-of-the-art Level Set methods. This comparison shows that the proposed procedure performs consistently well on both real and simulated data. As a by-product we develop a new filter that recovers the connectivity of a shape.

Francesco de PasqualeEmail:

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Francesco de Pasquale   received his Ph.D. in Applied Statistics from the University of Plymouth, United Kingdom in 2004 discussing a thesis on Bayesian and Template based methods for image analysis. Since his degree in Physics obtained at the University of Rome ‘La Sapienza’in 1999 his work has been focused on developing models and methods for Magnetic Resonance Imaging, in particular image registration, classification and segmentation in a Bayesian framework. After being appointed a 2-year contract as a Lecturer at the University of Plymouth from 2003 to 2004 he is now a post-Doc researcher at the ITAB, Institute for Advanced Biomedical Technologies, University of Chieti, Italy and he works on the analysis of fMRI and MEG data. Julian Stander   was born in Plymouth, UK in 1964. He received a BA in Mathematics with first class honours from University of Oxford in 1987, a Diploma in Mathematical Statistics with distinction from University of Cambridge in 1988, and a PhD from University of Bath in 1992. He has been a lecturer at the School of Mathematics and Statistics, University of Plymouth, since 1993, and was promoted to Reader in 2006. His fields of interest are: applications of statistics including image analysis, spatial modelling and disclosure limitation. He has published over 20 refereed journal articles.