Machine Vision forum in Heidelberg
Virginie Uhlmann, EPFL STI LIB

Test Run • 17 August 2016 • BM 4 233

Abstract
Title: Spline-based models for image segmentation Abstract: Splines provide a unifying framework for solving a whole variety of image-processing problems that are best formulated in the continuous domain. In particular, splines can be used to dene a particular type of active contour algorithm called spline-snakes [1]. Active contours (or snakes) are very popular methods for image segmentation that consist in a curve evolving in the image from an initial position to the boundaries of the object of interest. Many dierent snake algorithms exist, which can usually be grouped into three main categories, namely point-based, level sets and parametric snakes. Spline-snakes are a subcategory of parametric snakes which benet from a continuous-domain representation, hence involving less parameters and being easy to handle analytically. In addition, spline-snakes are well-suited for semi-automated analysis pipelines and therefore hold a strong potential for user-friendly segmentation frameworks. Spline-snake algorithms rely on two main ingredients. The first one is the denation of the snake model, which includes the choice of a spline generator that serves as basis function. The snake curve is then continuously-dened using the spline basis to interpolate between a collection of discrete control points on the image. The second ingredient is the so-called snake energy, an appropriately dened cost function that, upon minimization, drives the deformation of the snake curve to t object boundaries. The snake energy is generally composed of external and internal forces, which attract the curve towards prominent image features (data delity) or constrain its rigidity (regularization), respectively. Out of these two aspects (snake curve model and energy), a whole zoo of spline-snakes with different properties can be defined. In this way, spline-snakes can yield both multi-purpose segmentation methods as well as approaches specically tuned to match the features of particular problems. In this talk, we will present in more details the general spline-snake construction and illustrate its use through a collection of applications to segmentation in 2- and 3-D biomedical images. References [1] R. Delgado-Gonzalo, V. Uhlmann, D. Schmitter, and M. Unser, \Snakes on a Plane: A Perfect Snap for Bioimage Analysis," IEEE Signal Processing Magazine, vol. 32, no. 1, pp. 41--48, January 2015.