Jean-Jacques Rousselle

Genetic Algorithm to Set Active Contour

Active contours, very popular in image segmentation, suffer from delicate adjustments of many parameters. We propose to carry out these adjustments using genetic algorithm. Here an active contour is implemented using a greedy algorithm. Within this framework, two approaches are presented. A supervised approach which delivers a global set of parameters. In this case the greedy algorithm is involved in the evaluation function of the genetic algorithm. The second approach is unsupervised. It determines a local set of parameters. The genetic algorithm computes a set of parameters which minimizes the energy at each point in the neighborhood of the current point in the greedy algorithm try to move.

Active Contours Driven by Supervised Binary Classifiers for Texture Segmentation

In this paper, we propose a new active contour model for supervised texture segmentation driven by a binary classifier instead of a standard motion equation. A recent level set implementation developed by Shi et al in [1] is employed in an original way to introduce the classifier in the active contour. Carried out on a learning image, an expert segmentation is used to build the learning dataset composed of samples defined by their Haralick texture features. Then, the pre-learned classifier is used to drive the active contour among several test images. Results of three active contours driven by binary classifiers are presented: a k-nearest-neighbors model, a support vector machine model and a neural network model. Results are presented on medical echographic images and remote sensing images and compared to the Chan-Vese region-based active contour in terms of accuracy, bringing out the high performances of the proposed models.

Design of experiments to set active contours

Design of experiments has already been used for several years in different domains. It is often ignored in image processing. In this article, we would like to show that it has it place in this area where it is common to have parameters to be adjusted according to the images to be processed and which should remain valid for a family of images of the same type. These parameters are often numerous and they frequently interfere with each others. The use of an active contour requires several parameters rather delicate to be adjusted. The experimental research methodology allows, the factors to be considered to be listed and then, from these, the identification of those which are the most influential, in order to optimize them.

A supervised texture-based active contour model with linear programming

In this paper we propose a new supervised active contour model evolving with Haralick texture features. This model is divided in two stages. First, we use a supervised step where the user defines an ideal segmentation on a learning image. A linear programming model, modeling the behavior of the active contour, is then used to determine the weights of the Haralick features leading to the optimal segmentation. In a second step, a texture-oriented active contour based on the Chan-Vese model is launched on several test images with the learned weights and the closest segmentations to the one defined on the learning image is determined. Results of our method are presented on medical echographic images.