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Texture-Driven Parametric Snakes




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Texture-Driven Parametric Snakes

Anaïs Badoual, Biomedical Imaging Group (BIG), EPFL, Switzerland

Adrien Depeursinge, Biomedical Imaging Group (BIG) at EPFL and Institute of Information Systems, University of Applied Sciences Western Switzerland, Switzerland

Segmentation of a hair follicle Segmentation of leaves Segmentation of a squirrel Segmentation of a vacuole


We present a texture-driven parametric snake for interactive segmentation of a single structure in an image. We propose a new energy functional that combines intensity and texture information. The two types of image information are balanced using Fisher's linear discriminant analysis. The framework can be used with any filter-based texture features. The parametric representation of the snake allows for easy and friendly user interaction while the framework can be trained on-the-fly from pixel collections provided by the user. We demonstrate the efficiency of the snake through an extensive validation on synthetic as well as on real data. Additionally, we show that the proposed snake is robust to noise and that it improves the segmentation performance when compared to an intensity-only scheme.


A. Badoual, M. Unser, and A. Depeursinge, "Texture-Driven Parametric Snakes for Interactive Image Segmentation," submitted.

Open Source Plugin: AC_TextureSnakes

The method is implemented as a plugin for the Icy bioimaging platform.



Test images and settings -- PROVIDED AFTER PUBLICATION

Condition of use

The software is freely available for research purposes. However, it should not be redistributed without the consent of the authors. We expect the user to include a citation of this publication whenever presenting or publishing results that are based on the Icy plugin AC_TextureSnakes.