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Texture-Driven Parametric Snakes for Semi-Automatic Image Segmentation

A. Badoual, M. Unser, A. Depeursinge

Computer Vision and Image Understanding, vol. 188, paper no. 102793, pp. 1-11, November 2019.

We present a texture-driven parametric snake for semi-automatic segmentation of a single and closed 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.

AUTHOR="Badoual, A. and Unser, M. and Depeursinge, A.",
TITLE="Texture-Driven Parametric Snakes for Semi-Automatic Image
JOURNAL="Computer Vision and Image Understanding",
note="paper no.\ 102793")

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