A Segmentation Model and Application to Endoscopic Images
In this paper a variational segmentation model is proposed. It is a generalization of the Chan and Vese model, for the scalar and vector-valued cases. It incorporates extra terms, depending on the image gradient, and aims at approximating the smoothed image gradient norm, inside and outside the segmentation curve, by mean constant values. As a result, a flexible model is obtained. It segments, more accurately, any object displaying many oscillations in its interior. In effect, an external contour of the object, as a whole, is achieved, together with internal contours, inside the object. For determining the approximate solution a Levenberg-Marquardt Newton-type optimization method is applied to the finite element discretization of the model. Experiments on in vivo medical endoscopic images (displaying aberrant colonic crypt foci) illustrate the efficacy of this model.
Keywordssegmentation variational methods level-sets vector-valued images
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